feat: refactore code and add corrected apr and roi values

.gitignore

@@ -0,0 +1,135 @@
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+modius_env/
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
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+downloads/
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+.eggs/
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+lib64/
+parts/
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+var/
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+.installed.cfg
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app_trans_new.py

@@ -1,570 +0,0 @@
-import requests
-import pandas as pd
-import gradio as gr
-import plotly.express as px
-from datetime import datetime, timedelta
-import plotly.graph_objects as go
-import numpy as np
-import json
-from web3 import Web3
-import time
-import os
-from itertools import product
-# RPC URLs
-from dotenv import load_dotenv
-load_dotenv()
-
-OPTIMISM_RPC_URL = os.getenv('OPTIMISM_RPC_URL')
-BASE_RPC_URL = os.getenv('BASE_RPC_URL')
-ETH_RPC_URL = os.getenv('ETH_RPC_URL')
-MODE_RPC_URL = os.getenv('MODE_RPC_URL')
-
-print(f"Optimism RPC URL: {OPTIMISM_RPC_URL}")
-
-# Initialize Web3 instances
-print("Initializing Web3 instances...")
-web3_optimism = Web3(Web3.HTTPProvider(OPTIMISM_RPC_URL))
-web3_base = Web3(Web3.HTTPProvider(BASE_RPC_URL))
-web3_eth = Web3(Web3.HTTPProvider(ETH_RPC_URL))
-web3_mode = Web3(Web3.HTTPProvider(MODE_RPC_URL))  # New chain
-
-# Contract addresses for service registries
-contract_address_optimism = '0x3d77596beb0f130a4415df3D2D8232B3d3D31e44'
-contract_address_base = '0x3C1fF68f5aa342D296d4DEe4Bb1cACCA912D95fE'
-contract_address_eth = '0x48b6af7B12C71f09e2fC8aF4855De4Ff54e775cA'
-contract_address_mode = '0x3C1fF68f5aa342D296d4DEe4Bb1cACCA912D95fE'  # New chain
-
-# Load the ABI from a local JSON file
-with open('./contracts/service_registry_abi.json', 'r') as abi_file:
-    contract_abi = json.load(abi_file)
-
-# Create the contract instances
-service_registry_optimism = web3_optimism.eth.contract(address=contract_address_optimism, abi=contract_abi)
-service_registry_base = web3_base.eth.contract(address=contract_address_base, abi=contract_abi)
-service_registry_eth = web3_eth.eth.contract(address=contract_address_eth, abi=contract_abi)
-service_registry_mode = web3_mode.eth.contract(address=contract_address_mode, abi=contract_abi)  # New chain
-print("Service registry contracts loaded.")
-
-# Check if connection is successful
-if not web3_optimism.is_connected():
-    raise Exception("Failed to connect to the Optimism network.")
-if not web3_base.is_connected():
-    raise Exception("Failed to connect to the Base network.")
-if not web3_eth.is_connected():
-    raise Exception("Failed to connect to the ETH network.")
-if not web3_mode.is_connected():
-    raise Exception("Failed to connect to the Mode network.")  # New chain
-print("Successfully connected to Ethereum, Optimism, Base, and Mode networks.")
-
-
-
-def fetch_service_safes(web3, registry_contract):
-    print("\nFetching service safes...")
-    total_services = registry_contract.functions.totalSupply().call()
-    print(f"Total services: {total_services}")
-    service_safes_dict = {}
-    zero_address = '0x0000000000000000000000000000000000000000'
-    
-    for service_id in range(1, total_services + 1):
-        print(f"Processing service ID: {service_id}")
-        service = registry_contract.functions.getService(service_id).call()
-        agent_ids = service[-1]  # Assuming the last element is the list of agent IDs
-        print(f"Agent IDs: {agent_ids}")
-
-        if 40 in agent_ids or 25 in agent_ids:
-            agent_instance_data = registry_contract.functions.getAgentInstances(service_id).call()
-            service_safe = service[1]
-            agent_addresses = agent_instance_data[1]
-            if agent_addresses:
-                agent_address = agent_addresses[0]
-                if service_safe != zero_address:
-                    print(f"Found agent_address: {agent_address}")
-                    print(f"Found service safe: {service_safe}")
-                    service_safes_dict[agent_address] = service_safe
-                else:
-                    print(f"Found zero address for service safe, skipping for agent: {agent_address}")
-            else:
-                print(f"No agent address found for service ID: {service_id}")
-
-    print(f"Total unique agent addresses found (excluding zero safe addresses): {len(service_safes_dict)}")
-    return service_safes_dict
-
-# Fetch service safes for each network
-service_safes_optimism = fetch_service_safes(web3_optimism, service_registry_optimism)
-service_safes_base = fetch_service_safes(web3_base, service_registry_base)
-service_safes_eth = fetch_service_safes(web3_eth, service_registry_eth)
-service_safes_mode = fetch_service_safes(web3_mode, service_registry_mode)
-# service_safes_eth = {safe for safe in service_safes_eth if safe.lower() != '0x0000000000000000000000000000000000000000'}
-print(f"Service safes for Mode: {service_safes_mode}")
-def get_block_range_for_date(chain_id, date_str, api_key, base_url):
-    """Get the block range for a specific date."""
-    target_date = datetime.strptime(date_str, "%Y-%m-%d").date()
-    start_of_day = datetime.combine(target_date, datetime.min.time())
-    
-    if target_date == datetime.now().date():
-        end_of_day = datetime.now()  # Use the current time if the target date is today
-    else:
-        end_of_day = datetime.combine(target_date, datetime.max.time())
-
-    start_timestamp = int(start_of_day.timestamp())
-    end_timestamp = int(end_of_day.timestamp())
-
-    # Get start block
-    start_response = requests.get(
-        f"{base_url}?module=block&action=getblocknobytime&timestamp={start_timestamp}&closest=before&apikey={api_key}"
-    )
-    if start_response.status_code == 200:
-        start_data = start_response.json()
-        start_block = start_data.get('result')
-    else:
-        print(f"Error fetching start block for {date_str} on chain {chain_id}")
-        return None, None
-
-    if start_block is None:
-        print(f"No start block found for chain {chain_id} on {date_str}")
-        return None, None
-    print(f"Start block for chain {chain_id} on {date_str}: {start_block}")
-
-    # Get end block
-    time.sleep(1)
-    end_response = requests.get(
-        f"{base_url}?module=block&action=getblocknobytime&timestamp={end_timestamp}&closest=before&apikey={api_key}"
-    )
-    if end_response.status_code == 200:
-        end_data = end_response.json()
-        end_block = end_data.get('result')
-    else:
-        print(f"Error fetching end block for {date_str} on chain {chain_id}")
-        return None, None
-
-    if end_block is None:
-        print(f"No end block found for chain {chain_id} on {date_str}")
-        return None, None
-    print(f"End block for chain {chain_id} on {date_str}: {end_block}")
-
-    return start_block, end_block
-
-def get_transactions_mode_all(address):
-    url = f'https://explorer.mode.network/api/v2/addresses/{address}/transactions'
-    headers = {
-        'accept': 'application/json'
-    }
-    
-    response = requests.get(url, headers=headers)
-    return response.json()['items']
-
-def get_transactions(api_keys, wallet_address, chain_name, start_block, end_block):
-    """Retrieve transactions for the given wallet address, chain, and block range using the Etherscan or similar API."""
-    base_url = {
-        'optimism': "https://api-optimistic.etherscan.io/api",
-        'base': "https://api.basescan.org/api",
-        'ethereum': "https://api.etherscan.io/api",
-        'mode': "https://eth-sepolia.blockscout.com/api"
-    }.get(chain_name)
-
-    if not base_url:
-        print(f"Invalid chain name: {chain_name}")
-        return []
-
-    params = {
-        'module': 'account',
-        'action': 'txlist',
-        'address': wallet_address,
-        'startblock': start_block,
-        'endblock': end_block,
-        'sort': 'asc',
-        'apikey': api_keys.get(chain_name)
-    }
-
-    response = requests.get(base_url, params=params)
-    data = response.json()
-
-    time.sleep(1)
-    
-    if data['status'] != '1':
-        print(f"Error: {data['message']}")
-        return []
-
-    valid_transactions = [tx for tx in data['result'] if tx['isError'] == '0']
-    
-    return valid_transactions
-
-def get_mode_transactions(transactions_all_base,date_str):
-    target_date = datetime.strptime(date_str, "%Y-%m-%d").date()
-    start_of_day = datetime.combine(target_date, datetime.min.time())
-    
-    if target_date == datetime.now().date():
-        end_of_day = datetime.now()  # Use the current time if the target date is today
-    else:
-        end_of_day = datetime.combine(target_date, datetime.max.time())
-
-    start_timestamp = start_of_day.isoformat()
-    end_timestamp = end_of_day.isoformat()
-    processed_list = []
-    for transaction in transactions_all_base:
-        # Filter transactions based on the timestamp
-        if start_timestamp <= transaction['timestamp'] <= end_timestamp:
-            transaction_timestamp = datetime.strptime(transaction['timestamp'], "%Y-%m-%dT%H:%M:%S.%fZ").strftime("%Y-%m-%d %H:%M:%S")
-            processed_transaction = {
-                'hash': transaction['hash'],
-                'timeStamp': transaction_timestamp,
-                'from': transaction['from']['hash'],
-                'to': transaction['to']['hash'],
-                'value': transaction['value']
-            }
-            processed_list.append(processed_transaction)
-
-    return processed_list       
-
-
-def date_range(start_date, end_date):
-    """Generates a range of dates from start_date to end_date inclusive."""
-    start_dt = datetime.strptime(start_date, "%Y-%m-%d")
-    end_dt = datetime.strptime(end_date, "%Y-%m-%d")
-    delta = timedelta(days=1)
-    current_dt = start_dt
-    while current_dt <= end_dt:
-        yield current_dt.strftime("%Y-%m-%d")
-        current_dt += delta
-
-def fetch_transactions():
-    api_keys = {
-        'optimism': 'XQ72JA5XZ51QC7TG1W295AAIF4KTV92K1K',
-        'base': '4BFQMVW1QUKEPVDA4VW711CF4462682CY8',
-        'ethereum': '3GRYJGX55W3QWCEKGREF4H53AFHCAIVVR7',
-        'mode': 'f651444d-2916-4f37-bfd8-e70cc8232eb7'
-    }
-
-    base_urls = {
-        10: "https://api-optimistic.etherscan.io/api",
-        8453: "https://api.basescan.org/api",
-        1: "https://api.etherscan.io/api",
-        3443: "https://eth-sepolia.blockscout.com/api"
-    }
-
-    current_date = datetime.today().strftime("%Y-%m-%d")
-    csv_filename = 'daily_transactions_new.csv'
-
-    if os.path.exists(csv_filename):
-        df_existing = pd.read_csv(csv_filename)
-        if 'date' in df_existing:
-            last_date_in_csv = df_existing['date'].max()
-        else:
-            df_existing['date'] = pd.to_datetime(df_existing['timestamp']).dt.date
-            last_date_in_csv = df_existing['date'].max()
-    else:
-        df_existing = pd.DataFrame()
-        last_date_in_csv = '2024-09-19'
-
-    start_date = (datetime.strptime(last_date_in_csv, "%Y-%m-%d") + timedelta(days=1)).strftime("%Y-%m-%d")
-
-    chains = {
-        10: ('optimism', service_safes_optimism),
-        8453: ('base', service_safes_base),
-        1: ('ethereum', service_safes_eth),
-        3443: ('mode', service_safes_mode)
-    }
-
-    all_transactions = df_existing.to_dict('records') if not df_existing.empty else []
-
-    for chain_id, (chain_name, service_safes) in chains.items():
-        base_url = base_urls[chain_id]
-        api_key = api_keys[chain_name]
-
-        for agent_address, safe_address in service_safes.items():
-            print(f"\nProcessing {chain_name.capitalize()} for agent address {agent_address} (safe address {safe_address})...")
-            if chain_name == 'mode':
-                transactions_all_base = get_transactions_mode_all(safe_address)
-            
-            for single_date in date_range(start_date, current_date):
-                if chain_name == 'mode':
-                    print(f"Processing date {single_date} for chain {chain_name}...")
-                    transactions = get_mode_transactions(transactions_all_base,single_date)
-                    
-                else:
-                    print(f"Processing date {single_date} for chain {chain_name}...")
-                    start_block, end_block = get_block_range_for_date(chain_id, single_date, api_key, base_url)
-                    if start_block is None or end_block is None:
-                        print(f"Skipping date {single_date} for chain {chain_name} due to missing block data.")
-                        continue
-                    print(f"Start Block: {start_block}, End Block: {end_block} for date {single_date}")
-                    transactions = get_transactions(api_keys, safe_address, chain_name, start_block, end_block)
-                    
-                if transactions:
-                    print(f"Found {len(transactions)} transactions on {single_date} for {chain_name.capitalize()} safe address {safe_address}:")
-                    for tx in transactions:
-                        if chain_name != 'mode':
-                            print(f"Transaction Hash: {tx['hash']} on {chain_name.capitalize()} at {tx['timeStamp']}")
-                            tx_time = datetime.fromtimestamp(int(tx['timeStamp']))
-                            all_transactions.append({
-                                'chain': chain_name,
-                                'agent_address': agent_address,
-                                'safe_address': safe_address,
-                                'date': single_date,
-                                'transaction_hash': tx['hash'],
-                                'timestamp': tx_time,
-                                'from': tx['from'],
-                                'to': tx['to'],
-                                'value_eth': int(tx['value']) / 1e18  # Convert value to ETH
-                            })
-                        else:
-                            all_transactions.append({
-                                'chain': chain_name,
-                                'agent_address': agent_address,
-                                'safe_address': safe_address,
-                                'date': single_date,
-                                'transaction_hash': tx['hash'],
-                                'timestamp': tx['timeStamp'],
-                                'from': tx['from'],
-                                'to': tx['to'],
-                                'value_eth': int(tx['value']) / 1e18  # Convert value to ETH
-                            })
-
-                else:
-                    print(f"No transactions found for agent address {agent_address} (safe address {safe_address}) on {single_date} on {chain_name.capitalize()}.")
-
-    df_transactions_new = pd.DataFrame(all_transactions)
-    df_transactions_new.to_csv(csv_filename, index=False)
-    return df_transactions_new
-
-
-def create_transcation_visualizations():
-    df_transactions_new = fetch_transactions()
-    df_transactions_new['timestamp'] = pd.to_datetime(df_transactions_new['timestamp'])
-
-    # Group by date and chain, count transactions
-    daily_counts = df_transactions_new.groupby([df_transactions_new['timestamp'].dt.date, 'chain']).size().unstack(fill_value=0)
-
-        # Ensure required chains are present
-    chains = ['optimism', 'base', 'ethereum', 'mode'] 
-    for chain in chains:
-        if chain not in daily_counts.columns:
-            daily_counts[chain] = 0
-
-    daily_counts = daily_counts[chains]
-
-    # Read the original data
-    daily_counts['timestamp'] = daily_counts.index
-    daily_counts['timestamp'] = pd.to_datetime(daily_counts['timestamp'])
-    daily_counts = daily_counts.reset_index(drop=True)
-    # Get min and max dates
-    min_date = daily_counts['timestamp'].min()
-    max_date = daily_counts['timestamp'].max()
-
-    # Create complete date range
-    full_date_range = pd.date_range(start=min_date, end=max_date, freq='D')
-
-    # Create a new dataframe with all dates
-    complete_df = pd.DataFrame({'timestamp': full_date_range})
-    complete_df = complete_df.merge(daily_counts, on='timestamp', how='left')
-    complete_df = complete_df.fillna(0)
-    daily_counts = complete_df
-    # Convert timestamp to datetime
-    daily_counts['timestamp'] = pd.to_datetime(daily_counts['timestamp'])
-
-    # Create a new dataframe with 12-hour slots
-    new_rows = []
-
-    for _, row in daily_counts.iterrows():
-        # Create first 12-hour slot (0-12)
-        slot1 = row.copy()
-        # slot1['timestamp'] = row['timestamp'].replace(hour=0)
-        
-        # # Create second 12-hour slot (12-24)
-        new_rows.extend([slot1])
-        slot2 = row.copy()
-        if slot2['timestamp'].dayofweek == 6:  # 6 represents Sunday
-            slot2[chains] = 0
-            slot2['timestamp'] = row['timestamp'].replace(hour=12)
-            new_rows.extend([slot2])
-        
-        
-
-    # Create new dataframe with 12-hour slots
-    hourly_counts = pd.DataFrame(new_rows).sort_values('timestamp')
-
-    # Prepare data for plotting
-    dates = hourly_counts['timestamp'].tolist()
-    values = hourly_counts[chains].to_numpy()
-
-    # Create the figure
-    fig = go.Figure()
-    # Create arrays for positioning and width
-    # Convert dates to datetime objects first
-    date_objects = pd.to_datetime(dates)
-
-    # Create numeric indices for x-axis
-    x_numeric = np.arange(len(dates))
-
-    # Create arrays for positioning and width
-    width_array = []
-
-    for i, date in enumerate(date_objects):
-        width_array.append(1.0)  # Full width for other days
-
-    # Get Monday indices for tick positions
-    monday_indices = [i for i, date in enumerate(date_objects) if date.dayofweek == 0]
-    monday_labels = [date_objects[i].strftime('%m-%d') for i in monday_indices]
-
-    # Add traces for each series
-    fig.add_trace(go.Bar(
-        name='Optimism',
-        x=x_numeric,
-        y=values[:,0],
-        marker_color='blue',
-        opacity=0.7,
-        text=None,
-        width=width_array,
-        textposition='none',
-    ))
-
-    fig.add_trace(go.Bar(
-        name='Base',
-        x=x_numeric,
-        y=values[:,1],
-        marker_color='purple',
-        opacity=0.7,
-        text=None,
-        textposition='none',
-        width=width_array,
-    ))
-
-    fig.add_trace(go.Bar(
-        name='Ethereum',
-        x=x_numeric,
-        y=values[:,2],
-        marker_color='darkgreen',
-        opacity=0.7,
-        text=None,
-        width=width_array,
-        textposition='none',
-    ))
-
-    fig.add_trace(go.Bar(
-        name='Mode',
-        x=x_numeric,
-        y=values[:,3],
-        marker_color='orange',
-        opacity=0.7,
-        text=None,
-        width=width_array,
-        textposition='none',
-    ))
-
-    # Update layout with numeric x-axis
-    fig.update_layout(
-        title='Chain Daily Activity : Transactions',
-        xaxis_title='Date',
-        yaxis_title='Daily Transactions Count',
-        barmode='stack',
-        showlegend=True,
-        legend_title_text='Transaction Chain',
-        height=600,
-        bargap=0,
-        bargroupgap=0,
-        xaxis=dict(
-            tickangle=-45,
-            tickmode='array',
-            ticktext=monday_labels,
-            tickvals=monday_indices,
-        ),
-        template='plotly_white',
-        hoverlabel=dict(
-            font_size=12,
-        ),
-    )
-
-    # Update hover template
-    for trace in fig.data:
-        trace.update(
-            hovertemplate="<b>Date:</b> %{text}<br>" +
-                        "<b>" + trace.name + ":</b> %{y}<br>" +
-                        "<extra></extra>",
-            text=[d.strftime('%Y-%m-%d') for d in date_objects]  # Add date text for hover
-        )
-
-    # Show the plot
-    return fig
-
-def create_active_agents_visualizations():
-    df_transactions_new = fetch_transactions()
-    df_transactions_new['timestamp'] = pd.to_datetime(df_transactions_new['timestamp'])
-    # Extract week and day information
-    df_transactions_new['week_start'] = df_transactions_new['timestamp'].dt.to_period('W').apply(lambda r: r.start_time)
-    df_transactions_new['weekday'] = df_transactions_new['timestamp'].dt.weekday
-    
-    # Count unique agents per day
-    daily_agents = df_transactions_new.groupby(['week_start', 'weekday'])['agent_address'].nunique().reset_index()
-    
-    # Generate all possible combinations of week_start and weekday up to today
-    today = datetime.today()
-    yesterday = today - timedelta(days=1)
-    valid_weekdays = [(ws, wd) for ws, wd in product(daily_agents['week_start'].unique(), range(7)) if ws + timedelta(days=wd) <= yesterday]
-    
-    all_combinations = pd.DataFrame(valid_weekdays, columns=['week_start', 'weekday'])
-    
-    # Merge with daily_agents to fill missing days with zero
-    daily_agents = all_combinations.merge(daily_agents, on=['week_start', 'weekday'], how='left').fillna(0)
-    
-    # Compute average unique agents per week
-    weekly_avg_agents = daily_agents.groupby('week_start')['agent_address'].mean().reset_index()
-    weekly_avg_agents.rename(columns={'agent_address': 'avg_daily_active_agents'}, inplace=True)
-    
-    # Prepare data for plotting
-    weeks = weekly_avg_agents['week_start'].unique()
-    avg_agents_per_week = weekly_avg_agents['avg_daily_active_agents']
-    
-    # Create the bar chart
-    fig = go.Figure()
-    
-    fig.add_trace(go.Bar(
-        x=[f'{week.strftime("%b %d")}' for week in weeks],
-        y=avg_agents_per_week,
-
-        marker_color='blue',
-        opacity=0.7,
-        text=None,
-        hoverlabel=dict(
-            font_size=12,
-        ),
-    ))
-    
-    # Update layout
-    fig.update_layout(
-        title='Daily Active Agents: Weekly Average Number of Agents with at Least 1 Transaction Daily',
-        xaxis_title='Week',
-        yaxis_title='Average Number of Active Agents',
-        xaxis=dict(
-            tickangle=-45  # Rotate x-axis labels to 45 degrees
-        ),
-        height=600,
-        width=1000,
-        bargap=0,
-        bargroupgap=0.2,
-        template='plotly_white'
-    )
-    
-    return fig
-
-
-# Gradio interface
-def dashboard():
-    with gr.Blocks() as demo:
-        gr.Markdown("# Valory Transactions Dashboard")
-
-        # Fetch and display visualizations
-        with gr.Tab("Transactions"):
-            fig_tx_chain = create_transcation_visualizations()
-            gr.Plot(fig_tx_chain)
-        with gr.Tab("DAA"):
-            fig_active_agents = create_active_agents_visualizations()
-            gr.Plot(fig_active_agents)    
-
-        # Add more tabs as needed...
-        
-    return demo
-
-
-# Launch the dashboard
-if __name__ == "__main__":
-    dashboard().launch()

app_value_locked.py

@@ -1,405 +0,0 @@
-import json
-import requests
-from web3 import Web3
-from datetime import datetime, timedelta
-from typing import List, Dict, Optional
-import pandas as pd
-import time 
-import requests
-from requests.adapters import HTTPAdapter
-from requests.packages.urllib3.util.retry import Retry
-import time
-import os 
-
-ADDRESSES = {
-    "optimism": {
-        "balancer_vault": "0xBA12222222228d8Ba445958a75a0704d566BF2C8",
-        "uniswap_position_manager": "0xC36442b4a4522E871399CD717aBDD847Ab11FE88"
-    },
-    "base": {
-        "balancer_vault": "0xBA12222222228d8Ba445958a75a0704d566BF2C8",
-        "uniswap_position_manager": "0x03a520b32C04BF3bEEf7BEb72E919cf822Ed34f1"
-    }
-}
-
-# Defining RPC URLs and initializing Web3 instances
-OPTIMISM_RPC_URL = os.getenv('OPTIMISM_RPC_URL')
-BASE_RPC_URL = os.getenv('BASE_RPC_URL')
-ETH_RPC_URL = os.getenv('ETH_RPC_URL')
-
-print("Initializing Web3 instances...")
-web3_optimism = Web3(Web3.HTTPProvider(OPTIMISM_RPC_URL))
-web3_base = Web3(Web3.HTTPProvider(BASE_RPC_URL))
-
-# Contract addresses for service registries
-contract_address_optimism = '0x3d77596beb0f130a4415df3D2D8232B3d3D31e44'
-contract_address_base = '0x3C1fF68f5aa342D296d4DEe4Bb1cACCA912D95fE'
-
-# Load the ABI from a local JSON file
-with open('./contracts/service_registry_abi.json', 'r') as abi_file:
-    contract_abi = json.load(abi_file)
-
-# Create the contract instances
-service_registry_optimism = web3_optimism.eth.contract(address=contract_address_optimism, abi=contract_abi)
-service_registry_base = web3_base.eth.contract(address=contract_address_base, abi=contract_abi)
-print("Service registry contracts loaded.")
-
-def load_abi(filename):
-    with open(filename, 'r') as file:
-        contract_json = json.load(file)
-        return contract_json['abi']
-
-UNISWAP_ABI = load_abi('./contracts/NonfungiblePositionManager.json')
-
-def get_logs(api_key, chain_name, from_block, to_block, contract_address, service_safe):
-    """Fetch logs for the given contract and wallet address with specified topics."""
-    base_url = {
-        'optimism': "https://api-optimistic.etherscan.io/api",
-        'base': "https://api.basescan.org/api"
-    }.get(chain_name)
-
-    if not base_url:
-        print(f"Invalid chain name: {chain_name}")
-        return []
-    print('formatted safe address for topic', f"0x000000000000000000000000{service_safe[2:].lower()}")
-    params = {
-        'module': 'logs',
-        'action': 'getLogs',
-        'address': contract_address,
-        'fromBlock': from_block,
-        'toBlock': to_block,
-        'apikey': api_key,
-        'topic2': f"0x000000000000000000000000{service_safe[2:].lower()}"  # Properly formatted topic2
-    }
-
-    response = requests.get(base_url, params=params)
-    data = response.json()
-
-    if data['status'] != '1':
-        print(f"Error: {data['message']}")
-        return []
-
-    return data['result']
-
-def get_block_range_for_date(chain_id, date_str, api_key, base_url):
-    """Get the block range for a specific date."""
-    target_date = datetime.strptime(date_str, "%Y-%m-%d")
-    start_of_day = datetime.combine(target_date, datetime.min.time())
-    end_of_day = datetime.combine(target_date, datetime.max.time())
-
-    start_timestamp = int(start_of_day.timestamp())
-    end_timestamp = int(end_of_day.timestamp())
-
-    # Get start block
-    start_response = requests.get(
-        f"{base_url}?module=block&action=getblocknobytime&timestamp={start_timestamp}&closest=before&apikey={api_key}"
-    )
-    if start_response.status_code == 200:
-        start_data = start_response.json()
-        start_block = start_data.get('result')
-    else:
-        print(f"Error fetching start block for {date_str} on chain {chain_id}")
-        return None, None
-
-    if start_block is None:
-        print(f"No start block found for chain {chain_id} on {date_str}")
-        return None, None
-    print(f"Start block for chain {chain_id} on {date_str}: {start_block}")
-
-    # Get end block
-    end_response = requests.get(
-        f"{base_url}?module=block&action=getblocknobytime&timestamp={end_timestamp}&closest=before&apikey={api_key}"
-    )
-    if end_response.status_code == 200:
-        end_data = end_response.json()
-        end_block = end_data.get('result')
-    else:
-        print(f"Error fetching end block for {date_str} on chain {chain_id}")
-        return None, None
-
-    if end_block is None:
-        print(f"No end block found for chain {chain_id} on {date_str}")
-        return None, None
-    print(f"End block for chain {chain_id} on {date_str}: {end_block}")
-
-    return start_block, end_block
-
-def date_range(start_date, end_date):
-    """Generates a range of dates from start_date to end_date inclusive."""
-    start_dt = datetime.strptime(start_date, "%Y-%m-%d")
-    end_dt = datetime.strptime(end_date, "%Y-%m-%d")
-    delta = timedelta(days=1)
-    current_dt = start_dt
-    while current_dt <= end_dt:
-        yield current_dt.strftime("%Y-%m-%d")
-        current_dt += delta
-
-def parse_transfer_log(chain_name, single_date, log):
-    # ERC-721 Transfer event signature
-    TRANSFER_EVENT_SIGNATURE = "0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef"
-
-    if log['topics'][0].lower() == TRANSFER_EVENT_SIGNATURE.lower():
-        # This is a Transfer event
-        return {
-            "chain_name": chain_name,
-            "date": single_date,
-            "event": "Transfer",
-            "topic_0": log['topics'][0],
-            "from": "0x" + log['topics'][1][-40:],  # Extract the address from the padded topic
-            "to": "0x" + log['topics'][2][-40:],  # Extract the address from the padded topic
-            "token_id": int(log['topics'][3], 16)  # Convert hex to decimal
-        }
-
-    # If no Transfer event is found
-    return None
-
-def parse_pool_balance_log(chain_name, single_date, log):
-    POOL_BALANCE_SIGNATURE = "0xe5ce249087ce04f05a957192435400fd97868dba0e6a4b4c049abf8af80dae78"
-    if log['topics'][0].lower() == POOL_BALANCE_SIGNATURE.lower():
-        # Event PoolBalanceChanged: (bytes32 poolId, address liquidityProvider, address[] tokens, int256[] deltas, uint256[] protocolFeeAmounts)
-        return {
-            "chain_name": chain_name,
-            "date": single_date,
-            "event": "PoolBalanceChanged",
-            "poolId": log['topics'][1],
-            "liquidityProvider": "0x" + log['topics'][2][-40:],  # Extract the address from the padded topic
-            "tokens": [log['data'][i:i + 64] for i in range(0, len(log['data']), 64)],
-            "deltas": [int(log['data'][i:i + 64], 16) for i in range(0, len(log['data']), 64)],
-            "protocolFeeAmounts": [int(log['data'][i:i + 64], 16) for i in range(0, len(log['data']), 64)]
-        }
-
-def fetch_service_safes(web3, registry_contract):
-    print("\nFetching service safes...")
-    total_services = registry_contract.functions.totalSupply().call()
-    print(f"Total services: {total_services}")
-    service_safes = set()
-
-    for service_id in range(1, total_services + 1):
-        service = registry_contract.functions.getService(service_id).call()
-        agent_ids = service[-1]  # Assuming the last element is the list of agent IDs
-
-        if 40 in agent_ids:
-            service_safe = service[1]
-            service_safes.add(service_safe)
-
-    print(f"Total service safes found: {len(service_safes)}")
-    return service_safes
-
-def get_uniswap_v3_position(web3, contract_address, token_id):
-    """Fetch the Uniswap V3 position details including `token0`, `token1`."""
-    position_manager_contract = web3.eth.contract(address=contract_address, abi=UNISWAP_ABI)
-    position_data = position_manager_contract.functions.positions(token_id).call()
-    return {
-        'token0': position_data[2],
-        'token1': position_data[3]
-    }
-
-def get_uniswap_increase_liquidity(api_key, chain_name, token_id):
-    """Fetch the IncreaseLiquidity event details including `amount0`, `amount1`."""
-    base_url = {
-        'optimism': "https://api-optimistic.etherscan.io/api",
-        'base': "https://api.basescan.org/api"
-    }.get(chain_name)
-
-    if not base_url:
-        print(f"Invalid chain name: {chain_name}")
-        return {}
-
-    params = {
-        'module': 'logs',
-        'action': 'getLogs',
-        'address': ADDRESSES[chain_name]['uniswap_position_manager'],
-        'topic0': "0x3067048beee31b25b2f1681f88dac838c8bba36af25bfb2b7cf7473a5847e35f",
-        'topic1': f"0x{token_id:064x}",
-        'apikey': api_key
-    }
-
-    response = requests.get(base_url, params=params)
-    data = response.json()
-
-    if data['status'] != '1':
-        print(f"Error: {data['message']}")
-        return {}
-
-    log = data['result'][0] if data['result'] else None
-    if not log:
-        return {}
-
-    # Extracting amounts from the data hex-string
-    data_hex = log['data'][2:]  # Remove '0x' prefix
-    liquidity = int(data_hex[0:64], 16)
-    amount0 = int(data_hex[64:128], 16)
-    amount1 = int(data_hex[128:192], 16)
-
-    return {
-        'liquidity': liquidity,
-        'amount0': amount0,
-        'amount1': amount1
-    }
-
-def get_token_decimals(web3, token_address):
-    """Fetch the number of decimals for a given ERC-20 token."""
-    token_contract = web3.eth.contract(address=token_address, abi=[
-        {"constant":True,"inputs":[],"name":"decimals","outputs":[{"name":"","type":"uint8"}],"payable":False,"stateMutability":"view","type":"function"}
-    ])
-    return token_contract.functions.decimals().call()
-
-
-
-def requests_retry_session(
-    retries=3,
-    backoff_factor=0.3,
-    status_forcelist=(500, 502, 504),
-    session=None,
-):
-    session = session or requests.Session()
-    retry = Retry(
-        total=retries,
-        read=retries,
-        connect=retries,
-        backoff_factor=backoff_factor,
-        status_forcelist=status_forcelist,
-    )
-    adapter = HTTPAdapter(max_retries=retry)
-    session.mount('http://', adapter)
-    session.mount('https://', adapter)
-    return session
-
-def get_token_price_usd(chain, token_address):
-    chain_dict = {"optimism": "optimistic-ethereum", "base": "base", "ethereum": "ethereum"}
-    chain_name = chain_dict.get(chain, chain) 
-    time.sleep(4)
-    url = f"https://api.coingecko.com/api/v3/simple/token_price/{chain_name}?contract_addresses={token_address}&vs_currencies=usd"
-
-    headers = {
-        "accept": "application/json",
-        "x-cg-api-key": "CG-mf5xZnGELpSXeSqmHDLY2nNU"
-    }
-
-    try:
-        response = requests_retry_session().get(url, headers=headers, timeout=10)
-        response.raise_for_status()
-        data = response.json()
-
-        # Extract USD price
-        key = token_address.lower()
-        return data.get(key, {}).get('usd', None)
-    except requests.exceptions.RequestException as e:
-        print(f"An error occurred: {e}")
-        return None
-
-def load_existing_transactions(file_path):
-    """Load existing transactions from a CSV file."""
-    try:
-        return pd.read_csv(file_path)
-    except FileNotFoundError:
-        return pd.DataFrame()
-
-def get_last_processed_date(df):
-    """Get the last processed date from the DataFrame."""
-    if df.empty:
-        return None
-    return df['date'].max()
-
-# Main function to integrate the above based on provided context
-def fetch_daily_value_locked():
-    api_keys = {
-        'optimism': 'XQ72JA5XZ51QC7TG1W295AAIF4KTV92K1K',
-        'base': '4BFQMVW1QUKEPVDA4VW711CF4462682CY8'
-    }
-
-    base_urls = {
-        10: "https://api-optimistic.etherscan.io/api",
-        8453: "https://api.basescan.org/api"
-    }
-
-    # Load existing transactions if any
-    existing_transactions_path = 'daily_value_locked.csv'
-    df_existing_transactions = load_existing_transactions(existing_transactions_path)
-    last_processed_date = get_last_processed_date(df_existing_transactions)
-
-    # Determine the start date based on the last processed date
-    start_date = (pd.to_datetime(last_processed_date) + timedelta(days=1)).strftime('%Y-%m-%d') if last_processed_date else '2024-09-19'
-    current_date = datetime.now().strftime('%Y-%m-%d')  # Till present date
-
-    chains = {
-        10: ('optimism', 'uniswap_position_manager', 'balancer_vault'),
-        8453: ('base', 'uniswap_position_manager', 'balancer_vault')
-    }
-
-    # Example service safe addresses - Replace these with actual addresses
-    print("Fetching service safes for Optimism...")
-    service_safes_optimism = fetch_service_safes(web3_optimism, service_registry_optimism)
-    print(service_safes_optimism)
-    print("Fetching service safes for Base...")
-    service_safes_base = fetch_service_safes(web3_base, service_registry_base)
-    print(service_safes_base)
-
-    service_safes = {
-        'optimism': service_safes_optimism,
-        'base': service_safes_base
-    }
-
-    all_transactions = []  # List to hold all parsed logs
-
-    for chain_id, (chain_name, uniswap_contract_key, balancer_contract_key) in chains.items():
-        base_url = base_urls[chain_id]
-        api_key = api_keys[chain_name]
-
-        for service_safe in service_safes[chain_name]:
-            print(f"Checking service safe {service_safe} for chain {chain_name}")
-            for single_date in date_range(start_date, current_date):
-                start_block, end_block = get_block_range_for_date(chain_id, single_date, api_key, base_url)
-                if start_block is None or end_block is None:
-                    print(f"Skipping date {single_date} for chain {chain_name} due to missing block data.")
-                    continue
-
-                print(f"Start Block: {start_block}, End Block: {end_block} for date {single_date} on chain {chain_name} for service safe {service_safe}")
-
-                # Get logs for Uniswap and Balancer contracts
-                for contract_key, topic_key in [(uniswap_contract_key, "transfer"), (balancer_contract_key, "pool_balance_changed")]:
-                    contract_address = ADDRESSES[chain_name][contract_key]
-                    print(api_key, chain_name, start_block, end_block, contract_address, service_safe)
-                    logs = get_logs(api_key, chain_name, start_block, end_block, contract_address, service_safe)
-
-                    for log in logs:
-                        parsed_log = parse_pool_balance_log(chain_name, single_date, log) if topic_key == "pool_balance_changed" else parse_transfer_log(chain_name, single_date, log)
-                        if parsed_log:
-                            if topic_key == "transfer":
-                                # If the event is a Transfer event, fetch uniswap position details and increase liquidity event details
-                                uniswap_v3_data = get_uniswap_v3_position(web3_base if chain_name == 'base' else web3_optimism, contract_address, parsed_log['token_id'])
-                                increase_liquidity_data = get_uniswap_increase_liquidity(api_key, chain_name, parsed_log['token_id'])
-
-                                token0_address = uniswap_v3_data['token0']
-                                token1_address = uniswap_v3_data['token1']
-                                decimals_token0 = get_token_decimals(web3_base if chain_name == 'base' else web3_optimism, token0_address)
-                                decimals_token1 = get_token_decimals(web3_base if chain_name == 'base' else web3_optimism, token1_address)
-                                print(decimals_token0,decimals_token1)
-                                increase_liquidity_data['amount0'] /= 10**decimals_token0
-                                increase_liquidity_data['amount1'] /= 10**decimals_token1
-
-                                usd_price_token0 = get_token_price_usd(chain_name, token0_address)
-                                usd_price_token1 = get_token_price_usd(chain_name, token1_address)
-
-                                if usd_price_token0 is not None and usd_price_token1 is not None:
-                                    increase_liquidity_data['amount0_usd'] = increase_liquidity_data['amount0'] * usd_price_token0
-                                    increase_liquidity_data['amount1_usd'] = increase_liquidity_data['amount1'] * usd_price_token1
-
-                                parsed_log.update(uniswap_v3_data)
-                                parsed_log.update(increase_liquidity_data)
-                            all_transactions.append(parsed_log)
-    
-    # Convert to DataFrame and append to existing transactions
-    df_new_transactions = pd.DataFrame(all_transactions)
-
-    if not df_existing_transactions.empty:
-        all_data = pd.concat([df_existing_transactions, df_new_transactions])
-    else:
-        all_data = df_new_transactions
-
-    all_data.to_csv(existing_transactions_path, index=False)
-    print("Data saved to", existing_transactions_path)
-    
-    return all_data
-
-    #print(df_transactions)

apr_visualization.py

@@ -1,592 +0,0 @@
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib.dates as mdates
-import plotly.graph_objects as go
-import plotly.express as px
-from plotly.subplots import make_subplots
-import random
-from datetime import datetime, timedelta
-import requests
-import sys
-import json
-from typing import List, Dict, Any
-import logging
-
-logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
-logger = logging.getLogger(__name__)
-
-# Global variable to store the data for reuse
-global_df = None
-
-# Configuration
-API_BASE_URL = "https://afmdb.autonolas.tech"
-
-def get_agent_type_by_name(type_name: str) -> Dict[str, Any]:
-    """Get agent type by name"""
-    response = requests.get(f"{API_BASE_URL}/api/agent-types/name/{type_name}")
-    if response.status_code == 404:
-        logger.error(f"Agent type '{type_name}' not found")
-        return None
-    response.raise_for_status()
-    return response.json()
-
-def get_attribute_definition_by_name(attr_name: str) -> Dict[str, Any]:
-    """Get attribute definition by name"""
-    response = requests.get(f"{API_BASE_URL}/api/attributes/name/{attr_name}")
-    if response.status_code == 404:
-        logger.error(f"Attribute definition '{attr_name}' not found")
-        return None
-    response.raise_for_status()
-    return response.json()
-
-def get_agents_by_type(type_id: int) -> List[Dict[str, Any]]:
-    """Get all agents of a specific type"""
-    response = requests.get(f"{API_BASE_URL}/api/agent-types/{type_id}/agents/")
-    if response.status_code == 404:
-        logger.error(f"No agents found for type ID {type_id}")
-        return []
-    response.raise_for_status()
-    return response.json()
-
-def get_attribute_values_by_type_and_attr(agents: List[Dict[str, Any]], attr_def_id: int) -> List[Dict[str, Any]]:
-    """Get all attribute values for a specific attribute definition across all agents of a given list"""
-    all_attributes = []
-    
-    # For each agent, get their attributes and filter for the one we want
-    for agent in agents:
-        agent_id = agent["agent_id"]
-        
-        # Call the /api/agents/{agent_id}/attributes/ endpoint
-        response = requests.get(f"{API_BASE_URL}/api/agents/{agent_id}/attributes/", params={"limit": 1000})
-        if response.status_code == 404:
-            logger.error(f"No attributes found for agent ID {agent_id}")
-            continue
-        
-        try:
-            response.raise_for_status()
-            agent_attrs = response.json()
-            
-            # Filter for the specific attribute definition ID
-            filtered_attrs = [attr for attr in agent_attrs if attr.get("attr_def_id") == attr_def_id]
-            all_attributes.extend(filtered_attrs)
-        except requests.exceptions.RequestException as e:
-            logger.error(f"Error fetching attributes for agent ID {agent_id}: {e}")
-    
-    return all_attributes
-
-def get_agent_name(agent_id: int, agents: List[Dict[str, Any]]) -> str:
-    """Get agent name from agent ID"""
-    for agent in agents:
-        if agent["agent_id"] == agent_id:
-            return agent["agent_name"]
-    return "Unknown"
-
-def extract_apr_value(attr: Dict[str, Any]) -> Dict[str, Any]:
-    """Extract APR value and timestamp from JSON value"""
-    try:
-        # The APR value is stored in the json_value field
-        if attr["json_value"] is None:
-            return {"apr": None, "timestamp": None, "agent_id": attr["agent_id"], "is_dummy": False}
-        
-        # If json_value is a string, parse it
-        if isinstance(attr["json_value"], str):
-            json_data = json.loads(attr["json_value"])
-        else:
-            json_data = attr["json_value"]
-        
-        apr = json_data.get("apr")
-        timestamp = json_data.get("timestamp")
-        
-        # Convert timestamp to datetime if it exists
-        timestamp_dt = None
-        if timestamp:
-            timestamp_dt = datetime.fromtimestamp(timestamp)
-            
-        return {"apr": apr, "timestamp": timestamp_dt, "agent_id": attr["agent_id"], "is_dummy": False}
-    except (json.JSONDecodeError, KeyError, TypeError) as e:
-        logger.error(f"Error parsing JSON value: {e} for agent_id: {attr.get('agent_id')}")
-        return {"apr": None, "timestamp": None, "agent_id": attr["agent_id"], "is_dummy": False}
-
-def fetch_apr_data_from_db():
-    """
-    Fetch APR data from database using the API.
-    """
-    global global_df
-    
-    try:
-        # Step 1: Find the Modius agent type
-        modius_type = get_agent_type_by_name("Modius")
-        if not modius_type:
-            logger.error("Modius agent type not found, using placeholder data")
-            global_df = pd.DataFrame([])
-            return global_df
-        
-        type_id = modius_type["type_id"]
-        
-        # Step 2: Find the APR attribute definition
-        apr_attr_def = get_attribute_definition_by_name("APR")
-        if not apr_attr_def:
-            logger.error("APR attribute definition not found, using placeholder data")
-            global_df = pd.DataFrame([])
-            return global_df
-            
-        attr_def_id = apr_attr_def["attr_def_id"]
-        
-        # Step 3: Get all agents of type Modius
-        modius_agents = get_agents_by_type(type_id)
-        if not modius_agents:
-            logger.error("No agents of type 'Modius' found")
-            global_df = pd.DataFrame([])
-            return global_df
-        
-        # Step 4: Fetch all APR values for Modius agents
-        apr_attributes = get_attribute_values_by_type_and_attr(modius_agents, attr_def_id)
-        if not apr_attributes:
-            logger.error("No APR values found for 'Modius' agents")
-            global_df = pd.DataFrame([])
-            return global_df
-        
-        # Step 5: Extract APR data
-        apr_data_list = []
-        for attr in apr_attributes:
-            apr_data = extract_apr_value(attr)
-            if apr_data["apr"] is not None and apr_data["timestamp"] is not None:
-                # Get agent name
-                agent_name = get_agent_name(attr["agent_id"], modius_agents)
-                # Add agent name to the data
-                apr_data["agent_name"] = agent_name
-                # Add is_dummy flag (all real data)
-                apr_data["is_dummy"] = False
-                
-                # Mark negative values as "Performance" metrics
-                if apr_data["apr"] < 0:
-                    apr_data["metric_type"] = "Performance"
-                else:
-                    apr_data["metric_type"] = "APR"
-                    
-                apr_data_list.append(apr_data)
-        
-        # Convert list of dictionaries to DataFrame
-        if not apr_data_list:
-            logger.error("No valid APR data extracted")
-            global_df = pd.DataFrame([])
-            return global_df
-        
-        global_df = pd.DataFrame(apr_data_list)
-        return global_df
-        
-    except requests.exceptions.RequestException as e:
-        logger.error(f"API request error: {e}")
-        global_df = pd.DataFrame([])
-        return global_df
-    except Exception as e:
-        logger.error(f"Error fetching APR data: {e}")
-        global_df = pd.DataFrame([])
-        return global_df
-
-def generate_apr_visualizations():
-    """Generate APR visualizations with real data only (no dummy data)"""
-    global global_df
-    
-    # Fetch data from database
-    df = fetch_apr_data_from_db()
-    
-    # If we got no data at all, return placeholder figures
-    if df.empty:
-        logger.info("No APR data available. Using fallback visualization.")
-        # Create empty visualizations with a message using Plotly
-        fig = go.Figure()
-        fig.add_annotation(
-            x=0.5, y=0.5,
-            text="No APR data available",
-            font=dict(size=20),
-            showarrow=False
-        )
-        fig.update_layout(
-            xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
-            yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
-        )
-        
-        # Save as static files for reference
-        fig.write_html("modius_apr_per_agent_graph.html")
-        fig.write_image("modius_apr_per_agent_graph.png")
-        fig.write_html("modius_apr_combined_graph.html")
-        fig.write_image("modius_apr_combined_graph.png")
-        
-        csv_file = None
-        return fig, fig, csv_file
-    
-    # No longer generating dummy data
-    # Set global_df for access by other functions
-    global_df = df
-    
-    # Save to CSV before creating visualizations
-    csv_file = save_to_csv(df)
-    
-    # Create per-agent time series graph (returns figure object)
-    per_agent_fig = create_time_series_graph_per_agent(df)
-    
-    # Create combined time series graph (returns figure object)
-    combined_fig = create_combined_time_series_graph(df)
-    
-    return per_agent_fig, combined_fig, csv_file
-
-def create_time_series_graph_per_agent(df):
-    """Create a time series graph for each agent using Plotly"""
-    # Get unique agents
-    unique_agents = df['agent_id'].unique()
-    
-    if len(unique_agents) == 0:
-        logger.error("No agent data to plot")
-        fig = go.Figure()
-        fig.add_annotation(
-            text="No agent data available",
-            x=0.5, y=0.5,
-            showarrow=False, font=dict(size=20)
-        )
-        return fig
-    
-    # Create a subplot figure for each agent
-    fig = make_subplots(rows=len(unique_agents), cols=1, 
-                       subplot_titles=[f"Agent: {df[df['agent_id'] == agent_id]['agent_name'].iloc[0]}" 
-                                      for agent_id in unique_agents],
-                       vertical_spacing=0.1)
-    
-    # Plot data for each agent
-    for i, agent_id in enumerate(unique_agents):
-        agent_data = df[df['agent_id'] == agent_id].copy()
-        agent_name = agent_data['agent_name'].iloc[0]
-        row = i + 1
-        
-        # Add zero line to separate APR and Performance
-        fig.add_shape(
-            type="line", line=dict(dash="solid", width=1.5, color="black"),
-            y0=0, y1=0, x0=agent_data['timestamp'].min(), x1=agent_data['timestamp'].max(),
-            row=row, col=1
-        )
-        
-        # Add background colors
-        fig.add_shape(
-            type="rect", fillcolor="rgba(230, 243, 255, 0.3)", line=dict(width=0),
-            y0=0, y1=1000, x0=agent_data['timestamp'].min(), x1=agent_data['timestamp'].max(),
-            row=row, col=1, layer="below"
-        )
-        fig.add_shape(
-            type="rect", fillcolor="rgba(255, 230, 230, 0.3)", line=dict(width=0),
-            y0=-1000, y1=0, x0=agent_data['timestamp'].min(), x1=agent_data['timestamp'].max(),
-            row=row, col=1, layer="below"
-        )
-        
-        # Create separate dataframes for different data types
-        apr_data = agent_data[agent_data['metric_type'] == 'APR']
-        perf_data = agent_data[agent_data['metric_type'] == 'Performance']
-        
-        # Sort all data by timestamp for the line plots
-        combined_agent_data = agent_data.sort_values('timestamp')
-        
-        # Add main line connecting all points
-        fig.add_trace(
-            go.Scatter(
-                x=combined_agent_data['timestamp'], 
-                y=combined_agent_data['apr'],
-                mode='lines',
-                line=dict(color='purple', width=2),
-                name=f'{agent_name}',
-                legendgroup=agent_name,
-                showlegend=(i == 0),  # Only show in legend once
-                hovertemplate='Time: %{x}<br>Value: %{y:.2f}<extra></extra>'
-            ),
-            row=row, col=1
-        )
-        
-        # Add scatter points for APR values
-        if not apr_data.empty:
-            fig.add_trace(
-                go.Scatter(
-                    x=apr_data['timestamp'], 
-                    y=apr_data['apr'],
-                    mode='markers',
-                    marker=dict(color='blue', size=10, symbol='circle'),
-                    name='APR',
-                    legendgroup='APR',
-                    showlegend=(i == 0),
-                    hovertemplate='Time: %{x}<br>APR: %{y:.2f}<extra></extra>'
-                ),
-                row=row, col=1
-            )
-        
-        # Add scatter points for Performance values
-        if not perf_data.empty:
-            fig.add_trace(
-                go.Scatter(
-                    x=perf_data['timestamp'], 
-                    y=perf_data['apr'],
-                    mode='markers',
-                    marker=dict(color='red', size=10, symbol='square'),
-                    name='Performance',
-                    legendgroup='Performance',
-                    showlegend=(i == 0),
-                    hovertemplate='Time: %{x}<br>Performance: %{y:.2f}<extra></extra>'
-                ),
-                row=row, col=1
-            )
-        
-        # Update axes
-        fig.update_xaxes(title_text="Time", row=row, col=1)
-        fig.update_yaxes(title_text="Value", row=row, col=1, gridcolor='rgba(0,0,0,0.1)')
-    
-    # Update layout
-    fig.update_layout(
-        height=400 * len(unique_agents),
-        width=1000,
-        title_text="APR and Performance Values per Agent",
-        template="plotly_white",
-        legend=dict(
-            orientation="h",
-            yanchor="bottom",
-            y=1.02,
-            xanchor="right",
-            x=1
-        ),
-        margin=dict(r=20, l=20, t=30, b=20),
-        hovermode="closest"
-    )
-    
-    # Save the figure (still useful for reference)
-    graph_file = "modius_apr_per_agent_graph.html"
-    fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)
-    
-    # Also save as image for compatibility
-    img_file = "modius_apr_per_agent_graph.png"
-    fig.write_image(img_file)
-    
-    logger.info(f"Per-agent graph saved to {graph_file} and {img_file}")
-    
-    # Return the figure object for direct use in Gradio
-    return fig
-
-def create_combined_time_series_graph(df):
-    """Create a combined time series graph for all agents using Plotly"""
-    if len(df) == 0:
-        logger.error("No data to plot combined graph")
-        fig = go.Figure()
-        fig.add_annotation(
-            text="No data available",
-            x=0.5, y=0.5,
-            showarrow=False, font=dict(size=20)
-        )
-        return fig
-    
-    # Create Plotly figure
-    fig = go.Figure()
-    
-    # Get unique agents
-    unique_agents = df['agent_id'].unique()
-    
-    # Define a color scale for different agents
-    colors = px.colors.qualitative.Plotly[:len(unique_agents)]
-    
-    # Add background shapes for APR and Performance regions
-    min_time = df['timestamp'].min()
-    max_time = df['timestamp'].max()
-    
-    # Add shape for APR region (above zero)
-    fig.add_shape(
-        type="rect",
-        fillcolor="rgba(230, 243, 255, 0.3)",
-        line=dict(width=0),
-        y0=0, y1=1000,
-        x0=min_time, x1=max_time,
-        layer="below"
-    )
-    
-    # Add shape for Performance region (below zero)
-    fig.add_shape(
-        type="rect",
-        fillcolor="rgba(255, 230, 230, 0.3)",
-        line=dict(width=0),
-        y0=-1000, y1=0,
-        x0=min_time, x1=max_time,
-        layer="below"
-    )
-    
-    # Add zero line
-    fig.add_shape(
-        type="line",
-        line=dict(dash="solid", width=1.5, color="black"),
-        y0=0, y1=0,
-        x0=min_time, x1=max_time
-    )
-    
-    # Add data for each agent
-    for i, agent_id in enumerate(unique_agents):
-        agent_data = df[df['agent_id'] == agent_id].copy()
-        agent_name = agent_data['agent_name'].iloc[0]
-        color = colors[i % len(colors)]
-        
-        # Sort the data by timestamp
-        agent_data = agent_data.sort_values('timestamp')
-        
-        # Add the combined line for both APR and Performance
-        fig.add_trace(
-            go.Scatter(
-                x=agent_data['timestamp'],
-                y=agent_data['apr'],
-                mode='lines',
-                line=dict(color=color, width=2),
-                name=f'{agent_name}',
-                legendgroup=agent_name,
-                hovertemplate='Time: %{x}<br>Value: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>'
-            )
-        )
-        
-        # Add scatter points for APR values
-        apr_data = agent_data[agent_data['metric_type'] == 'APR']
-        if not apr_data.empty:
-            fig.add_trace(
-                go.Scatter(
-                    x=apr_data['timestamp'],
-                    y=apr_data['apr'],
-                    mode='markers',
-                    marker=dict(color=color, symbol='circle', size=8),
-                    name=f'{agent_name} APR',
-                    legendgroup=agent_name,
-                    showlegend=False,
-                    hovertemplate='Time: %{x}<br>APR: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>'
-                )
-            )
-        
-        # Add scatter points for Performance values
-        perf_data = agent_data[agent_data['metric_type'] == 'Performance']
-        if not perf_data.empty:
-            fig.add_trace(
-                go.Scatter(
-                    x=perf_data['timestamp'],
-                    y=perf_data['apr'],
-                    mode='markers',
-                    marker=dict(color=color, symbol='square', size=8),
-                    name=f'{agent_name} Perf',
-                    legendgroup=agent_name,
-                    showlegend=False,
-                    hovertemplate='Time: %{x}<br>Performance: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>'
-                )
-            )
-    
-    # Update layout
-    fig.update_layout(
-        title="APR and Performance Values for All Agents",
-        xaxis_title="Time",
-        yaxis_title="Value",
-        template="plotly_white",
-        height=600,
-        width=1000,
-        legend=dict(
-            orientation="h",
-            yanchor="bottom",
-            y=1.02,
-            xanchor="right",
-            x=1,
-            groupclick="toggleitem"
-        ),
-        margin=dict(r=20, l=20, t=30, b=20),
-        hovermode="closest"
-    )
-    
-    # Update axes
-    fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='rgba(0,0,0,0.1)')
-    fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='rgba(0,0,0,0.1)')
-    
-    # Save the figure (still useful for reference)
-    graph_file = "modius_apr_combined_graph.html"
-    fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)
-    
-    # Also save as image for compatibility
-    img_file = "modius_apr_combined_graph.png"
-    fig.write_image(img_file)
-    
-    logger.info(f"Combined graph saved to {graph_file} and {img_file}")
-    
-    # Return the figure object for direct use in Gradio
-    return fig
-
-def save_to_csv(df):
-    """Save the APR data DataFrame to a CSV file and return the file path"""
-    if df.empty:
-        logger.error("No APR data to save to CSV")
-        return None
-    
-    # Define the CSV file path
-    csv_file = "modius_apr_values.csv"
-    
-    # Save to CSV
-    df.to_csv(csv_file, index=False)
-    logger.info(f"APR data saved to {csv_file}")
-    
-    # Also generate a statistics CSV file
-    stats_df = generate_statistics_from_data(df)
-    stats_csv = "modius_apr_statistics.csv"
-    stats_df.to_csv(stats_csv, index=False)
-    logger.info(f"Statistics saved to {stats_csv}")
-    
-    return csv_file
-
-def generate_statistics_from_data(df):
-    """Generate statistics from the APR data"""
-    if df.empty:
-        return pd.DataFrame()
-    
-    # Get unique agents
-    unique_agents = df['agent_id'].unique()
-    stats_list = []
-    
-    # Generate per-agent statistics
-    for agent_id in unique_agents:
-        agent_data = df[df['agent_id'] == agent_id]
-        agent_name = agent_data['agent_name'].iloc[0]
-        
-        # APR statistics
-        apr_data = agent_data[agent_data['metric_type'] == 'APR']
-        real_apr = apr_data[apr_data['is_dummy'] == False]
-        
-        # Performance statistics
-        perf_data = agent_data[agent_data['metric_type'] == 'Performance']
-        real_perf = perf_data[perf_data['is_dummy'] == False]
-        
-        stats = {
-            'agent_id': agent_id,
-            'agent_name': agent_name,
-            'total_points': len(agent_data),
-            'apr_points': len(apr_data),
-            'performance_points': len(perf_data),
-            'real_apr_points': len(real_apr),
-            'real_performance_points': len(real_perf),
-            'avg_apr': apr_data['apr'].mean() if not apr_data.empty else None,
-            'avg_performance': perf_data['apr'].mean() if not perf_data.empty else None,
-            'max_apr': apr_data['apr'].max() if not apr_data.empty else None,
-            'min_apr': apr_data['apr'].min() if not apr_data.empty else None,
-            'latest_timestamp': agent_data['timestamp'].max().strftime('%Y-%m-%d %H:%M:%S') if not agent_data.empty else None
-        }
-        stats_list.append(stats)
-    
-    # Generate overall statistics
-    apr_only = df[df['metric_type'] == 'APR']
-    perf_only = df[df['metric_type'] == 'Performance']
-    
-    overall_stats = {
-        'agent_id': 'ALL',
-        'agent_name': 'All Agents',
-        'total_points': len(df),
-        'apr_points': len(apr_only),
-        'performance_points': len(perf_only),
-        'real_apr_points': len(apr_only[apr_only['is_dummy'] == False]),
-        'real_performance_points': len(perf_only[perf_only['is_dummy'] == False]),
-        'avg_apr': apr_only['apr'].mean() if not apr_only.empty else None,
-        'avg_performance': perf_only['apr'].mean() if not perf_only.empty else None,
-        'max_apr': apr_only['apr'].max() if not apr_only.empty else None,
-        'min_apr': apr_only['apr'].min() if not apr_only.empty else None,
-        'latest_timestamp': df['timestamp'].max().strftime('%Y-%m-%d %H:%M:%S') if not df.empty else None
-    }
-    stats_list.append(overall_stats)
-    
-    return pd.DataFrame(stats_list)

apr_vs_agent_hash.py

@@ -1,475 +0,0 @@
-import pandas as pd
-import plotly.graph_objects as go
-import plotly.express as px
-from datetime import datetime
-import logging
-import json
-import os
-
-# Set up logging
-logger = logging.getLogger(__name__)
-
-def create_apr_vs_agent_hash_graph(df):
-    """
-    Create a box plot showing APR values distribution for each agent hash version.
-    
-    Args:
-        df: DataFrame containing the APR data with agent_hash column
-    
-    Returns:
-        A Plotly figure object
-    """
-    if len(df) == 0 or 'agent_hash' not in df.columns:
-        logger.error("No data or agent_hash column not found to plot APR vs agent hash graph")
-        fig = go.Figure()
-        fig.add_annotation(
-            text="No agent hash data available",
-            x=0.5, y=0.5,
-            showarrow=False, font=dict(size=20)
-        )
-        return fig
-    
-    # Filter for APR data only and ensure agent_hash is not null
-    apr_data = df[(df['metric_type'] == 'APR') & (df['agent_hash'].notna())].copy()
-    
-    if len(apr_data) == 0:
-        logger.error("No valid APR data with agent_hash found")
-        fig = go.Figure()
-        fig.add_annotation(
-            text="No valid APR data with agent_hash found",
-            x=0.5, y=0.5,
-            showarrow=False, font=dict(size=20)
-        )
-        return fig
-    
-    # Filter out outliers (APR values above 200 or below -200)
-    outlier_data = apr_data[(apr_data['apr'] > 200) | (apr_data['apr'] < -200)].copy()
-    apr_data_filtered = apr_data[(apr_data['apr'] <= 200) & (apr_data['apr'] >= -200)].copy()
-    
-    # Log the outliers for better debugging
-    if len(outlier_data) > 0:
-        excluded_count = len(outlier_data)
-        logger.info(f"Excluded {excluded_count} data points with outlier APR values (>200 or <-200)")
-        
-        # Group outliers by agent for detailed logging
-        outlier_agents = outlier_data.groupby('agent_name')
-        for agent_name, agent_outliers in outlier_agents:
-            logger.info(f"Agent '{agent_name}' has {len(agent_outliers)} outlier values:")
-            for idx, row in agent_outliers.iterrows():
-                logger.info(f"  - APR: {row['apr']}, timestamp: {row['timestamp']}, agent_hash: {row['agent_hash']}")
-    
-    # Use the filtered data for all subsequent operations
-    apr_data = apr_data_filtered
-    
-    # Create Plotly figure
-    fig = go.Figure()
-    
-    # Add a zero line that spans the entire width
-    fig.add_shape(
-        type="line",
-        line=dict(dash="solid", width=1.5, color="black"),
-        y0=0, y1=0,
-        x0=-0.5, x1=10,  # Will be adjusted later based on number of boxes
-        layer="below"
-    )
-    
-    # Add background shapes for positive and negative regions
-    # These will be adjusted later based on the actual x-axis range
-    fig.add_shape(
-        type="rect",
-        fillcolor="rgba(230, 243, 255, 0.3)",
-        line=dict(width=0),
-        y0=0, y1=100,  # Use a fixed positive value
-        x0=-0.5, x1=10,  # Will be adjusted later
-        layer="below"
-    )
-    
-    fig.add_shape(
-        type="rect",
-        fillcolor="rgba(255, 230, 230, 0.3)",
-        line=dict(width=0),
-        y0=-100, y1=0,  # Use a fixed negative value
-        x0=-0.5, x1=10,  # Will be adjusted later
-        layer="below"
-    )
-    
-    # Group by agent_hash
-    unique_hashes = apr_data['agent_hash'].unique()
-    
-    # Map for version labels based on hash endings
-    version_map = {}
-    for hash_val in unique_hashes:
-        if hash_val.endswith("tby"):
-            version_map[hash_val] = "v0.4.1"
-        elif hash_val.endswith("vq"):
-            version_map[hash_val] = "v0.4.2"
-        else:
-            # For any other hashes, use the last 6 characters
-            version_map[hash_val] = f"Hash: {hash_val[-6:]}"
-    
-    # Sort hashes by version (v0.4.1 first, then v0.4.2)
-    sorted_hashes = sorted(unique_hashes, key=lambda h: "1" if h.endswith("tby") else "2" if h.endswith("vq") else h)
-    
-    # Colors for different versions
-    version_colors = {
-        "v0.4.1": "rgba(31, 119, 180, 0.7)",  # Blue
-        "v0.4.2": "rgba(44, 160, 44, 0.7)",   # Green
-    }
-    
-    # Default color for other hashes
-    default_color = "rgba(214, 39, 40, 0.7)"  # Red
-    
-    # Prepare data for box plots and statistics
-    box_data = []
-    version_stats = {}
-    
-    # X-axis positions and labels
-    x_positions = []
-    x_labels = []
-    
-    # Process each hash to create box plot data
-    for i, agent_hash in enumerate(sorted_hashes):
-        hash_data = apr_data[apr_data['agent_hash'] == agent_hash]
-        
-        # Get agent name for this hash (should be the same for all records with this hash)
-        agent_name = hash_data['agent_name'].iloc[0] if not hash_data.empty else "Unknown"
-        
-        # Get version label
-        version = version_map[agent_hash]
-        
-        # Choose color based on version
-        if version in version_colors:
-            color = version_colors[version]
-        else:
-            color = default_color
-        
-        # Calculate statistics for this hash
-        apr_values = hash_data['apr'].tolist()
-        median_apr = hash_data['apr'].median()
-        mean_apr = hash_data['apr'].mean()
-        min_apr = hash_data['apr'].min()
-        max_apr = hash_data['apr'].max()
-        count = len(apr_values)
-        
-        # Store statistics for later use
-        if version not in version_stats:
-            version_stats[version] = {
-                'apr_values': [],
-                'count': 0,
-                'hashes': []
-            }
-        
-        version_stats[version]['apr_values'].extend(apr_values)
-        version_stats[version]['count'] += count
-        version_stats[version]['hashes'].append(agent_hash)
-        
-        # Create label with version only (no hash)
-        label = f"{version}"
-        
-        # Add to x-axis positions and labels
-        x_positions.append(i)
-        x_labels.append(label)
-        
-        # Create hover text with detailed statistics
-        hover_text = (
-            f"Version: {version}<br>"
-            f"Agent: {agent_name}<br>"
-            f"Hash: {agent_hash}<br>"
-            f"Median APR: {median_apr:.2f}%<br>"
-            f"Mean APR: {mean_apr:.2f}%<br>"
-            f"Min APR: {min_apr:.2f}%<br>"
-            f"Max APR: {max_apr:.2f}%<br>"
-            f"Data points: {count}"
-        )
-        
-        # Add box plot for this hash
-        fig.add_trace(
-            go.Box(
-                y=apr_values,
-                x=[i] * len(apr_values),  # Position on x-axis
-                name=label,
-                boxpoints='outliers',  # Show only outlier points instead of all points
-                jitter=0.1,  # Reduced jitter for less horizontal spread
-                pointpos=0,  # Position of points relative to box
-                marker=dict(
-                    color=color,
-                    size=6,  # Smaller point size
-                    opacity=0.7,  # Add transparency
-                    line=dict(width=1, color='black')
-                ),
-                line=dict(
-                    color='black',
-                    width=2  # Thicker line for better visibility
-                ),
-                fillcolor=color,
-                hoverinfo='text',
-                hovertext=hover_text,
-                showlegend=False,
-                boxmean=True,  # Show mean as a dashed line
-                whiskerwidth=0.8,  # Slightly thinner whiskers
-                width=0.6  # Wider boxes
-            )
-        )
-        
-        logger.info(f"Added box plot for agent hash {agent_hash} ({version}) with {count} points")
-        
-        # Add text annotation with median value above each box
-        fig.add_annotation(
-            x=i,
-            y=median_apr + 5,  # Position above the box
-            text=f"{median_apr:.1f}%",
-            showarrow=False,
-            font=dict(
-                family="Arial, sans-serif",
-                size=12,
-                color="black",
-                weight="bold"
-            )
-        )
-    
-    # Calculate improvement metrics between versions
-    if "v0.4.1" in version_stats and "v0.4.2" in version_stats:
-        v041_values = version_stats["v0.4.1"]["apr_values"]
-        v042_values = version_stats["v0.4.2"]["apr_values"]
-        
-        v041_median = pd.Series(v041_values).median()
-        v042_median = pd.Series(v042_values).median()
-        
-        improvement = v042_median - v041_median
-        improvement_pct = (improvement / abs(v041_median)) * 100 if v041_median != 0 else float('inf')
-        
-        # Determine if the change is positive or negative
-        is_improvement = improvement > 0
-        change_color = "green" if is_improvement else "red"
-        change_text = "improvement" if is_improvement else "decrease"
-        
-        # Add annotation showing improvement with better styling (black color)
-        fig.add_annotation(
-            x=(len(sorted_hashes) - 1) / 2,  # Center of the x-axis
-            y=90,  # Top of the chart
-            text=f"<b>Version Comparison:</b> {abs(improvement):.2f}% {change_text} from v0.4.1 to v0.4.2",
-            showarrow=False,
-            font=dict(
-                family="Arial, sans-serif",
-                size=16,
-                color="black",  # Changed to black
-                weight="bold"
-            ),
-            bgcolor="rgba(255, 255, 255, 0.9)",
-            bordercolor="black",  # Changed to black
-            borderwidth=2,
-            borderpad=6,
-            opacity=0.9
-        )
-    
-    # Update the shapes to match the actual x-axis range
-    num_boxes = len(sorted_hashes)
-    fig.update_shapes(
-        dict(x0=-0.5, x1=num_boxes - 0.5),
-        selector=dict(type='rect')
-    )
-    fig.update_shapes(
-        dict(x0=-0.5, x1=num_boxes - 0.5),
-        selector=dict(type='line')
-    )
-    
-    # Update layout with improved styling
-    fig.update_layout(
-        title=dict(
-            text="Performance Graph",
-            font=dict(
-                family="Arial, sans-serif",
-                size=24,  # Larger title
-                color="black",
-                weight="bold"
-            ),
-            x=0.5,  # Center the title
-            y=0.95  # Position slightly higher
-        ),
-        xaxis_title=dict(
-            text="Agent Version",
-            font=dict(
-                family="Arial, sans-serif",
-                size=18,  # Larger axis title
-                color="black",
-                weight="bold"
-            )
-        ),
-        yaxis_title=None,  # Remove the y-axis title as we'll use annotations instead
-        template="plotly_white",
-        height=900,  # Increased height for better visualization and more vertical space
-        # width parameter removed to allow full responsiveness
-        autosize=True,  # Enable auto-sizing for responsiveness
-        boxmode='group',  # Group boxes together
-        margin=dict(r=50, l=120, t=100, b=100),  # Reduced right margin since guide was removed
-        hovermode="closest",
-        plot_bgcolor='rgba(250,250,250,0.9)',  # Slightly off-white background
-        paper_bgcolor='white',
-        font=dict(
-            family="Arial, sans-serif",
-            size=14,
-            color="black"
-        ),
-        showlegend=False
-    )
-    
-    # Add single annotation for y-axis
-    fig.add_annotation(
-        x=-0.08,  # Position further from the y-axis to avoid overlapping with tick labels
-        y=0,      # Center of the y-axis
-        xref="paper",
-        yref="y",
-        text="Agent APR [%]",
-        showarrow=False,
-        font=dict(size=16, family="Arial, sans-serif", color="black", weight="bold"),  # Adjusted font size
-        textangle=-90,  # Rotate text to be vertical
-        align="center"
-    )
-    
-    # Box plot guide removed as per user request
-    
-    # Update y-axis with autoscaling
-    fig.update_yaxes(
-        showgrid=True, 
-        gridwidth=1, 
-        gridcolor='rgba(0,0,0,0.1)',
-        # Use autoscaling
-        autorange=True,  # Enable autoscaling
-        tickformat=".2f",  # Format tick labels with 2 decimal places
-        tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold"),  # Adjusted font size
-        title=None  # Remove the built-in axis title since we're using annotations
-    )
-    
-    # Update x-axis with custom labels
-    fig.update_xaxes(
-        showgrid=True, 
-        gridwidth=1, 
-        gridcolor='rgba(0,0,0,0.1)',
-        tickmode='array',
-        tickvals=x_positions,
-        ticktext=x_labels,
-        tickangle=-45,  # Angle the labels for better readability
-        tickfont=dict(size=14, family="Arial, sans-serif", color="black", weight="bold")  # Adjusted font size
-    )
-    
-    try:
-        # Save the figure
-        graph_file = "modius_apr_vs_agent_hash_graph.html"
-        fig.write_html(graph_file, include_plotlyjs='cdn', full_html=False)
-        
-        # Also save as image for compatibility
-        img_file = "modius_apr_vs_agent_hash_graph.png"
-        try:
-            fig.write_image(img_file)
-            logger.info(f"APR vs agent hash graph saved to {graph_file} and {img_file}")
-        except Exception as e:
-            logger.error(f"Error saving image: {e}")
-            logger.info(f"APR vs agent hash graph saved to {graph_file} only")
-        
-        # Return the figure object for direct use in Gradio
-        return fig
-    except Exception as e:
-        logger.error(f"Error creating APR vs agent hash graph: {e}")
-        
-        # Create a simpler graph as fallback
-        simple_fig = go.Figure()
-        
-        # Add zero line
-        simple_fig.add_shape(
-            type="line",
-            line=dict(dash="solid", width=1.5, color="black"),
-            y0=0, y1=0,
-            x0=-0.5, x1=1.5  # Fixed values for error case
-        )
-        
-        # Add a note about the error
-        simple_fig.add_annotation(
-            text=f"Error creating graph: {str(e)}",
-            x=0.5, y=0.5,
-            showarrow=False,
-            font=dict(size=15, color="red")
-        )
-        
-        return simple_fig
-
-def save_apr_vs_agent_hash_to_csv(df):
-    """
-    Save the APR vs agent hash data to a CSV file.
-    
-    Args:
-        df: DataFrame containing the APR data with agent_hash column
-    
-    Returns:
-        The path to the saved CSV file, or None if no data was saved
-    """
-    if df.empty or 'agent_hash' not in df.columns:
-        logger.error("No data or agent_hash column not found to save to CSV")
-        return None
-    
-    # Filter for APR data only and ensure agent_hash is not null
-    apr_data = df[(df['metric_type'] == 'APR') & (df['agent_hash'].notna())].copy()
-    
-    if apr_data.empty:
-        logger.error("No valid APR data with agent_hash found to save to CSV")
-        return None
-    
-    # Define the CSV file path
-    csv_file = "modius_apr_vs_agent_hash.csv"
-    
-    # Save to CSV
-    apr_data.to_csv(csv_file, index=False)
-    logger.info(f"APR vs agent hash data saved to {csv_file}")
-    
-    return csv_file
-
-def generate_apr_vs_agent_hash_visualizations(df):
-    """
-    Generate APR vs agent hash visualizations.
-    
-    Args:
-        df: DataFrame containing the APR data
-    
-    Returns:
-        A tuple containing the Plotly figure object and the path to the saved CSV file
-    """
-    if df.empty:
-        logger.info("No APR data available for agent hash visualization.")
-        # Create empty visualization with a message using Plotly
-        fig = go.Figure()
-        fig.add_annotation(
-            x=0.5, y=0.5,
-            text="No APR data available for agent hash visualization",
-            font=dict(size=20),
-            showarrow=False
-        )
-        fig.update_layout(
-            xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
-            yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
-        )
-        
-        return fig, None
-    
-    # Check if agent_hash column exists
-    if 'agent_hash' not in df.columns:
-        logger.error("agent_hash column not found in DataFrame")
-        fig = go.Figure()
-        fig.add_annotation(
-            x=0.5, y=0.5,
-            text="agent_hash column not found in data",
-            font=dict(size=20),
-            showarrow=False
-        )
-        fig.update_layout(
-            xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
-            yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
-        )
-        
-        return fig, None
-    
-    # Save to CSV before creating visualization
-    csv_file = save_apr_vs_agent_hash_to_csv(df)
-    
-    # Create the visualization
-    fig = create_apr_vs_agent_hash_graph(df)
-    
-    return fig, csv_file

contracts/NonfungiblePositionManager.json

@@ -1,1228 +0,0 @@
-{
-    "contractName": "",
-    "abi": [
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "_factory",
-                    "type": "address"
-                },
-                {
-                    "internalType": "address",
-                    "name": "_WETH9",
-                    "type": "address"
-                },
-                {
-                    "internalType": "address",
-                    "name": "_tokenDescriptor_",
-                    "type": "address"
-                }
-            ],
-            "stateMutability": "nonpayable",
-            "type": "constructor"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "address",
-                    "name": "owner",
-                    "type": "address"
-                },
-                {
-                    "indexed": true,
-                    "internalType": "address",
-                    "name": "approved",
-                    "type": "address"
-                },
-                {
-                    "indexed": true,
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                }
-            ],
-            "name": "Approval",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "address",
-                    "name": "owner",
-                    "type": "address"
-                },
-                {
-                    "indexed": true,
-                    "internalType": "address",
-                    "name": "operator",
-                    "type": "address"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "bool",
-                    "name": "approved",
-                    "type": "bool"
-                }
-            ],
-            "name": "ApprovalForAll",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "address",
-                    "name": "recipient",
-                    "type": "address"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "uint256",
-                    "name": "amount0",
-                    "type": "uint256"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "uint256",
-                    "name": "amount1",
-                    "type": "uint256"
-                }
-            ],
-            "name": "Collect",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "uint128",
-                    "name": "liquidity",
-                    "type": "uint128"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "uint256",
-                    "name": "amount0",
-                    "type": "uint256"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "uint256",
-                    "name": "amount1",
-                    "type": "uint256"
-                }
-            ],
-            "name": "DecreaseLiquidity",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "uint128",
-                    "name": "liquidity",
-                    "type": "uint128"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "uint256",
-                    "name": "amount0",
-                    "type": "uint256"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "uint256",
-                    "name": "amount1",
-                    "type": "uint256"
-                }
-            ],
-            "name": "IncreaseLiquidity",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "address",
-                    "name": "from",
-                    "type": "address"
-                },
-                {
-                    "indexed": true,
-                    "internalType": "address",
-                    "name": "to",
-                    "type": "address"
-                },
-                {
-                    "indexed": true,
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                }
-            ],
-            "name": "Transfer",
-            "type": "event"
-        },
-        {
-            "inputs": [],
-            "name": "DOMAIN_SEPARATOR",
-            "outputs": [
-                {
-                    "internalType": "bytes32",
-                    "name": "",
-                    "type": "bytes32"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [],
-            "name": "PERMIT_TYPEHASH",
-            "outputs": [
-                {
-                    "internalType": "bytes32",
-                    "name": "",
-                    "type": "bytes32"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [],
-            "name": "WETH9",
-            "outputs": [
-                {
-                    "internalType": "address",
-                    "name": "",
-                    "type": "address"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "to",
-                    "type": "address"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                }
-            ],
-            "name": "approve",
-            "outputs": [],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "owner",
-                    "type": "address"
-                }
-            ],
-            "name": "balanceOf",
-            "outputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "",
-                    "type": "uint256"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [],
-            "name": "baseURI",
-            "outputs": [
-                {
-                    "internalType": "string",
-                    "name": "",
-                    "type": "string"
-                }
-            ],
-            "stateMutability": "pure",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                }
-            ],
-            "name": "burn",
-            "outputs": [],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "components": [
-                        {
-                            "internalType": "uint256",
-                            "name": "tokenId",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "address",
-                            "name": "recipient",
-                            "type": "address"
-                        },
-                        {
-                            "internalType": "uint128",
-                            "name": "amount0Max",
-                            "type": "uint128"
-                        },
-                        {
-                            "internalType": "uint128",
-                            "name": "amount1Max",
-                            "type": "uint128"
-                        }
-                    ],
-                    "internalType": "struct INonfungiblePositionManager.CollectParams",
-                    "name": "params",
-                    "type": "tuple"
-                }
-            ],
-            "name": "collect",
-            "outputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "amount0",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "amount1",
-                    "type": "uint256"
-                }
-            ],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "token0",
-                    "type": "address"
-                },
-                {
-                    "internalType": "address",
-                    "name": "token1",
-                    "type": "address"
-                },
-                {
-                    "internalType": "uint24",
-                    "name": "fee",
-                    "type": "uint24"
-                },
-                {
-                    "internalType": "uint160",
-                    "name": "sqrtPriceX96",
-                    "type": "uint160"
-                }
-            ],
-            "name": "createAndInitializePoolIfNecessary",
-            "outputs": [
-                {
-                    "internalType": "address",
-                    "name": "pool",
-                    "type": "address"
-                }
-            ],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "components": [
-                        {
-                            "internalType": "uint256",
-                            "name": "tokenId",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "uint128",
-                            "name": "liquidity",
-                            "type": "uint128"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "amount0Min",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "amount1Min",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "deadline",
-                            "type": "uint256"
-                        }
-                    ],
-                    "internalType": "struct INonfungiblePositionManager.DecreaseLiquidityParams",
-                    "name": "params",
-                    "type": "tuple"
-                }
-            ],
-            "name": "decreaseLiquidity",
-            "outputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "amount0",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "amount1",
-                    "type": "uint256"
-                }
-            ],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [],
-            "name": "factory",
-            "outputs": [
-                {
-                    "internalType": "address",
-                    "name": "",
-                    "type": "address"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                }
-            ],
-            "name": "getApproved",
-            "outputs": [
-                {
-                    "internalType": "address",
-                    "name": "",
-                    "type": "address"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "components": [
-                        {
-                            "internalType": "uint256",
-                            "name": "tokenId",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "amount0Desired",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "amount1Desired",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "amount0Min",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "amount1Min",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "deadline",
-                            "type": "uint256"
-                        }
-                    ],
-                    "internalType": "struct INonfungiblePositionManager.IncreaseLiquidityParams",
-                    "name": "params",
-                    "type": "tuple"
-                }
-            ],
-            "name": "increaseLiquidity",
-            "outputs": [
-                {
-                    "internalType": "uint128",
-                    "name": "liquidity",
-                    "type": "uint128"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "amount0",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "amount1",
-                    "type": "uint256"
-                }
-            ],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "owner",
-                    "type": "address"
-                },
-                {
-                    "internalType": "address",
-                    "name": "operator",
-                    "type": "address"
-                }
-            ],
-            "name": "isApprovedForAll",
-            "outputs": [
-                {
-                    "internalType": "bool",
-                    "name": "",
-                    "type": "bool"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "components": [
-                        {
-                            "internalType": "address",
-                            "name": "token0",
-                            "type": "address"
-                        },
-                        {
-                            "internalType": "address",
-                            "name": "token1",
-                            "type": "address"
-                        },
-                        {
-                            "internalType": "uint24",
-                            "name": "fee",
-                            "type": "uint24"
-                        },
-                        {
-                            "internalType": "int24",
-                            "name": "tickLower",
-                            "type": "int24"
-                        },
-                        {
-                            "internalType": "int24",
-                            "name": "tickUpper",
-                            "type": "int24"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "amount0Desired",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "amount1Desired",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "amount0Min",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "amount1Min",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "address",
-                            "name": "recipient",
-                            "type": "address"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "deadline",
-                            "type": "uint256"
-                        }
-                    ],
-                    "internalType": "struct INonfungiblePositionManager.MintParams",
-                    "name": "params",
-                    "type": "tuple"
-                }
-            ],
-            "name": "mint",
-            "outputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint128",
-                    "name": "liquidity",
-                    "type": "uint128"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "amount0",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "amount1",
-                    "type": "uint256"
-                }
-            ],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "bytes[]",
-                    "name": "data",
-                    "type": "bytes[]"
-                }
-            ],
-            "name": "multicall",
-            "outputs": [
-                {
-                    "internalType": "bytes[]",
-                    "name": "results",
-                    "type": "bytes[]"
-                }
-            ],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [],
-            "name": "name",
-            "outputs": [
-                {
-                    "internalType": "string",
-                    "name": "",
-                    "type": "string"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                }
-            ],
-            "name": "ownerOf",
-            "outputs": [
-                {
-                    "internalType": "address",
-                    "name": "",
-                    "type": "address"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "spender",
-                    "type": "address"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "deadline",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint8",
-                    "name": "v",
-                    "type": "uint8"
-                },
-                {
-                    "internalType": "bytes32",
-                    "name": "r",
-                    "type": "bytes32"
-                },
-                {
-                    "internalType": "bytes32",
-                    "name": "s",
-                    "type": "bytes32"
-                }
-            ],
-            "name": "permit",
-            "outputs": [],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                }
-            ],
-            "name": "positions",
-            "outputs": [
-                {
-                    "internalType": "uint96",
-                    "name": "nonce",
-                    "type": "uint96"
-                },
-                {
-                    "internalType": "address",
-                    "name": "operator",
-                    "type": "address"
-                },
-                {
-                    "internalType": "address",
-                    "name": "token0",
-                    "type": "address"
-                },
-                {
-                    "internalType": "address",
-                    "name": "token1",
-                    "type": "address"
-                },
-                {
-                    "internalType": "uint24",
-                    "name": "fee",
-                    "type": "uint24"
-                },
-                {
-                    "internalType": "int24",
-                    "name": "tickLower",
-                    "type": "int24"
-                },
-                {
-                    "internalType": "int24",
-                    "name": "tickUpper",
-                    "type": "int24"
-                },
-                {
-                    "internalType": "uint128",
-                    "name": "liquidity",
-                    "type": "uint128"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "feeGrowthInside0LastX128",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "feeGrowthInside1LastX128",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint128",
-                    "name": "tokensOwed0",
-                    "type": "uint128"
-                },
-                {
-                    "internalType": "uint128",
-                    "name": "tokensOwed1",
-                    "type": "uint128"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [],
-            "name": "refundETH",
-            "outputs": [],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "from",
-                    "type": "address"
-                },
-                {
-                    "internalType": "address",
-                    "name": "to",
-                    "type": "address"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                }
-            ],
-            "name": "safeTransferFrom",
-            "outputs": [],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "from",
-                    "type": "address"
-                },
-                {
-                    "internalType": "address",
-                    "name": "to",
-                    "type": "address"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "bytes",
-                    "name": "_data",
-                    "type": "bytes"
-                }
-            ],
-            "name": "safeTransferFrom",
-            "outputs": [],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "token",
-                    "type": "address"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "value",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "deadline",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint8",
-                    "name": "v",
-                    "type": "uint8"
-                },
-                {
-                    "internalType": "bytes32",
-                    "name": "r",
-                    "type": "bytes32"
-                },
-                {
-                    "internalType": "bytes32",
-                    "name": "s",
-                    "type": "bytes32"
-                }
-            ],
-            "name": "selfPermit",
-            "outputs": [],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "token",
-                    "type": "address"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "nonce",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "expiry",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint8",
-                    "name": "v",
-                    "type": "uint8"
-                },
-                {
-                    "internalType": "bytes32",
-                    "name": "r",
-                    "type": "bytes32"
-                },
-                {
-                    "internalType": "bytes32",
-                    "name": "s",
-                    "type": "bytes32"
-                }
-            ],
-            "name": "selfPermitAllowed",
-            "outputs": [],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "token",
-                    "type": "address"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "nonce",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "expiry",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint8",
-                    "name": "v",
-                    "type": "uint8"
-                },
-                {
-                    "internalType": "bytes32",
-                    "name": "r",
-                    "type": "bytes32"
-                },
-                {
-                    "internalType": "bytes32",
-                    "name": "s",
-                    "type": "bytes32"
-                }
-            ],
-            "name": "selfPermitAllowedIfNecessary",
-            "outputs": [],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "token",
-                    "type": "address"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "value",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "deadline",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint8",
-                    "name": "v",
-                    "type": "uint8"
-                },
-                {
-                    "internalType": "bytes32",
-                    "name": "r",
-                    "type": "bytes32"
-                },
-                {
-                    "internalType": "bytes32",
-                    "name": "s",
-                    "type": "bytes32"
-                }
-            ],
-            "name": "selfPermitIfNecessary",
-            "outputs": [],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "operator",
-                    "type": "address"
-                },
-                {
-                    "internalType": "bool",
-                    "name": "approved",
-                    "type": "bool"
-                }
-            ],
-            "name": "setApprovalForAll",
-            "outputs": [],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "bytes4",
-                    "name": "interfaceId",
-                    "type": "bytes4"
-                }
-            ],
-            "name": "supportsInterface",
-            "outputs": [
-                {
-                    "internalType": "bool",
-                    "name": "",
-                    "type": "bool"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "token",
-                    "type": "address"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "amountMinimum",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "address",
-                    "name": "recipient",
-                    "type": "address"
-                }
-            ],
-            "name": "sweepToken",
-            "outputs": [],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [],
-            "name": "symbol",
-            "outputs": [
-                {
-                    "internalType": "string",
-                    "name": "",
-                    "type": "string"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "index",
-                    "type": "uint256"
-                }
-            ],
-            "name": "tokenByIndex",
-            "outputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "",
-                    "type": "uint256"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "owner",
-                    "type": "address"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "index",
-                    "type": "uint256"
-                }
-            ],
-            "name": "tokenOfOwnerByIndex",
-            "outputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "",
-                    "type": "uint256"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                }
-            ],
-            "name": "tokenURI",
-            "outputs": [
-                {
-                    "internalType": "string",
-                    "name": "",
-                    "type": "string"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [],
-            "name": "totalSupply",
-            "outputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "",
-                    "type": "uint256"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "from",
-                    "type": "address"
-                },
-                {
-                    "internalType": "address",
-                    "name": "to",
-                    "type": "address"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "tokenId",
-                    "type": "uint256"
-                }
-            ],
-            "name": "transferFrom",
-            "outputs": [],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "amount0Owed",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "amount1Owed",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "bytes",
-                    "name": "data",
-                    "type": "bytes"
-                }
-            ],
-            "name": "uniswapV3MintCallback",
-            "outputs": [],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "amountMinimum",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "address",
-                    "name": "recipient",
-                    "type": "address"
-                }
-            ],
-            "name": "unwrapWETH9",
-            "outputs": [],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "stateMutability": "payable",
-            "type": "receive"
-        }
-    ],
-    "bytecode": "",
-    "deployedBytecode": "",
-    "linkReferences": {},
-    "deployedLinkReferences": {}
-}

contracts/ServiceRegistryTokenUtility.json

@@ -1,926 +0,0 @@
-{
-  "_format": "hh-sol-artifact-1",
-  "contractName": "ServiceRegistryTokenUtility",
-  "sourceName": "contracts/ServiceRegistryTokenUtility.sol",
-  "abi": [
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "_serviceRegistry",
-          "type": "address"
-        }
-      ],
-      "stateMutability": "nonpayable",
-      "type": "constructor"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "operator",
-          "type": "address"
-        }
-      ],
-      "name": "AgentInstanceRegistered",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "AgentInstancesSlotsFilled",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "agentId",
-          "type": "uint256"
-        }
-      ],
-      "name": "AgentNotFound",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "agentId",
-          "type": "uint256"
-        },
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "AgentNotInService",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "componentId",
-          "type": "uint256"
-        }
-      ],
-      "name": "ComponentNotFound",
-      "type": "error"
-    },
-    {
-      "inputs": [],
-      "name": "HashExists",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "sent",
-          "type": "uint256"
-        },
-        {
-          "internalType": "uint256",
-          "name": "expected",
-          "type": "uint256"
-        },
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "IncorrectAgentBondingValue",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "sent",
-          "type": "uint256"
-        },
-        {
-          "internalType": "uint256",
-          "name": "expected",
-          "type": "uint256"
-        },
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "IncorrectRegistrationDepositValue",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "sender",
-          "type": "address"
-        },
-        {
-          "internalType": "address",
-          "name": "manager",
-          "type": "address"
-        }
-      ],
-      "name": "ManagerOnly",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "provided",
-          "type": "address"
-        },
-        {
-          "internalType": "address",
-          "name": "expected",
-          "type": "address"
-        },
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "OnlyOwnServiceMultisig",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "operator",
-          "type": "address"
-        },
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "OperatorHasNoInstances",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "provided",
-          "type": "uint256"
-        },
-        {
-          "internalType": "uint256",
-          "name": "max",
-          "type": "uint256"
-        }
-      ],
-      "name": "Overflow",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "sender",
-          "type": "address"
-        },
-        {
-          "internalType": "address",
-          "name": "owner",
-          "type": "address"
-        }
-      ],
-      "name": "OwnerOnly",
-      "type": "error"
-    },
-    {
-      "inputs": [],
-      "name": "Paused",
-      "type": "error"
-    },
-    {
-      "inputs": [],
-      "name": "ReentrancyGuard",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "ServiceMustBeInactive",
-      "type": "error"
-    },
-    {
-      "inputs": [
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-          "internalType": "address",
-          "name": "token",
-          "type": "address"
-        }
-      ],
-      "name": "TokenRejected",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "token",
-          "type": "address"
-        },
-        {
-          "internalType": "address",
-          "name": "from",
-          "type": "address"
-        },
-        {
-          "internalType": "address",
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-          "type": "address"
-        },
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-          "internalType": "uint256",
-          "name": "value",
-          "type": "uint256"
-        }
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-    },
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-      "inputs": [
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-          "internalType": "address",
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-          "type": "address"
-        }
-      ],
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-      "type": "error"
-    },
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-      "inputs": [
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-          "internalType": "uint256",
-          "name": "agentId",
-          "type": "uint256"
-        }
-      ],
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-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "numValues1",
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-        },
-        {
-          "internalType": "uint256",
-          "name": "numValues2",
-          "type": "uint256"
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-      "type": "error"
-    },
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-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "WrongOperator",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "state",
-          "type": "uint256"
-        },
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
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-        }
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-      "type": "error"
-    },
-    {
-      "inputs": [
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-          "internalType": "uint256",
-          "name": "currentThreshold",
-          "type": "uint256"
-        },
-        {
-          "internalType": "uint256",
-          "name": "minThreshold",
-          "type": "uint256"
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-        {
-          "internalType": "uint256",
-          "name": "maxThreshold",
-          "type": "uint256"
-        }
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-      "inputs": [],
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-    },
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-        }
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-      "anonymous": false,
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-        {
-          "indexed": true,
-          "internalType": "address",
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-          "type": "address"
-        }
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-          "indexed": false,
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-          "name": "amount",
-          "type": "uint256"
-        },
-        {
-          "indexed": true,
-          "internalType": "address",
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-          "type": "address"
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-        {
-          "indexed": true,
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-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
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-    {
-      "anonymous": false,
-      "inputs": [
-        {
-          "indexed": true,
-          "internalType": "address",
-          "name": "owner",
-          "type": "address"
-        }
-      ],
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-    },
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-      "anonymous": false,
-      "inputs": [
-        {
-          "indexed": true,
-          "internalType": "address",
-          "name": "account",
-          "type": "address"
-        },
-        {
-          "indexed": true,
-          "internalType": "address",
-          "name": "token",
-          "type": "address"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "amount",
-          "type": "uint256"
-        }
-      ],
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-    },
-    {
-      "anonymous": false,
-      "inputs": [
-        {
-          "indexed": true,
-          "internalType": "address",
-          "name": "drainer",
-          "type": "address"
-        },
-        {
-          "indexed": true,
-          "internalType": "address",
-          "name": "token",
-          "type": "address"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "amount",
-          "type": "uint256"
-        }
-      ],
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-      "type": "event"
-    },
-    {
-      "anonymous": false,
-      "inputs": [
-        {
-          "indexed": true,
-          "internalType": "address",
-          "name": "account",
-          "type": "address"
-        },
-        {
-          "indexed": true,
-          "internalType": "address",
-          "name": "token",
-          "type": "address"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "amount",
-          "type": "uint256"
-        }
-      ],
-      "name": "TokenRefund",
-      "type": "event"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "activateRegistrationTokenDeposit",
-      "outputs": [
-        {
-          "internalType": "bool",
-          "name": "isTokenSecured",
-          "type": "bool"
-        }
-      ],
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-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "newDrainer",
-          "type": "address"
-        }
-      ],
-      "name": "changeDrainer",
-      "outputs": [],
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-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "newManager",
-          "type": "address"
-        }
-      ],
-      "name": "changeManager",
-      "outputs": [],
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-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "newOwner",
-          "type": "address"
-        }
-      ],
-      "name": "changeOwner",
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-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        },
-        {
-          "internalType": "address",
-          "name": "token",
-          "type": "address"
-        },
-        {
-          "internalType": "uint32[]",
-          "name": "agentIds",
-          "type": "uint32[]"
-        },
-        {
-          "internalType": "uint256[]",
-          "name": "bonds",
-          "type": "uint256[]"
-        }
-      ],
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-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "token",
-          "type": "address"
-        }
-      ],
-      "name": "drain",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "amount",
-          "type": "uint256"
-        }
-      ],
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-      "type": "function"
-    },
-    {
-      "inputs": [],
-      "name": "drainer",
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-        {
-          "internalType": "address",
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-          "type": "address"
-        }
-      ],
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-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        },
-        {
-          "internalType": "uint256",
-          "name": "agentId",
-          "type": "uint256"
-        }
-      ],
-      "name": "getAgentBond",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "bond",
-          "type": "uint256"
-        }
-      ],
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-      "type": "function"
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-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "operator",
-          "type": "address"
-        },
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "getOperatorBalance",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "balance",
-          "type": "uint256"
-        }
-      ],
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-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "isTokenSecuredService",
-      "outputs": [
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-          "internalType": "bool",
-          "name": "",
-          "type": "bool"
-        }
-      ],
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-      "type": "function"
-    },
-    {
-      "inputs": [],
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-        {
-          "internalType": "address",
-          "name": "",
-          "type": "address"
-        }
-      ],
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-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "",
-          "type": "uint256"
-        }
-      ],
-      "name": "mapOperatorAndServiceIdOperatorBalances",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "",
-          "type": "uint256"
-        }
-      ],
-      "name": "mapServiceAndAgentIdAgentBond",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "",
-          "type": "uint256"
-        }
-      ],
-      "name": "mapServiceIdTokenDeposit",
-      "outputs": [
-        {
-          "internalType": "address",
-          "name": "token",
-          "type": "address"
-        },
-        {
-          "internalType": "uint96",
-          "name": "securityDeposit",
-          "type": "uint96"
-        }
-      ],
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-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "",
-          "type": "address"
-        }
-      ],
-      "name": "mapSlashedFunds",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "",
-          "type": "uint256"
-        }
-      ],
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-      "type": "function"
-    },
-    {
-      "inputs": [],
-      "name": "owner",
-      "outputs": [
-        {
-          "internalType": "address",
-          "name": "",
-          "type": "address"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "operator",
-          "type": "address"
-        },
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        },
-        {
-          "internalType": "uint32[]",
-          "name": "agentIds",
-          "type": "uint32[]"
-        }
-      ],
-      "name": "registerAgentsTokenDeposit",
-      "outputs": [
-        {
-          "internalType": "bool",
-          "name": "isTokenSecured",
-          "type": "bool"
-        }
-      ],
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-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "resetServiceToken",
-      "outputs": [],
-      "stateMutability": "nonpayable",
-      "type": "function"
-    },
-    {
-      "inputs": [],
-      "name": "serviceRegistry",
-      "outputs": [
-        {
-          "internalType": "address",
-          "name": "",
-          "type": "address"
-        }
-      ],
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-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address[]",
-          "name": "agentInstances",
-          "type": "address[]"
-        },
-        {
-          "internalType": "uint256[]",
-          "name": "amounts",
-          "type": "uint256[]"
-        },
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "slash",
-      "outputs": [
-        {
-          "internalType": "bool",
-          "name": "success",
-          "type": "bool"
-        }
-      ],
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-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "terminateTokenRefund",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "securityRefund",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "nonpayable",
-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "operator",
-          "type": "address"
-        },
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "unbondTokenRefund",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "refund",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "nonpayable",
-      "type": "function"
-    }
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-  "linkReferences": {},
-  "deployedLinkReferences": {}
-}

contracts/StakingActivityChecker.json

@@ -1,87 +0,0 @@
-{
-  "_format": "hh-sol-artifact-1",
-  "contractName": "StakingActivityChecker",
-  "sourceName": "contracts/staking/StakingActivityChecker.sol",
-  "abi": [
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "_livenessRatio",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "nonpayable",
-      "type": "constructor"
-    },
-    {
-      "inputs": [],
-      "name": "ZeroValue",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "multisig",
-          "type": "address"
-        }
-      ],
-      "name": "getMultisigNonces",
-      "outputs": [
-        {
-          "internalType": "uint256[]",
-          "name": "nonces",
-          "type": "uint256[]"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256[]",
-          "name": "curNonces",
-          "type": "uint256[]"
-        },
-        {
-          "internalType": "uint256[]",
-          "name": "lastNonces",
-          "type": "uint256[]"
-        },
-        {
-          "internalType": "uint256",
-          "name": "ts",
-          "type": "uint256"
-        }
-      ],
-      "name": "isRatioPass",
-      "outputs": [
-        {
-          "internalType": "bool",
-          "name": "ratioPass",
-          "type": "bool"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [],
-      "name": "livenessRatio",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    }
-  ],
-  "bytecode": "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",
-  "linkReferences": {},
-  "deployedLinkReferences": {}
-}

contracts/StakingToken.json

@@ -1,1274 +0,0 @@
-{
-  "_format": "hh-sol-artifact-1",
-  "contractName": "ServiceStakingToken",
-  "sourceName": "contracts/staking/ServiceStakingToken.sol",
-  "abi": [
-    {
-      "inputs": [],
-      "name": "AlreadyInitialized",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "activityChecker",
-          "type": "address"
-        }
-      ],
-      "name": "ContractOnly",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "provided",
-          "type": "uint256"
-        },
-        {
-          "internalType": "uint256",
-          "name": "expected",
-          "type": "uint256"
-        }
-      ],
-      "name": "LowerThan",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "maxNumServices",
-          "type": "uint256"
-        }
-      ],
-      "name": "MaxNumServicesReached",
-      "type": "error"
-    },
-    {
-      "inputs": [],
-      "name": "NoRewardsAvailable",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        },
-        {
-          "internalType": "uint256",
-          "name": "tsProvided",
-          "type": "uint256"
-        },
-        {
-          "internalType": "uint256",
-          "name": "tsExpected",
-          "type": "uint256"
-        }
-      ],
-      "name": "NotEnoughTimeStaked",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "sender",
-          "type": "address"
-        },
-        {
-          "internalType": "address",
-          "name": "owner",
-          "type": "address"
-        }
-      ],
-      "name": "OwnerOnly",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "ServiceNotUnstaked",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "token",
-          "type": "address"
-        },
-        {
-          "internalType": "address",
-          "name": "from",
-          "type": "address"
-        },
-        {
-          "internalType": "address",
-          "name": "to",
-          "type": "address"
-        },
-        {
-          "internalType": "uint256",
-          "name": "value",
-          "type": "uint256"
-        }
-      ],
-      "name": "TokenTransferFailed",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "multisig",
-          "type": "address"
-        }
-      ],
-      "name": "UnauthorizedMultisig",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "provided",
-          "type": "uint256"
-        },
-        {
-          "internalType": "uint256",
-          "name": "expected",
-          "type": "uint256"
-        }
-      ],
-      "name": "ValueLowerThan",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "agentId",
-          "type": "uint256"
-        }
-      ],
-      "name": "WrongAgentId",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "WrongServiceConfiguration",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "state",
-          "type": "uint256"
-        },
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "WrongServiceState",
-      "type": "error"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "address",
-          "name": "expected",
-          "type": "address"
-        },
-        {
-          "internalType": "address",
-          "name": "provided",
-          "type": "address"
-        }
-      ],
-      "name": "WrongStakingToken",
-      "type": "error"
-    },
-    {
-      "inputs": [],
-      "name": "ZeroAddress",
-      "type": "error"
-    },
-    {
-      "inputs": [],
-      "name": "ZeroTokenAddress",
-      "type": "error"
-    },
-    {
-      "inputs": [],
-      "name": "ZeroValue",
-      "type": "error"
-    },
-    {
-      "anonymous": false,
-      "inputs": [
-        {
-          "indexed": true,
-          "internalType": "uint256",
-          "name": "epoch",
-          "type": "uint256"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "availableRewards",
-          "type": "uint256"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256[]",
-          "name": "serviceIds",
-          "type": "uint256[]"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256[]",
-          "name": "rewards",
-          "type": "uint256[]"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "epochLength",
-          "type": "uint256"
-        }
-      ],
-      "name": "Checkpoint",
-      "type": "event"
-    },
-    {
-      "anonymous": false,
-      "inputs": [
-        {
-          "indexed": true,
-          "internalType": "address",
-          "name": "sender",
-          "type": "address"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "amount",
-          "type": "uint256"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "balance",
-          "type": "uint256"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "availableRewards",
-          "type": "uint256"
-        }
-      ],
-      "name": "Deposit",
-      "type": "event"
-    },
-    {
-      "anonymous": false,
-      "inputs": [
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "epoch",
-          "type": "uint256"
-        },
-        {
-          "indexed": true,
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        },
-        {
-          "indexed": true,
-          "internalType": "address",
-          "name": "owner",
-          "type": "address"
-        },
-        {
-          "indexed": true,
-          "internalType": "address",
-          "name": "multisig",
-          "type": "address"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256[]",
-          "name": "nonces",
-          "type": "uint256[]"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "reward",
-          "type": "uint256"
-        }
-      ],
-      "name": "RewardClaimed",
-      "type": "event"
-    },
-    {
-      "anonymous": false,
-      "inputs": [
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "epoch",
-          "type": "uint256"
-        },
-        {
-          "indexed": true,
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "serviceInactivity",
-          "type": "uint256"
-        }
-      ],
-      "name": "ServiceInactivityWarning",
-      "type": "event"
-    },
-    {
-      "anonymous": false,
-      "inputs": [
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "epoch",
-          "type": "uint256"
-        },
-        {
-          "indexed": true,
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        },
-        {
-          "indexed": true,
-          "internalType": "address",
-          "name": "owner",
-          "type": "address"
-        },
-        {
-          "indexed": true,
-          "internalType": "address",
-          "name": "multisig",
-          "type": "address"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256[]",
-          "name": "nonces",
-          "type": "uint256[]"
-        }
-      ],
-      "name": "ServiceStaked",
-      "type": "event"
-    },
-    {
-      "anonymous": false,
-      "inputs": [
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "epoch",
-          "type": "uint256"
-        },
-        {
-          "indexed": true,
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        },
-        {
-          "indexed": true,
-          "internalType": "address",
-          "name": "owner",
-          "type": "address"
-        },
-        {
-          "indexed": true,
-          "internalType": "address",
-          "name": "multisig",
-          "type": "address"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256[]",
-          "name": "nonces",
-          "type": "uint256[]"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "reward",
-          "type": "uint256"
-        }
-      ],
-      "name": "ServiceUnstaked",
-      "type": "event"
-    },
-    {
-      "anonymous": false,
-      "inputs": [
-        {
-          "indexed": true,
-          "internalType": "uint256",
-          "name": "epoch",
-          "type": "uint256"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256[]",
-          "name": "serviceIds",
-          "type": "uint256[]"
-        },
-        {
-          "indexed": false,
-          "internalType": "address[]",
-          "name": "owners",
-          "type": "address[]"
-        },
-        {
-          "indexed": false,
-          "internalType": "address[]",
-          "name": "multisigs",
-          "type": "address[]"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256[]",
-          "name": "serviceInactivity",
-          "type": "uint256[]"
-        }
-      ],
-      "name": "ServicesEvicted",
-      "type": "event"
-    },
-    {
-      "anonymous": false,
-      "inputs": [
-        {
-          "indexed": true,
-          "internalType": "address",
-          "name": "to",
-          "type": "address"
-        },
-        {
-          "indexed": false,
-          "internalType": "uint256",
-          "name": "amount",
-          "type": "uint256"
-        }
-      ],
-      "name": "Withdraw",
-      "type": "event"
-    },
-    {
-      "inputs": [],
-      "name": "VERSION",
-      "outputs": [
-        {
-          "internalType": "string",
-          "name": "",
-          "type": "string"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [],
-      "name": "activityChecker",
-      "outputs": [
-        {
-          "internalType": "address",
-          "name": "",
-          "type": "address"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "",
-          "type": "uint256"
-        }
-      ],
-      "name": "agentIds",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [],
-      "name": "availableRewards",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [],
-      "name": "balance",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "calculateStakingLastReward",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "reward",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "calculateStakingReward",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "reward",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [],
-      "name": "checkpoint",
-      "outputs": [
-        {
-          "internalType": "uint256[]",
-          "name": "",
-          "type": "uint256[]"
-        },
-        {
-          "internalType": "uint256[][]",
-          "name": "",
-          "type": "uint256[][]"
-        },
-        {
-          "internalType": "uint256[]",
-          "name": "",
-          "type": "uint256[]"
-        },
-        {
-          "internalType": "uint256[]",
-          "name": "",
-          "type": "uint256[]"
-        },
-        {
-          "internalType": "uint256[]",
-          "name": "evictServiceIds",
-          "type": "uint256[]"
-        }
-      ],
-      "stateMutability": "nonpayable",
-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "checkpointAndClaim",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "nonpayable",
-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "claim",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "nonpayable",
-      "type": "function"
-    },
-    {
-      "inputs": [],
-      "name": "configHash",
-      "outputs": [
-        {
-          "internalType": "bytes32",
-          "name": "",
-          "type": "bytes32"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "amount",
-          "type": "uint256"
-        }
-      ],
-      "name": "deposit",
-      "outputs": [],
-      "stateMutability": "nonpayable",
-      "type": "function"
-    },
-    {
-      "inputs": [],
-      "name": "emissionsAmount",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [],
-      "name": "epochCounter",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [],
-      "name": "getAgentIds",
-      "outputs": [
-        {
-          "internalType": "uint256[]",
-          "name": "",
-          "type": "uint256[]"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [],
-      "name": "getNextRewardCheckpointTimestamp",
-      "outputs": [
-        {
-          "internalType": "uint256",
-          "name": "tsNext",
-          "type": "uint256"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [],
-      "name": "getServiceIds",
-      "outputs": [
-        {
-          "internalType": "uint256[]",
-          "name": "",
-          "type": "uint256[]"
-        }
-      ],
-      "stateMutability": "view",
-      "type": "function"
-    },
-    {
-      "inputs": [
-        {
-          "internalType": "uint256",
-          "name": "serviceId",
-          "type": "uint256"
-        }
-      ],
-      "name": "getServiceInfo",
-      "outputs": [
-        {
-          "components": [
-            {
-              "internalType": "address",
-              "name": "multisig",
-              "type": "address"
-            },
-            {
-              "internalType": "address",
-              "name": "owner",
-              "type": "address"
-            },
-            {
-              "internalType": "uint256[]",
-              "name": "nonces",
-              "type": "uint256[]"
-            },
-            {
-              "internalType": "uint256",
-              "name": "tsStart",
-              "type": "uint256"
-            },
-            {
-              "internalType": "uint256",
-              "name": "reward",
-              "type": "uint256"
-            },
-            {
-              "internalType": "uint256",
-              "name": "inactivity",
-              "type": "uint256"
-            }
-          ],
-          "internalType": "struct ServiceInfo",
-          "name": "sInfo",
-          "type": "tuple"
-        }
-      ],
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-  "linkReferences": {},
-  "deployedLinkReferences": {}
-}

contracts/Vault.json

@@ -1,1186 +0,0 @@
-{
-    "contractName": "",
-    "abi": [
-        {
-            "inputs": [
-                {
-                    "internalType": "contract IAuthorizer",
-                    "name": "authorizer",
-                    "type": "address"
-                },
-                {
-                    "internalType": "contract IWETH",
-                    "name": "weth",
-                    "type": "address"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "pauseWindowDuration",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "bufferPeriodDuration",
-                    "type": "uint256"
-                }
-            ],
-            "stateMutability": "nonpayable",
-            "type": "constructor"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "contract IAuthorizer",
-                    "name": "newAuthorizer",
-                    "type": "address"
-                }
-            ],
-            "name": "AuthorizerChanged",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "contract IERC20",
-                    "name": "token",
-                    "type": "address"
-                },
-                {
-                    "indexed": true,
-                    "internalType": "address",
-                    "name": "sender",
-                    "type": "address"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "address",
-                    "name": "recipient",
-                    "type": "address"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "uint256",
-                    "name": "amount",
-                    "type": "uint256"
-                }
-            ],
-            "name": "ExternalBalanceTransfer",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "contract IFlashLoanRecipient",
-                    "name": "recipient",
-                    "type": "address"
-                },
-                {
-                    "indexed": true,
-                    "internalType": "contract IERC20",
-                    "name": "token",
-                    "type": "address"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "uint256",
-                    "name": "amount",
-                    "type": "uint256"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "uint256",
-                    "name": "feeAmount",
-                    "type": "uint256"
-                }
-            ],
-            "name": "FlashLoan",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "address",
-                    "name": "user",
-                    "type": "address"
-                },
-                {
-                    "indexed": true,
-                    "internalType": "contract IERC20",
-                    "name": "token",
-                    "type": "address"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "int256",
-                    "name": "delta",
-                    "type": "int256"
-                }
-            ],
-            "name": "InternalBalanceChanged",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": false,
-                    "internalType": "bool",
-                    "name": "paused",
-                    "type": "bool"
-                }
-            ],
-            "name": "PausedStateChanged",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "bytes32",
-                    "name": "poolId",
-                    "type": "bytes32"
-                },
-                {
-                    "indexed": true,
-                    "internalType": "address",
-                    "name": "liquidityProvider",
-                    "type": "address"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "contract IERC20[]",
-                    "name": "tokens",
-                    "type": "address[]"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "int256[]",
-                    "name": "deltas",
-                    "type": "int256[]"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "uint256[]",
-                    "name": "protocolFeeAmounts",
-                    "type": "uint256[]"
-                }
-            ],
-            "name": "PoolBalanceChanged",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "bytes32",
-                    "name": "poolId",
-                    "type": "bytes32"
-                },
-                {
-                    "indexed": true,
-                    "internalType": "address",
-                    "name": "assetManager",
-                    "type": "address"
-                },
-                {
-                    "indexed": true,
-                    "internalType": "contract IERC20",
-                    "name": "token",
-                    "type": "address"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "int256",
-                    "name": "cashDelta",
-                    "type": "int256"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "int256",
-                    "name": "managedDelta",
-                    "type": "int256"
-                }
-            ],
-            "name": "PoolBalanceManaged",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "bytes32",
-                    "name": "poolId",
-                    "type": "bytes32"
-                },
-                {
-                    "indexed": true,
-                    "internalType": "address",
-                    "name": "poolAddress",
-                    "type": "address"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "enum IVault.PoolSpecialization",
-                    "name": "specialization",
-                    "type": "uint8"
-                }
-            ],
-            "name": "PoolRegistered",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "address",
-                    "name": "relayer",
-                    "type": "address"
-                },
-                {
-                    "indexed": true,
-                    "internalType": "address",
-                    "name": "sender",
-                    "type": "address"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "bool",
-                    "name": "approved",
-                    "type": "bool"
-                }
-            ],
-            "name": "RelayerApprovalChanged",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "bytes32",
-                    "name": "poolId",
-                    "type": "bytes32"
-                },
-                {
-                    "indexed": true,
-                    "internalType": "contract IERC20",
-                    "name": "tokenIn",
-                    "type": "address"
-                },
-                {
-                    "indexed": true,
-                    "internalType": "contract IERC20",
-                    "name": "tokenOut",
-                    "type": "address"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "uint256",
-                    "name": "amountIn",
-                    "type": "uint256"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "uint256",
-                    "name": "amountOut",
-                    "type": "uint256"
-                }
-            ],
-            "name": "Swap",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "bytes32",
-                    "name": "poolId",
-                    "type": "bytes32"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "contract IERC20[]",
-                    "name": "tokens",
-                    "type": "address[]"
-                }
-            ],
-            "name": "TokensDeregistered",
-            "type": "event"
-        },
-        {
-            "anonymous": false,
-            "inputs": [
-                {
-                    "indexed": true,
-                    "internalType": "bytes32",
-                    "name": "poolId",
-                    "type": "bytes32"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "contract IERC20[]",
-                    "name": "tokens",
-                    "type": "address[]"
-                },
-                {
-                    "indexed": false,
-                    "internalType": "address[]",
-                    "name": "assetManagers",
-                    "type": "address[]"
-                }
-            ],
-            "name": "TokensRegistered",
-            "type": "event"
-        },
-        {
-            "inputs": [],
-            "name": "WETH",
-            "outputs": [
-                {
-                    "internalType": "contract IWETH",
-                    "name": "",
-                    "type": "address"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "enum IVault.SwapKind",
-                    "name": "kind",
-                    "type": "uint8"
-                },
-                {
-                    "components": [
-                        {
-                            "internalType": "bytes32",
-                            "name": "poolId",
-                            "type": "bytes32"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "assetInIndex",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "assetOutIndex",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "amount",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "bytes",
-                            "name": "userData",
-                            "type": "bytes"
-                        }
-                    ],
-                    "internalType": "struct IVault.BatchSwapStep[]",
-                    "name": "swaps",
-                    "type": "tuple[]"
-                },
-                {
-                    "internalType": "contract IAsset[]",
-                    "name": "assets",
-                    "type": "address[]"
-                },
-                {
-                    "components": [
-                        {
-                            "internalType": "address",
-                            "name": "sender",
-                            "type": "address"
-                        },
-                        {
-                            "internalType": "bool",
-                            "name": "fromInternalBalance",
-                            "type": "bool"
-                        },
-                        {
-                            "internalType": "address payable",
-                            "name": "recipient",
-                            "type": "address"
-                        },
-                        {
-                            "internalType": "bool",
-                            "name": "toInternalBalance",
-                            "type": "bool"
-                        }
-                    ],
-                    "internalType": "struct IVault.FundManagement",
-                    "name": "funds",
-                    "type": "tuple"
-                },
-                {
-                    "internalType": "int256[]",
-                    "name": "limits",
-                    "type": "int256[]"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "deadline",
-                    "type": "uint256"
-                }
-            ],
-            "name": "batchSwap",
-            "outputs": [
-                {
-                    "internalType": "int256[]",
-                    "name": "assetDeltas",
-                    "type": "int256[]"
-                }
-            ],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "bytes32",
-                    "name": "poolId",
-                    "type": "bytes32"
-                },
-                {
-                    "internalType": "contract IERC20[]",
-                    "name": "tokens",
-                    "type": "address[]"
-                }
-            ],
-            "name": "deregisterTokens",
-            "outputs": [],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "bytes32",
-                    "name": "poolId",
-                    "type": "bytes32"
-                },
-                {
-                    "internalType": "address",
-                    "name": "sender",
-                    "type": "address"
-                },
-                {
-                    "internalType": "address payable",
-                    "name": "recipient",
-                    "type": "address"
-                },
-                {
-                    "components": [
-                        {
-                            "internalType": "contract IAsset[]",
-                            "name": "assets",
-                            "type": "address[]"
-                        },
-                        {
-                            "internalType": "uint256[]",
-                            "name": "minAmountsOut",
-                            "type": "uint256[]"
-                        },
-                        {
-                            "internalType": "bytes",
-                            "name": "userData",
-                            "type": "bytes"
-                        },
-                        {
-                            "internalType": "bool",
-                            "name": "toInternalBalance",
-                            "type": "bool"
-                        }
-                    ],
-                    "internalType": "struct IVault.ExitPoolRequest",
-                    "name": "request",
-                    "type": "tuple"
-                }
-            ],
-            "name": "exitPool",
-            "outputs": [],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "contract IFlashLoanRecipient",
-                    "name": "recipient",
-                    "type": "address"
-                },
-                {
-                    "internalType": "contract IERC20[]",
-                    "name": "tokens",
-                    "type": "address[]"
-                },
-                {
-                    "internalType": "uint256[]",
-                    "name": "amounts",
-                    "type": "uint256[]"
-                },
-                {
-                    "internalType": "bytes",
-                    "name": "userData",
-                    "type": "bytes"
-                }
-            ],
-            "name": "flashLoan",
-            "outputs": [],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "bytes4",
-                    "name": "selector",
-                    "type": "bytes4"
-                }
-            ],
-            "name": "getActionId",
-            "outputs": [
-                {
-                    "internalType": "bytes32",
-                    "name": "",
-                    "type": "bytes32"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [],
-            "name": "getAuthorizer",
-            "outputs": [
-                {
-                    "internalType": "contract IAuthorizer",
-                    "name": "",
-                    "type": "address"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [],
-            "name": "getDomainSeparator",
-            "outputs": [
-                {
-                    "internalType": "bytes32",
-                    "name": "",
-                    "type": "bytes32"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "user",
-                    "type": "address"
-                },
-                {
-                    "internalType": "contract IERC20[]",
-                    "name": "tokens",
-                    "type": "address[]"
-                }
-            ],
-            "name": "getInternalBalance",
-            "outputs": [
-                {
-                    "internalType": "uint256[]",
-                    "name": "balances",
-                    "type": "uint256[]"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "user",
-                    "type": "address"
-                }
-            ],
-            "name": "getNextNonce",
-            "outputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "",
-                    "type": "uint256"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [],
-            "name": "getPausedState",
-            "outputs": [
-                {
-                    "internalType": "bool",
-                    "name": "paused",
-                    "type": "bool"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "pauseWindowEndTime",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "bufferPeriodEndTime",
-                    "type": "uint256"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "bytes32",
-                    "name": "poolId",
-                    "type": "bytes32"
-                }
-            ],
-            "name": "getPool",
-            "outputs": [
-                {
-                    "internalType": "address",
-                    "name": "",
-                    "type": "address"
-                },
-                {
-                    "internalType": "enum IVault.PoolSpecialization",
-                    "name": "",
-                    "type": "uint8"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "bytes32",
-                    "name": "poolId",
-                    "type": "bytes32"
-                },
-                {
-                    "internalType": "contract IERC20",
-                    "name": "token",
-                    "type": "address"
-                }
-            ],
-            "name": "getPoolTokenInfo",
-            "outputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "cash",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "managed",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "lastChangeBlock",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "address",
-                    "name": "assetManager",
-                    "type": "address"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "bytes32",
-                    "name": "poolId",
-                    "type": "bytes32"
-                }
-            ],
-            "name": "getPoolTokens",
-            "outputs": [
-                {
-                    "internalType": "contract IERC20[]",
-                    "name": "tokens",
-                    "type": "address[]"
-                },
-                {
-                    "internalType": "uint256[]",
-                    "name": "balances",
-                    "type": "uint256[]"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "lastChangeBlock",
-                    "type": "uint256"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [],
-            "name": "getProtocolFeesCollector",
-            "outputs": [
-                {
-                    "internalType": "contract ProtocolFeesCollector",
-                    "name": "",
-                    "type": "address"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "user",
-                    "type": "address"
-                },
-                {
-                    "internalType": "address",
-                    "name": "relayer",
-                    "type": "address"
-                }
-            ],
-            "name": "hasApprovedRelayer",
-            "outputs": [
-                {
-                    "internalType": "bool",
-                    "name": "",
-                    "type": "bool"
-                }
-            ],
-            "stateMutability": "view",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "bytes32",
-                    "name": "poolId",
-                    "type": "bytes32"
-                },
-                {
-                    "internalType": "address",
-                    "name": "sender",
-                    "type": "address"
-                },
-                {
-                    "internalType": "address",
-                    "name": "recipient",
-                    "type": "address"
-                },
-                {
-                    "components": [
-                        {
-                            "internalType": "contract IAsset[]",
-                            "name": "assets",
-                            "type": "address[]"
-                        },
-                        {
-                            "internalType": "uint256[]",
-                            "name": "maxAmountsIn",
-                            "type": "uint256[]"
-                        },
-                        {
-                            "internalType": "bytes",
-                            "name": "userData",
-                            "type": "bytes"
-                        },
-                        {
-                            "internalType": "bool",
-                            "name": "fromInternalBalance",
-                            "type": "bool"
-                        }
-                    ],
-                    "internalType": "struct IVault.JoinPoolRequest",
-                    "name": "request",
-                    "type": "tuple"
-                }
-            ],
-            "name": "joinPool",
-            "outputs": [],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "components": [
-                        {
-                            "internalType": "enum IVault.PoolBalanceOpKind",
-                            "name": "kind",
-                            "type": "uint8"
-                        },
-                        {
-                            "internalType": "bytes32",
-                            "name": "poolId",
-                            "type": "bytes32"
-                        },
-                        {
-                            "internalType": "contract IERC20",
-                            "name": "token",
-                            "type": "address"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "amount",
-                            "type": "uint256"
-                        }
-                    ],
-                    "internalType": "struct IVault.PoolBalanceOp[]",
-                    "name": "ops",
-                    "type": "tuple[]"
-                }
-            ],
-            "name": "managePoolBalance",
-            "outputs": [],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "components": [
-                        {
-                            "internalType": "enum IVault.UserBalanceOpKind",
-                            "name": "kind",
-                            "type": "uint8"
-                        },
-                        {
-                            "internalType": "contract IAsset",
-                            "name": "asset",
-                            "type": "address"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "amount",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "address",
-                            "name": "sender",
-                            "type": "address"
-                        },
-                        {
-                            "internalType": "address payable",
-                            "name": "recipient",
-                            "type": "address"
-                        }
-                    ],
-                    "internalType": "struct IVault.UserBalanceOp[]",
-                    "name": "ops",
-                    "type": "tuple[]"
-                }
-            ],
-            "name": "manageUserBalance",
-            "outputs": [],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "enum IVault.SwapKind",
-                    "name": "kind",
-                    "type": "uint8"
-                },
-                {
-                    "components": [
-                        {
-                            "internalType": "bytes32",
-                            "name": "poolId",
-                            "type": "bytes32"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "assetInIndex",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "assetOutIndex",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "amount",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "bytes",
-                            "name": "userData",
-                            "type": "bytes"
-                        }
-                    ],
-                    "internalType": "struct IVault.BatchSwapStep[]",
-                    "name": "swaps",
-                    "type": "tuple[]"
-                },
-                {
-                    "internalType": "contract IAsset[]",
-                    "name": "assets",
-                    "type": "address[]"
-                },
-                {
-                    "components": [
-                        {
-                            "internalType": "address",
-                            "name": "sender",
-                            "type": "address"
-                        },
-                        {
-                            "internalType": "bool",
-                            "name": "fromInternalBalance",
-                            "type": "bool"
-                        },
-                        {
-                            "internalType": "address payable",
-                            "name": "recipient",
-                            "type": "address"
-                        },
-                        {
-                            "internalType": "bool",
-                            "name": "toInternalBalance",
-                            "type": "bool"
-                        }
-                    ],
-                    "internalType": "struct IVault.FundManagement",
-                    "name": "funds",
-                    "type": "tuple"
-                }
-            ],
-            "name": "queryBatchSwap",
-            "outputs": [
-                {
-                    "internalType": "int256[]",
-                    "name": "",
-                    "type": "int256[]"
-                }
-            ],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "enum IVault.PoolSpecialization",
-                    "name": "specialization",
-                    "type": "uint8"
-                }
-            ],
-            "name": "registerPool",
-            "outputs": [
-                {
-                    "internalType": "bytes32",
-                    "name": "",
-                    "type": "bytes32"
-                }
-            ],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "bytes32",
-                    "name": "poolId",
-                    "type": "bytes32"
-                },
-                {
-                    "internalType": "contract IERC20[]",
-                    "name": "tokens",
-                    "type": "address[]"
-                },
-                {
-                    "internalType": "address[]",
-                    "name": "assetManagers",
-                    "type": "address[]"
-                }
-            ],
-            "name": "registerTokens",
-            "outputs": [],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "contract IAuthorizer",
-                    "name": "newAuthorizer",
-                    "type": "address"
-                }
-            ],
-            "name": "setAuthorizer",
-            "outputs": [],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "bool",
-                    "name": "paused",
-                    "type": "bool"
-                }
-            ],
-            "name": "setPaused",
-            "outputs": [],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "internalType": "address",
-                    "name": "sender",
-                    "type": "address"
-                },
-                {
-                    "internalType": "address",
-                    "name": "relayer",
-                    "type": "address"
-                },
-                {
-                    "internalType": "bool",
-                    "name": "approved",
-                    "type": "bool"
-                }
-            ],
-            "name": "setRelayerApproval",
-            "outputs": [],
-            "stateMutability": "nonpayable",
-            "type": "function"
-        },
-        {
-            "inputs": [
-                {
-                    "components": [
-                        {
-                            "internalType": "bytes32",
-                            "name": "poolId",
-                            "type": "bytes32"
-                        },
-                        {
-                            "internalType": "enum IVault.SwapKind",
-                            "name": "kind",
-                            "type": "uint8"
-                        },
-                        {
-                            "internalType": "contract IAsset",
-                            "name": "assetIn",
-                            "type": "address"
-                        },
-                        {
-                            "internalType": "contract IAsset",
-                            "name": "assetOut",
-                            "type": "address"
-                        },
-                        {
-                            "internalType": "uint256",
-                            "name": "amount",
-                            "type": "uint256"
-                        },
-                        {
-                            "internalType": "bytes",
-                            "name": "userData",
-                            "type": "bytes"
-                        }
-                    ],
-                    "internalType": "struct IVault.SingleSwap",
-                    "name": "singleSwap",
-                    "type": "tuple"
-                },
-                {
-                    "components": [
-                        {
-                            "internalType": "address",
-                            "name": "sender",
-                            "type": "address"
-                        },
-                        {
-                            "internalType": "bool",
-                            "name": "fromInternalBalance",
-                            "type": "bool"
-                        },
-                        {
-                            "internalType": "address payable",
-                            "name": "recipient",
-                            "type": "address"
-                        },
-                        {
-                            "internalType": "bool",
-                            "name": "toInternalBalance",
-                            "type": "bool"
-                        }
-                    ],
-                    "internalType": "struct IVault.FundManagement",
-                    "name": "funds",
-                    "type": "tuple"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "limit",
-                    "type": "uint256"
-                },
-                {
-                    "internalType": "uint256",
-                    "name": "deadline",
-                    "type": "uint256"
-                }
-            ],
-            "name": "swap",
-            "outputs": [
-                {
-                    "internalType": "uint256",
-                    "name": "amountCalculated",
-                    "type": "uint256"
-                }
-            ],
-            "stateMutability": "payable",
-            "type": "function"
-        },
-        {
-            "stateMutability": "payable",
-            "type": "receive"
-        }
-    ],
-    "bytecode": "",
-    "deployedBytecode": "",
-    "linkReferences": {},
-    "deployedLinkReferences": {}
-}

contracts/service_registry_abi.json

@@ -1 +0,0 @@
-[{"inputs":[{"internalType":"string","name":"_name","type":"string"},{"internalType":"string","name":"_symbol","type":"string"},{"internalType":"string","name":"_baseURI","type":"string"}],"stateMutability":"nonpayable","type":"constructor"},{"inputs":[{"internalType":"address","name":"operator","type":"address"}],"name":"AgentInstanceRegistered","type":"error"},{"inputs":[{"internalType":"uint256","name":"serviceId","type":"uint256"}],"name":"AgentInstancesSlotsFilled","type":"error"},{"inputs":[{"internalType":"uint256","name":"agentId","type":"uint256"}],"name":"AgentNotFound","type":"error"},{"inputs":[{"internalType":"uint256","name":"agentId","type":"uint256"},{"internalType":"uint256","name":"serviceId","type":"uint256"}],"name":"AgentNotInService","type":"error"},{"inputs":[{"internalType":"uint256","name":"componentId","type":"uint256"}],"name":"ComponentNotFound","type":"error"},{"inputs":[],"name":"HashExists","type":"error"},{"inputs":[{"internalType":"uint256","name":"sent","type":"uint256"},{"internalType":"uint256","name":"expected","type":"uint256"},{"internalType":"uint256","name":"serviceId","type":"uint256"}],"name":"IncorrectAgentBondingValue","type":"error"},{"inputs":[{"internalType":"uint256","name":"sent","type":"uint256"},{"internalType":"uint256","name":"expected","type":"uint256"},{"internalType":"uint256","name":"serviceId","type":"uint256"}],"name":"IncorrectRegistrationDepositValue","type":"error"},{"inputs":[{"internalType":"address","name":"sender","type":"address"},{"internalType":"address","name":"manager","type":"address"}],"name":"ManagerOnly","type":"error"},{"inputs":[{"internalType":"address","name":"provided","type":"address"},{"internalType":"address","name":"expected","type":"address"},{"internalType":"uint256","name":"serviceId","type":"uint256"}],"name":"OnlyOwnServiceMultisig","type":"error"},{"inputs":[{"internalType":"address","name":"operator","type":"address"},{"internalType":"uint256","name":"serviceId","type":"uint256"}],"name":"OperatorHasNoInstances","type":"error"},{"inputs":[{"internalType":"uint256","name":"provided","type":"uint256"},{"internalType":"uint256","name":"max","type":"uint256"}],"name":"Overflow","type":"error"},{"inputs":[{"internalType":"address","name":"sender","type":"address"},{"internalType":"address","name":"owner","type":"address"}],"name":"OwnerOnly","type":"error"},{"inputs":[],"name":"Paused","type":"error"},{"inputs":[],"name":"ReentrancyGuard","type":"error"},{"inputs":[{"internalType":"uint256","name":"serviceId","type":"uint256"}],"name":"ServiceMustBeInactive","type":"error"},{"inputs":[{"internalType":"address","name":"token","type":"address"},{"internalType":"address","name":"from","type":"address"},{"internalType":"address","name":"to","type":"address"},{"internalType":"uint256","name":"value","type":"uint256"}],"name":"TransferFailed","type":"error"},{"inputs":[{"internalType":"address","name":"multisig","type":"address"}],"name":"UnauthorizedMultisig","type":"error"},{"inputs":[{"internalType":"uint256","name":"agentId","type":"uint256"}],"name":"WrongAgentId","type":"error"},{"inputs":[{"internalType":"uint256","name":"numValues1","type":"uint256"},{"internalType":"uint256","name":"numValues2","type":"uint256"}],"name":"WrongArrayLength","type":"error"},{"inputs":[{"internalType":"uint256","name":"serviceId","type":"uint256"}],"name":"WrongOperator","type":"error"},{"inputs":[{"internalType":"uint256","name":"state","type":"uint256"},{"internalType":"uint256","name":"serviceId","type":"uint256"}],"name":"WrongServiceState","type":"error"},{"inputs":[{"internalType":"uint256","name":"currentThreshold","type":"uint256"},{"internalType":"uint256","name":"minThreshold","type":"uint256"},{"internalType":"uint256","name":"maxThreshold","type":"uint256"}],"name":"WrongThreshold","type":"error"},{"inputs":[],"name":"ZeroAddress","type":"error"},{"inputs":[],"name":"ZeroValue","type":"error"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"uint256","name":"serviceId","type":"uint256"}],"name":"ActivateRegistration","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"owner","type":"address"},{"indexed":true,"internalType":"address","name":"spender","type":"address"},{"indexed":true,"internalType":"uint256","name":"id","type":"uint256"}],"name":"Approval","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"owner","type":"address"},{"indexed":true,"internalType":"address","name":"operator","type":"address"},{"indexed":false,"internalType":"bool","name":"approved","type":"bool"}],"name":"ApprovalForAll","type":"event"},{"anonymous":false,"inputs":[{"indexed":false,"internalType":"string","name":"baseURI","type":"string"}],"name":"BaseURIChanged","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"uint256","name":"serviceId","type":"uint256"},{"indexed":true,"internalType":"address","name":"multisig","type":"ad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erviceOwner","type":"address"},{"internalType":"bytes32","name":"configHash","type":"bytes32"},{"internalType":"uint32[]","name":"agentIds","type":"uint32[]"},{"components":[{"internalType":"uint32","name":"slots","type":"uint32"},{"internalType":"uint96","name":"bond","type":"uint96"}],"internalType":"struct AgentParams[]","name":"agentParams","type":"tuple[]"},{"internalType":"uint32","name":"threshold","type":"uint32"}],"name":"create","outputs":[{"internalType":"uint256","name":"serviceId","type":"uint256"}],"stateMutability":"nonpayable","type":"function"},{"inputs":[{"internalType":"address","name":"serviceOwner","type":"address"},{"internalType":"uint256","name":"serviceId","type":"uint256"},{"internalType":"address","name":"multisigImplementation","type":"address"},{"internalType":"bytes","name":"data","type":"bytes"}],"name":"deploy","outputs":[{"internalType":"address","name":"multisig","type":"address"}],"stateMutability":"nonpayable","type":"function"},{"inputs":[],"name":"drain","outputs":[{"internalType":"uint256","name":"amount","type":"uint256"}],"stateMutability":"nonpayable","type":"function"},{"inputs":[],"name":"drainer","outputs":[{"internalType":"address","name":"","type":"address"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"unitId","type":"uint256"}],"name":"exists","outputs":[{"internalType":"bool","name":"","type":"bool"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"serviceId","type":"uint256"}],"name":"getAgentInstances","outputs":[{"internalType":"uint256","name":"numAgentInstances","type":"uint256"},{"internalType":"address[]","name":"agentInstances","type":"address[]"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"serviceId","type":"uint256"}],"name":"getAgentParams","outputs":[{"internalType":"uint256","name":"numAgentIds","type":"uint256"},{"components":[{"internalType":"uint32","name":"slots","type":"uint32"},{"internalType":"uint96","name":"bond","type":"uint96"}],"internalType":"struct AgentParams[]","name":"agentParams","type":"tuple[]"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"","type":"uint256"}],"name":"getApproved","outputs":[{"internalType":"address","name":"","type":"address"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"serviceId","type":"uint256"},{"internalType":"uint256","name":"agentId","type":"uint256"}],"name":"getInstancesForAgentId","outputs":[{"internalType":"uint256","name":"numAgentInstances","type":"uint256"},{"internalType":"address[]","name":"agentInstances","type":"address[]"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"address","name":"operator","type":"address"},{"internalType":"uint256","name":"serviceId","type":"uint256"}],"name":"getOperatorBalance","outputs":[{"internalType":"uint256","name":"balance","type":"uint256"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"serviceId","type":"uint256"}],"name":"getPreviousHashes","outputs":[{"internalType":"uint256","name":"numHashes","type":"uint256"},{"internalType":"bytes32[]","name":"configHashes","type":"bytes32[]"}],"stateMutability":"view","type":"function"},{"inputs":[{"internalType":"uint256","name":"serviceId","type":"uint256"}],"name":"getService","outputs":[{"components":[{"internalType":"uint96","name":"securityDeposit","type":"uint96"},{"internalType":"address","name":"multisig","type":"address"},{"internalType":"bytes32","name":"configHash","type":"bytes32"},{"internalType":"uint32","name":"threshold","type":"uint32"},{"internalType":"uint32","name":"maxNumAgentInstances","type":"uint32"},{"internalType":"uint32","name":"numAgentInstances","type":"uint32"},{"internalType":"enum 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daily_transactions_new.csv

@@ -1,334 +0,0 @@
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-optimism,0x37036d9A0E4aDE3898336a42CC31c71387D48B78,2024-11-20,0xa5d0aa63e6dcc4a7c15df2a2ff27c7852aaa9aaacf41db864cc2bfa01ee7f7d9,2024-11-20 20:23:05,0x050e90bea65c9ba6416996bb6fffa8f925885d40,0x37036d9a0e4ade3898336a42cc31c71387d48b78,0.0,0x050E90BEA65c9Ba6416996bb6fFfa8f925885d40
-optimism,0x5f0C4273Ff97ae91fc8D2fc8621b5E37a741d1b1,2024-11-20,0xd880358907e288a44179e4818d853c708df2eed8e6e22f1615ef2745b523c73d,2024-11-20 15:22:37,0xa0ff35bfbd3c42e3afe29255742c7558498f5544,0x5f0c4273ff97ae91fc8d2fc8621b5e37a741d1b1,0.0,0xA0fF35Bfbd3C42E3aFE29255742C7558498f5544
-optimism,0x5f0C4273Ff97ae91fc8D2fc8621b5E37a741d1b1,2024-11-20,0x73a4bd3298fe8cff0c8fbc7b2a4ed13c6b65487005f1e4941f62ec76c7d722a8,2024-11-20 15:23:05,0xa0ff35bfbd3c42e3afe29255742c7558498f5544,0x5f0c4273ff97ae91fc8d2fc8621b5e37a741d1b1,0.0,0xA0fF35Bfbd3C42E3aFE29255742C7558498f5544
-optimism,0x5f0C4273Ff97ae91fc8D2fc8621b5E37a741d1b1,2024-11-20,0x1c481b97d2f51165d16c3bb9ab9be3150ef3e9e498d62d505790b1696a1d711a,2024-11-20 15:23:39,0xa0ff35bfbd3c42e3afe29255742c7558498f5544,0x5f0c4273ff97ae91fc8d2fc8621b5e37a741d1b1,0.0,0xA0fF35Bfbd3C42E3aFE29255742C7558498f5544
-optimism,0x5f0C4273Ff97ae91fc8D2fc8621b5E37a741d1b1,2024-11-20,0x1a22efadf88b6963b3a2cd8673eee706031cb7e7e836c1e6285ffae8d4707cde,2024-11-20 15:24:07,0xa0ff35bfbd3c42e3afe29255742c7558498f5544,0x5f0c4273ff97ae91fc8d2fc8621b5e37a741d1b1,0.0,0xA0fF35Bfbd3C42E3aFE29255742C7558498f5544
-optimism,0x5EFC7B394A916aC8090a7D6221F798AbFb9F5510,2024-11-20,0xaaae37299329f78ac559ecc15645c370d0a005418c644ee74fc8e168b1315d5a,2024-11-20 04:09:03,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0x5efc7b394a916ac8090a7d6221f798abfb9f5510,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
-optimism,0x5EFC7B394A916aC8090a7D6221F798AbFb9F5510,2024-11-20,0xb2fe13dc01083c55b85fb19839a10088c8b1f17f6c1785dc478103e70615f5b3,2024-11-20 04:09:33,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0x5efc7b394a916ac8090a7d6221f798abfb9f5510,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
-optimism,0x5EFC7B394A916aC8090a7D6221F798AbFb9F5510,2024-11-20,0x05961dda08b3e88605517591d5a9f133f611d66d1c80f6ecb229f70c97b9aeb7,2024-11-20 04:10:03,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0x5efc7b394a916ac8090a7d6221f798abfb9f5510,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
-optimism,0x5EFC7B394A916aC8090a7D6221F798AbFb9F5510,2024-11-20,0x0095018a7d02defd5c9a1f8fda0f4b3294a3a17d9ae97b1885c5ed0431ffbb16,2024-11-20 04:10:31,0x554d1444b6a38fa2eb18d86f2c10f42be630c89d,0x5efc7b394a916ac8090a7d6221f798abfb9f5510,0.0,0x554D1444b6a38fA2eb18d86f2C10F42BE630c89D
-mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-20,0xcfb768d0d84ad720cde123add806bac3a926b8c1cc00fd06633e45f4eeb5d299,2024-11-20 09:28:43,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
-mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-20,0xed0d98331f87b174b3e71a2e4a366be444735b8146aabaa73deca121b6672c9d,2024-11-20 09:28:11,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
-mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-20,0x007f6d8ee852a151298131b944e0e26f7c64477f91070399c797bbac4ffdc31f,2024-11-20 09:27:45,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
-mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-20,0xe1243862f190fe30e8daaf89d1e9f499a93d2dbbd2bc717fdfa80437597a8e12,2024-11-20 09:27:13,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92
-mode,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,2024-11-20,0xb654fa5b7e2ae3e05577cfd7b9ad3b6beb1446463dac5f39ed12e8dc74d65380,2024-11-20 09:00:53,0x2302548951Ae698370e3C7943A843861641efB92,0x602afe99D8CF58ba03794d4C7489f6f08702e4e6,0.0,0x2302548951Ae698370e3C7943A843861641efB92

daily_value_locked.csv

@@ -1,8 +0,0 @@
-chain_name,date,event,topic_0,from,to,token_id,token0,token1,liquidity,amount0,amount1,amount0_usd,amount1_usd
-optimism,2024-10-07,Transfer,0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef,0x0000000000000000000000000000000000000000,0x5f0c4273ff97ae91fc8d2fc8621b5e37a741d1b1,826974,0x2218a117083f5B482B0bB821d27056Ba9c04b1D3,0xdFA46478F9e5EA86d57387849598dbFB2e964b02,13516644515555081265,8.98929230571145,20.324144854406946,9.97811445933971,10.184307041674192
-base,2024-09-19,Transfer,0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef,0x0000000000000000000000000000000000000000,0xa13dfc6ddcff0b5b637e721ee83d6cf7e0676e73,963863,0x01CCF4941298a0b5AC4714c0E1799a2dF8387048,0x4200000000000000000000000000000000000006,1210264887099988865,359.58545796548844,0.0040734158306472,9.341785679831974,10.270669941235578
-base,2024-09-26,Transfer,0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef,0x0000000000000000000000000000000000000000,0xac55eeb3fdacfdfae78c62caa58934900ad54ed2,978001,0x01CCF4941298a0b5AC4714c0E1799a2dF8387048,0x4200000000000000000000000000000000000006,1524286215726092055,469.7981777244338,0.004945631076533,12.205037194519935,12.469864740059744
-mode,2024-11-07,Transfer,0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef,0x0000000000000000000000000000000000000000,0xac55eeb3fdacfdfae78c62caa58934900ad54ed2,978001,0x01CCF4941298a0b5AC4714c0E1799a2dF8387048,0x4200000000000000000000000000000000000006,1524286215726092055,469.7981777244338,0.004945631076533,18.2495,18.3223
-mode,2024-11-19,Transfer,0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef,0x0000000000000000000000000000000000000000,0xac55eeb3fdacfdfae78c62caa58934900ad54ed2,978001,0x01CCF4941298a0b5AC4714c0E1799a2dF8387048,0x4200000000000000000000000000000000000006,1524286215726092055,469.7981777244338,0.004945631076533,16.9373,16.9373
-optimism,2024-10-14,Transfer,0xddf252ad1be2c89b66c2b069fc378daa952ba7f163c4a11628f55a4df523b3ef,0x0000000000000000000000000000000000000000,0xac55eeb3fdacfdfae78c62caa58934900ad54ed2,978001,0x01CCF4941298a0b5AC4714c0E1799a2dF8387048,0x4200000000000000000000000000000000000006,1524286215726092055,469.7981777244338,0.004945631076533,12.0644,12.3921
-optimism,2024-10-04,Transfer,0xddf272ad1be2c89b69c2b068fc378daa956ba7f163c4a11628f55a4df523b3ef,0x0000000000000000000000000000000000000000,0xac55eeb3fdacfdfae78c62caa58934900ad54ed2,978001,0x01CCF4941298a0b5AC4714c0E1799a2dF8387048,0x4200000000000000000000000000000000000006,1524286215726092055,469.7981777244338,0.004945631076533,14.9849,14.9849

modius_apr_statistics.csv

@@ -1,3 +0,0 @@
-agent_id,agent_name,total_points,apr_points,performance_points,real_apr_points,real_performance_points,avg_apr,avg_performance,max_apr,min_apr,latest_timestamp
-2,gonma-pronfa59,1,0,1,0,1,,-100.0,,,2025-04-04 14:03:55
-ALL,All Agents,1,0,1,0,1,,-100.0,,,2025-04-04 14:03:55

modius_apr_values.csv

@@ -1,2 +0,0 @@
-apr,timestamp,agent_id,is_dummy,agent_name,metric_type
--100.0,2025-04-04 14:03:55,2,False,gonma-pronfa59,Performance

modius_performance/config/constants.py

@@ -0,0 +1,90 @@
+"""
+Constants and configuration values for the Modius Agent Performance application.
+"""
+from datetime import datetime
+from typing import Dict, Any
+
+# API Configuration
+API_BASE_URL = "https://afmdb.autonolas.tech"
+
+# Chart Colors
+CHART_COLORS = {
+    'apr': 'red',
+    'adjusted_apr': 'green',
+    'roi': 'blue',
+    'volume': 'purple',
+    'positive_region': 'rgba(230, 243, 255, 0.3)',
+    'negative_region': 'rgba(255, 230, 230, 0.3)',
+    'zero_line': 'black'
+}
+
+# Date Ranges
+DATE_RANGES = {
+    'apr_start': datetime(2025, 4, 17),
+    'may_cutoff': datetime(2025, 5, 10),
+    'feb_start': datetime(2025, 2, 1)
+}
+
+# Chart Configuration
+CHART_CONFIG = {
+    'height': 600,
+    'template': 'plotly_white',
+    'font_family': 'Arial, sans-serif',
+    'title_size': 22,
+    'axis_font_size': 14,
+    'legend_font_size': 14,
+    'moving_average_window_days': 7,
+    'use_corrected_data': True,  # Use corrected CSV data instead of database
+    'corrected_data_file': 'corrected_apr_roi_data.csv',
+    'max_visible_agents': 5,
+    'timestamp_aggregation_method': 'median'  # 'mean' or 'median'
+}
+
+# Y-axis Ranges
+Y_AXIS_RANGES = {
+    'apr': {'min': -50, 'max': 100},
+    'roi': {'min': -100, 'max': 100},
+    'volume': {'auto': True}
+}
+
+# Agent Hash Version Mapping
+AGENT_HASH_VERSIONS = {
+    'tby': 'v0.4.1',
+    'vq': 'v0.4.2'
+}
+
+# Logging Configuration
+LOGGING_CONFIG = {
+    'level': 'INFO',
+    'format': '%(asctime)s - %(levelname)s - %(message)s',
+    'file': 'app_debug.log'
+}
+
+# File Paths
+FILE_PATHS = {
+    'apr_csv': 'modius_apr_values.csv',
+    'roi_csv': 'modius_roi_values.csv',
+    'volume_csv': 'modius_volume_values.csv',
+    'apr_hash_csv': 'modius_apr_vs_agent_hash.csv',
+    'statistics_csv': 'modius_apr_statistics.csv',
+    'apr_processed_csv': 'modius_apr_processed_values.csv',
+    'roi_processed_csv': 'modius_roi_processed_values.csv',
+    'apr_graph_html': 'modius_apr_combined_graph.html',
+    'apr_graph_png': 'modius_apr_combined_graph.png',
+    'roi_graph_html': 'modius_roi_graph.html',
+    'roi_graph_png': 'modius_roi_graph.png',
+    'volume_graph_html': 'modius_volume_graph.html',
+    'volume_graph_png': 'modius_volume_graph.png',
+    'apr_hash_graph_html': 'modius_apr_vs_agent_hash_graph.html',
+    'apr_hash_graph_png': 'modius_apr_vs_agent_hash_graph.png'
+}
+
+# Data Processing Configuration
+DATA_CONFIG = {
+    'agent_type_name': 'Modius',
+    'attribute_name': 'APR',
+    'api_limit': 1000,
+    'exclude_apr_values': [0],  # Only exclude zero APR values
+    'excluded_agents': [1, 18, 2, 5, 4],  # Include all agents including testing agents for population analysis
+    'max_apr_threshold': 600  # Maximum allowed APR value (remove values above this)
+}

modius_performance/data/api_client.py

@@ -0,0 +1,164 @@
+"""
+API client for interacting with the Modius Agent Performance API.
+"""
+import requests
+import logging
+from typing import List, Dict, Any, Optional
+
+from ..config.constants import API_BASE_URL, DATA_CONFIG
+from .models import (
+    AgentTypeResponse, AttributeDefinitionResponse, 
+    AgentsListResponse, AttributeValuesResponse
+)
+
+logger = logging.getLogger(__name__)
+
+
+class ModiusAPIClient:
+    """Client for interacting with the Modius Agent Performance API."""
+    
+    def __init__(self, base_url: str = API_BASE_URL):
+        self.base_url = base_url
+        self.session = requests.Session()
+        # Set default timeout and headers
+        self.session.timeout = 30
+        self.session.headers.update({
+            'User-Agent': 'Modius-Agent-Performance/1.0'
+        })
+    
+    def _make_request(self, endpoint: str, params: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        """Make a request to the API and return the response."""
+        url = f"{self.base_url}{endpoint}"
+        logger.debug(f"Making API request to: {url}")
+        
+        try:
+            response = self.session.get(url, params=params)
+            logger.debug(f"Response status: {response.status_code}")
+            
+            if response.status_code == 404:
+                logger.warning(f"Resource not found: {url}")
+                return {"error": "Not found", "status_code": 404}
+            
+            response.raise_for_status()
+            return {"data": response.json(), "status_code": response.status_code}
+            
+        except requests.exceptions.RequestException as e:
+            logger.error(f"API request error for {url}: {e}")
+            return {"error": str(e), "status_code": getattr(e.response, 'status_code', 500)}
+    
+    def get_agent_type_by_name(self, type_name: str) -> AgentTypeResponse:
+        """Get agent type by name."""
+        endpoint = f"/api/agent-types/name/{type_name}"
+        result = self._make_request(endpoint)
+        
+        if "error" in result:
+            return AgentTypeResponse({}, result["status_code"])
+        
+        return AgentTypeResponse(result["data"], result["status_code"])
+    
+    def get_attribute_definition_by_name(self, attr_name: str) -> AttributeDefinitionResponse:
+        """Get attribute definition by name."""
+        endpoint = f"/api/attributes/name/{attr_name}"
+        result = self._make_request(endpoint)
+        
+        if "error" in result:
+            return AttributeDefinitionResponse({}, result["status_code"])
+        
+        return AttributeDefinitionResponse(result["data"], result["status_code"])
+    
+    def get_agents_by_type(self, type_id: int) -> AgentsListResponse:
+        """Get all agents of a specific type."""
+        endpoint = f"/api/agent-types/{type_id}/agents/"
+        result = self._make_request(endpoint)
+        
+        if "error" in result:
+            return AgentsListResponse([], result["status_code"])
+        
+        return AgentsListResponse(result["data"], result["status_code"])
+    
+    def get_agent_attributes(self, agent_id: int, limit: int = None) -> AttributeValuesResponse:
+        """Get attributes for a specific agent."""
+        endpoint = f"/api/agents/{agent_id}/attributes/"
+        params = {"limit": limit or DATA_CONFIG["api_limit"]}
+        result = self._make_request(endpoint, params)
+        
+        if "error" in result:
+            return AttributeValuesResponse([], result["status_code"])
+        
+        return AttributeValuesResponse(result["data"], result["status_code"])
+    
+    def get_attribute_values_by_type_and_attr(
+        self, 
+        agents: List[Dict[str, Any]], 
+        attr_def_id: int
+    ) -> List[Dict[str, Any]]:
+        """Get all attribute values for a specific attribute definition across all agents."""
+        all_attributes = []
+        logger.debug(f"Getting attributes for {len(agents)} agents with attr_def_id: {attr_def_id}")
+        
+        for agent in agents:
+            agent_id = agent["agent_id"]
+            
+            # Get agent attributes
+            response = self.get_agent_attributes(agent_id)
+            
+            if not response.is_success():
+                logger.error(f"Failed to get attributes for agent ID {agent_id}")
+                continue
+            
+            agent_attrs = response.get_attribute_values()
+            logger.debug(f"Agent {agent_id} has {len(agent_attrs)} attributes")
+            
+            # Filter for the specific attribute definition ID
+            filtered_attrs = [
+                attr for attr in agent_attrs 
+                if attr.get("attr_def_id") == attr_def_id
+            ]
+            logger.debug(f"Agent {agent_id} has {len(filtered_attrs)} matching attributes")
+            
+            if filtered_attrs:
+                logger.debug(f"Sample attribute for agent {agent_id}: {filtered_attrs[0]}")
+            
+            all_attributes.extend(filtered_attrs)
+        
+        logger.info(f"Total matching attributes found across all agents: {len(all_attributes)}")
+        return all_attributes
+    
+    def close(self):
+        """Close the session."""
+        self.session.close()
+    
+    def __enter__(self):
+        return self
+    
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.close()
+
+
+# Utility functions for backward compatibility
+def get_agent_name(agent_id: int, agents: List[Dict[str, Any]]) -> str:
+    """Get agent name from agent ID."""
+    for agent in agents:
+        if agent["agent_id"] == agent_id:
+            return agent["agent_name"]
+    return "Unknown"
+
+
+# Global client instance (can be replaced with dependency injection later)
+_client_instance = None
+
+
+def get_api_client() -> ModiusAPIClient:
+    """Get the global API client instance."""
+    global _client_instance
+    if _client_instance is None:
+        _client_instance = ModiusAPIClient()
+    return _client_instance
+
+
+def close_api_client():
+    """Close the global API client instance."""
+    global _client_instance
+    if _client_instance is not None:
+        _client_instance.close()
+        _client_instance = None

modius_performance/data/data_processor.py

@@ -0,0 +1,765 @@
+"""
+Data processing utilities for the Modius Agent Performance application.
+"""
+import json
+import pandas as pd
+import logging
+import os
+from datetime import datetime, timedelta
+from typing import List, Dict, Any, Optional, Tuple
+
+from ..config.constants import DATA_CONFIG, DATE_RANGES, CHART_CONFIG
+from .models import AgentMetric, AgentStatistics
+from .api_client import get_api_client, get_agent_name
+
+logger = logging.getLogger(__name__)
+
+
+class DataProcessor:
+    """Handles data processing and transformation for agent metrics."""
+    
+    def __init__(self):
+        self.api_client = get_api_client()
+    
+    def extract_apr_value(self, attr: Dict[str, Any]) -> Dict[str, Any]:
+        """Extract APR value, adjusted APR value, ROI value, volume, and timestamp from JSON value."""
+        try:
+            agent_id = attr.get("agent_id", "unknown")
+            logger.debug(f"Extracting APR value for agent {agent_id}")
+            
+            # The APR value is stored in the json_value field
+            if attr["json_value"] is None:
+                logger.debug(f"Agent {agent_id}: json_value is None")
+                return {
+                    "apr": None, "adjusted_apr": None, "roi": None, 
+                    "volume": None, "timestamp": None, "agent_id": agent_id, 
+                    "agent_hash": None, "is_dummy": False
+                }
+            
+            # If json_value is a string, parse it
+            if isinstance(attr["json_value"], str):
+                logger.debug(f"Agent {agent_id}: json_value is string, parsing")
+                json_data = json.loads(attr["json_value"])
+            else:
+                json_data = attr["json_value"]
+            
+            apr = json_data.get("apr")
+            adjusted_apr = json_data.get("adjusted_apr")
+            timestamp = json_data.get("timestamp")
+            volume = json_data.get("volume")
+            
+            # ROI extraction from calculation_metrics.f_i_ratio
+            roi = None
+            if "calculation_metrics" in json_data and json_data["calculation_metrics"] is not None:
+                if isinstance(json_data["calculation_metrics"], dict):
+                    f_i_ratio = json_data["calculation_metrics"].get("f_i_ratio")
+                    if f_i_ratio is not None:
+                        roi = f_i_ratio
+                        logger.debug(f"Agent {agent_id}: ROI extracted from f_i_ratio: {roi}")
+                    else:
+                        logger.debug(f"Agent {agent_id}: No f_i_ratio found in calculation_metrics")
+                else:
+                    logger.debug(f"Agent {agent_id}: calculation_metrics is not a dict: {type(json_data['calculation_metrics'])}")
+            else:
+                logger.debug(f"Agent {agent_id}: No calculation_metrics found")
+            
+            # Try to extract volume from portfolio_snapshot if it's not directly in json_data
+            if volume is None and "portfolio_snapshot" in json_data and json_data["portfolio_snapshot"] is not None:
+                portfolio = json_data["portfolio_snapshot"].get("portfolio")
+                if portfolio and isinstance(portfolio, dict):
+                    volume = portfolio.get("volume")
+            
+            # Extract agent_hash from json_data or portfolio_snapshot
+            agent_hash = json_data.get("agent_hash")
+            if agent_hash is None and "portfolio_snapshot" in json_data and json_data["portfolio_snapshot"] is not None:
+                portfolio = json_data["portfolio_snapshot"].get("portfolio")
+                if portfolio and isinstance(portfolio, dict):
+                    agent_hash = portfolio.get("agent_hash")
+            
+            logger.debug(f"Agent {agent_id}: Raw values - APR: {apr}, adjusted APR: {adjusted_apr}, ROI: {roi}, volume: {volume}, timestamp: {timestamp}, agent_hash: {agent_hash}")
+            
+            # Convert timestamp to datetime if it exists
+            timestamp_dt = None
+            if timestamp:
+                try:
+                    timestamp_dt = datetime.fromtimestamp(timestamp)
+                except (ValueError, TypeError, OSError) as e:
+                    logger.warning(f"Invalid timestamp {timestamp} for agent {agent_id}: {e}")
+                    timestamp_dt = None
+                
+            result = {
+                "apr": apr, 
+                "adjusted_apr": adjusted_apr, 
+                "roi": roi,
+                "volume": volume,
+                "timestamp": timestamp_dt, 
+                "agent_id": agent_id,
+                "agent_hash": agent_hash,
+                "is_dummy": False
+            }
+            logger.debug(f"Agent {agent_id}: Extracted result: {result}")
+            return result
+            
+        except (json.JSONDecodeError, KeyError, TypeError) as e:
+            logger.error(f"Error parsing JSON value: {e} for agent_id: {attr.get('agent_id')}")
+            logger.error(f"Problematic json_value: {attr.get('json_value')}")
+            return {
+                "apr": None, "adjusted_apr": None, "roi": None, 
+                "volume": None, "timestamp": None, "agent_id": attr.get('agent_id'), 
+                "agent_hash": None, "is_dummy": False
+            }
+    
+    def load_corrected_data_from_csv(self) -> Tuple[pd.DataFrame, pd.DataFrame]:
+        """Load corrected APR and ROI data from CSV file."""
+        logger.info("==== Loading corrected data from CSV ====")
+        
+        try:
+            # Get the CSV file path
+            csv_filename = CHART_CONFIG.get('corrected_data_file', 'corrected_apr_roi_data.csv')
+            csv_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), csv_filename)
+            
+            if not os.path.exists(csv_path):
+                logger.error(f"Corrected data file not found: {csv_path}")
+                return pd.DataFrame([]), pd.DataFrame([])
+            
+            # Read the CSV file
+            df = pd.read_csv(csv_path)
+            logger.info(f"Loaded {len(df)} rows from {csv_path}")
+            
+            # Parse dates and convert to datetime
+            df['Date'] = pd.to_datetime(df['Date'], format='%Y-%m-%d')
+            
+            # Create APR DataFrame
+            apr_data_list = []
+            roi_data_list = []
+            
+            for _, row in df.iterrows():
+                timestamp = row['Date']
+                apr_value = row['APR']
+                roi_value = row['ROI']
+                adjusted_apr_value = row['Adjusted_APR'] if 'Adjusted_APR' in df.columns else None
+                
+                # Create APR entry
+                apr_entry = {
+                    'apr': apr_value,
+                    'adjusted_apr': adjusted_apr_value,  # Now available in corrected data
+                    'roi': None,
+                    'volume': None,
+                    'timestamp': timestamp,
+                    'agent_id': 'corrected_data',
+                    'agent_name': 'Corrected Manual Data',
+                    'agent_hash': None,
+                    'is_dummy': False,
+                    'metric_type': 'APR'
+                }
+                apr_data_list.append(apr_entry)
+                
+                # Create ROI entry
+                roi_entry = {
+                    'roi': roi_value,
+                    'timestamp': timestamp,
+                    'agent_id': 'corrected_data',
+                    'agent_name': 'Corrected Manual Data',
+                    'is_dummy': False,
+                    'metric_type': 'ROI'
+                }
+                roi_data_list.append(roi_entry)
+            
+            # Convert to DataFrames
+            apr_df = pd.DataFrame(apr_data_list)
+            roi_df = pd.DataFrame(roi_data_list)
+            
+            # Filter data to end at May 29, 2025
+            end_date = datetime(2025, 5, 29)
+            apr_df = apr_df[apr_df['timestamp'] <= end_date]
+            roi_df = roi_df[roi_df['timestamp'] <= end_date]
+            
+            logger.info(f"Created APR DataFrame with {len(apr_df)} rows (filtered to end at May 29, 2025)")
+            logger.info(f"Created ROI DataFrame with {len(roi_df)} rows (filtered to end at May 29, 2025)")
+            
+            if not apr_df.empty:
+                logger.info(f"APR date range: {apr_df['timestamp'].min()} to {apr_df['timestamp'].max()}")
+                logger.info(f"APR statistics: min={apr_df['apr'].min():.2f}, max={apr_df['apr'].max():.2f}, mean={apr_df['apr'].mean():.2f}")
+            
+            if not roi_df.empty:
+                logger.info(f"ROI date range: {roi_df['timestamp'].min()} to {roi_df['timestamp'].max()}")
+                logger.info(f"ROI statistics: min={roi_df['roi'].min():.2f}, max={roi_df['roi'].max():.2f}, mean={roi_df['roi'].mean():.2f}")
+            
+            return apr_df, roi_df
+            
+        except Exception as e:
+            logger.error(f"Error loading corrected data from CSV: {e}")
+            logger.exception("Exception traceback:")
+            return pd.DataFrame([]), pd.DataFrame([])
+
+    def fetch_apr_data_from_db(self) -> Tuple[pd.DataFrame, pd.DataFrame]:
+        """Fetch APR data from database using the API."""
+        # Check if we should use corrected data instead
+        if CHART_CONFIG.get('use_corrected_data', False):
+            logger.info("Using corrected CSV data instead of database")
+            return self.load_corrected_data_from_csv()
+        
+        logger.info("==== Starting APR data fetch ====")
+        
+        try:
+            # Step 1: Find the Modius agent type
+            logger.info("Finding Modius agent type")
+            modius_type_response = self.api_client.get_agent_type_by_name(DATA_CONFIG["agent_type_name"])
+            
+            if not modius_type_response.is_success():
+                logger.error("Modius agent type not found, using placeholder data")
+                return pd.DataFrame([]), pd.DataFrame([])
+            
+            modius_type = modius_type_response.get_agent_type()
+            type_id = modius_type.type_id
+            logger.info(f"Found Modius agent type with ID: {type_id}")
+            
+            # Step 2: Find the APR attribute definition
+            logger.info("Finding APR attribute definition")
+            apr_attr_response = self.api_client.get_attribute_definition_by_name(DATA_CONFIG["attribute_name"])
+            
+            if not apr_attr_response.is_success():
+                logger.error("APR attribute definition not found, using placeholder data")
+                return pd.DataFrame([]), pd.DataFrame([])
+                
+            apr_attr_def = apr_attr_response.get_attribute_definition()
+            attr_def_id = apr_attr_def.attr_def_id
+            logger.info(f"Found APR attribute definition with ID: {attr_def_id}")
+            
+            # Step 3: Get all agents of type Modius
+            logger.info(f"Getting all agents of type Modius (type_id: {type_id})")
+            agents_response = self.api_client.get_agents_by_type(type_id)
+            
+            if not agents_response.is_success():
+                logger.error("No agents of type 'Modius' found")
+                return pd.DataFrame([]), pd.DataFrame([])
+            
+            modius_agents_info = agents_response.get_agents()
+            modius_agents = [
+                {
+                    "agent_id": agent.agent_id,
+                    "agent_name": agent.agent_name,
+                    "type_id": agent.type_id
+                }
+                for agent in modius_agents_info
+            ]
+            
+            # Filter out excluded agents
+            excluded_agents = DATA_CONFIG.get("excluded_agents", [])
+            if excluded_agents:
+                original_count = len(modius_agents)
+                modius_agents = [agent for agent in modius_agents if agent["agent_id"] not in excluded_agents]
+                filtered_count = original_count - len(modius_agents)
+                logger.info(f"Filtered out {filtered_count} excluded agents: {excluded_agents}")
+            
+            logger.info(f"Found {len(modius_agents)} Modius agents after filtering")
+            logger.debug(f"Modius agents: {[{'agent_id': a['agent_id'], 'agent_name': a['agent_name']} for a in modius_agents]}")
+            
+            # Step 4: Fetch all APR values for Modius agents
+            logger.info(f"Fetching APR values for all Modius agents (attr_def_id: {attr_def_id})")
+            apr_attributes = self.api_client.get_attribute_values_by_type_and_attr(modius_agents, attr_def_id)
+            
+            if not apr_attributes:
+                logger.error("No APR values found for 'Modius' agents")
+                return pd.DataFrame([]), pd.DataFrame([])
+            
+            logger.info(f"Found {len(apr_attributes)} APR attributes total")
+            
+            # Step 5: Extract APR and ROI data
+            logger.info("Extracting APR and ROI data from attributes")
+            apr_data_list = []
+            roi_data_list = []
+            
+            for attr in apr_attributes:
+                data = self.extract_apr_value(attr)
+                if data["timestamp"] is not None:
+                    # Get agent name
+                    agent_name = get_agent_name(attr["agent_id"], modius_agents)
+                    data["agent_name"] = agent_name
+                    data["is_dummy"] = False
+                    
+                    # Process APR data
+                    if data["apr"] is not None:
+                        # Filter APR values based on exclusion list
+                        if data["apr"] not in DATA_CONFIG.get("exclude_apr_values", []):
+                            apr_entry = data.copy()
+                            apr_entry["metric_type"] = "APR"
+                            logger.debug(f"Agent {agent_name} ({attr['agent_id']}): APR value: {data['apr']}")
+                            apr_data_list.append(apr_entry)
+                        else:
+                            logger.debug(f"Skipping APR value for agent {agent_name}: {data['apr']} (excluded value)")
+                    
+                    # Process ROI data
+                    if data["roi"] is not None:
+                        # Filter ROI values if needed (similar to APR filtering)
+                        roi_entry = {
+                            "roi": data["roi"],
+                            "timestamp": data["timestamp"],
+                            "agent_id": data["agent_id"],
+                            "agent_name": agent_name,
+                            "is_dummy": False,
+                            "metric_type": "ROI"
+                        }
+                        logger.debug(f"Agent {agent_name} ({attr['agent_id']}): ROI value: {data['roi']}")
+                        roi_data_list.append(roi_entry)
+                    else:
+                        logger.debug(f"Agent {agent_name} ({attr['agent_id']}): No ROI data available")
+            
+            logger.info(f"Extracted {len(apr_data_list)} valid APR data points and {len(roi_data_list)} valid ROI data points")
+            
+            # Convert to DataFrames
+            apr_df = pd.DataFrame(apr_data_list) if apr_data_list else pd.DataFrame([])
+            roi_df = pd.DataFrame(roi_data_list) if roi_data_list else pd.DataFrame([])
+            
+            # Log the resulting dataframes
+            logger.info(f"Created APR DataFrame with {len(apr_df)} rows")
+            if not apr_df.empty:
+                logger.info(f"APR DataFrame columns: {apr_df.columns.tolist()}")
+                logger.info(f"APR statistics: min={apr_df['apr'].min()}, max={apr_df['apr'].max()}, mean={apr_df['apr'].mean()}")
+            
+            logger.info(f"Created ROI DataFrame with {len(roi_df)} rows")
+            if not roi_df.empty:
+                logger.info(f"ROI DataFrame columns: {roi_df.columns.tolist()}")
+                logger.info(f"ROI statistics: min={roi_df['roi'].min()}, max={roi_df['roi'].max()}, mean={roi_df['roi'].mean()}")
+            
+            return apr_df, roi_df
+            
+        except Exception as e:
+            logger.error(f"Error fetching APR data: {e}")
+            logger.exception("Exception traceback:")
+            return pd.DataFrame([]), pd.DataFrame([])
+    
+    def filter_outliers(self, df: pd.DataFrame, column: str, threshold: float = None) -> pd.DataFrame:
+        """Filter outliers from a DataFrame based on a threshold."""
+        if df.empty or column not in df.columns:
+            return df
+        
+        # Use configured outlier threshold
+        threshold = threshold or DATA_CONFIG.get("outlier_threshold", 200)
+        
+        # Filter out outliers based on threshold
+        outlier_mask = (df[column] > threshold) | (df[column] < -threshold)
+        filtered_df = df[~outlier_mask]
+        
+        outliers_removed = len(df) - len(filtered_df)
+        if outliers_removed > 0:
+            logger.info(f"Filtered {outliers_removed} outliers from {column} column using threshold ±{threshold}")
+        else:
+            logger.info(f"No outliers found in {column} column using threshold ±{threshold}")
+        
+        return filtered_df
+    
+    def calculate_moving_average(self, df: pd.DataFrame, value_column: str, window_days: int = None) -> pd.DataFrame:
+        """Calculate moving average for a DataFrame."""
+        if df.empty:
+            return df
+        
+        window_days = window_days or CHART_CONFIG["moving_average_window_days"]
+        time_window = pd.Timedelta(days=window_days)
+        aggregation_method = CHART_CONFIG.get("timestamp_aggregation_method", "mean")
+        
+        # Sort by timestamp
+        df_sorted = df.sort_values('timestamp')
+        
+        # Group by timestamp and calculate aggregation (mean or median based on config)
+        if aggregation_method == "median":
+            aggregated_data = df_sorted.groupby('timestamp')[value_column].median().reset_index()
+            logger.info(f"Using median aggregation for timestamp grouping")
+        else:
+            aggregated_data = df_sorted.groupby('timestamp')[value_column].mean().reset_index()
+            logger.info(f"Using mean aggregation for timestamp grouping")
+        
+        aggregated_data = aggregated_data.sort_values('timestamp')
+        
+        # Calculate moving average (still using mean for the moving window)
+        aggregated_data['moving_avg'] = None
+        
+        for i, row in aggregated_data.iterrows():
+            current_time = row['timestamp']
+            window_start = current_time - time_window
+            
+            # Get all data points within the time window
+            window_data = df_sorted[
+                (df_sorted['timestamp'] >= window_start) & 
+                (df_sorted['timestamp'] <= current_time)
+            ]
+            
+            if not window_data.empty:
+                aggregated_data.at[i, 'moving_avg'] = window_data[value_column].mean()
+            else:
+                aggregated_data.at[i, 'moving_avg'] = row[value_column]
+        
+        logger.info(f"Calculated {window_days}-day moving average with {len(aggregated_data)} points using {aggregation_method} timestamp aggregation")
+        return aggregated_data
+    
+    def generate_statistics(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Generate statistics from the data."""
+        if df.empty:
+            return pd.DataFrame()
+        
+        # Get unique agents
+        unique_agents = df['agent_id'].unique()
+        stats_list = []
+        
+        # Generate per-agent statistics
+        for agent_id in unique_agents:
+            agent_data = df[df['agent_id'] == agent_id]
+            agent_name = agent_data['agent_name'].iloc[0]
+            
+            # APR statistics
+            apr_data = agent_data[agent_data['metric_type'] == 'APR']
+            real_apr = apr_data[apr_data['is_dummy'] == False]
+            
+            # Check if adjusted_apr exists and has non-null values
+            has_adjusted_apr = 'adjusted_apr' in apr_data.columns and apr_data['adjusted_apr'].notna().any()
+            
+            stats = {
+                'agent_id': agent_id,
+                'agent_name': agent_name,
+                'total_points': len(agent_data),
+                'apr_points': len(apr_data),
+                'real_apr_points': len(real_apr),
+                'avg_apr': apr_data['apr'].mean() if not apr_data.empty else None,
+                'max_apr': apr_data['apr'].max() if not apr_data.empty else None,
+                'min_apr': apr_data['apr'].min() if not apr_data.empty else None,
+                'avg_adjusted_apr': apr_data['adjusted_apr'].mean() if has_adjusted_apr else None,
+                'max_adjusted_apr': apr_data['adjusted_apr'].max() if has_adjusted_apr else None,
+                'min_adjusted_apr': apr_data['adjusted_apr'].min() if has_adjusted_apr else None,
+                'latest_timestamp': agent_data['timestamp'].max().strftime('%Y-%m-%d %H:%M:%S') if not agent_data.empty and pd.notna(agent_data['timestamp'].max()) else None
+            }
+            stats_list.append(stats)
+        
+        # Generate overall statistics
+        apr_only = df[df['metric_type'] == 'APR']
+        has_adjusted_apr_overall = 'adjusted_apr' in apr_only.columns and apr_only['adjusted_apr'].notna().any()
+        
+        overall_stats = {
+            'agent_id': 'ALL',
+            'agent_name': 'All Agents',
+            'total_points': len(df),
+            'apr_points': len(apr_only),
+            'real_apr_points': len(apr_only[apr_only['is_dummy'] == False]),
+            'avg_apr': apr_only['apr'].mean() if not apr_only.empty else None,
+            'max_apr': apr_only['apr'].max() if not apr_only.empty else None,
+            'min_apr': apr_only['apr'].min() if not apr_only.empty else None,
+            'avg_adjusted_apr': apr_only['adjusted_apr'].mean() if has_adjusted_apr_overall else None,
+            'max_adjusted_apr': apr_only['adjusted_apr'].max() if has_adjusted_apr_overall else None,
+            'min_adjusted_apr': apr_only['adjusted_apr'].min() if has_adjusted_apr_overall else None,
+            'latest_timestamp': df['timestamp'].max().strftime('%Y-%m-%d %H:%M:%S') if not df.empty and pd.notna(df['timestamp'].max()) else None
+        }
+        stats_list.append(overall_stats)
+        
+        return pd.DataFrame(stats_list)
+    
+    def calculate_daily_volume_changes(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Calculate daily volume totals and cumulative volume with 7-day SMA."""
+        if df.empty or 'volume' not in df.columns:
+            logger.warning("No volume data available for daily calculation")
+            return pd.DataFrame()
+        
+        # Filter out excluded agents
+        excluded_agents = DATA_CONFIG.get("excluded_agents", [])
+        if excluded_agents:
+            original_count = len(df)
+            df = df[~df['agent_id'].isin(excluded_agents)]
+            filtered_count = original_count - len(df)
+            logger.info(f"Filtered out {filtered_count} records from excluded agents {excluded_agents} for volume calculation")
+        
+        # Filter for records with volume data
+        volume_data = df[df['volume'].notna()].copy()
+        if volume_data.empty:
+            logger.warning("No valid volume data found")
+            return pd.DataFrame()
+        
+        # Ensure volume is float and timestamp is datetime
+        volume_data['volume'] = volume_data['volume'].astype(float)
+        volume_data = volume_data.sort_values('timestamp')
+        
+        logger.info(f"Processing {len(volume_data)} volume records for daily aggregation")
+        
+        # Group by date (day) and sum volume across all agents
+        volume_data['date'] = volume_data['timestamp'].dt.date
+        daily_sums = volume_data.groupby('date')['volume'].sum().reset_index()
+        daily_sums = daily_sums.sort_values('date')
+        
+        # Convert date back to datetime for easier plotting
+        daily_sums['date'] = pd.to_datetime(daily_sums['date'])
+        
+        # Calculate cumulative volume (each day adds to the previous day's total)
+        daily_sums['cumulative_volume'] = daily_sums['volume'].cumsum()
+        
+        # Calculate day-over-day percentage changes for the cumulative volume
+        daily_sums['prev_cumulative'] = daily_sums['cumulative_volume'].shift(1)
+        daily_sums['day_over_day_pct'] = ((daily_sums['cumulative_volume'] / daily_sums['prev_cumulative']) - 1) * 100
+        
+        # Set first day to 0% change
+        daily_sums.loc[0, 'day_over_day_pct'] = 0.0
+        
+        # Fill any NaN or infinite values with 0
+        daily_sums['day_over_day_pct'] = daily_sums['day_over_day_pct'].replace([float('inf'), float('-inf')], 0.0)
+        daily_sums['day_over_day_pct'] = daily_sums['day_over_day_pct'].fillna(0.0)
+        
+        # Calculate 7-day Simple Moving Average (SMA) of percentage changes
+        daily_sums['sma_7d'] = daily_sums['day_over_day_pct'].rolling(window=7, min_periods=1).mean()
+        
+        logger.info(f"Calculated daily changes for {len(daily_sums)} days")
+        logger.info(f"Daily volume range: {daily_sums['volume'].min():.2f} to {daily_sums['volume'].max():.2f}")
+        logger.info(f"Cumulative volume range: {daily_sums['cumulative_volume'].min():.2f} to {daily_sums['cumulative_volume'].max():.2f}")
+        logger.info(f"Day-over-day percentage range: {daily_sums['day_over_day_pct'].min():.1f}% to {daily_sums['day_over_day_pct'].max():.1f}%")
+        logger.info(f"7-day SMA range: {daily_sums['sma_7d'].min():.1f}% to {daily_sums['sma_7d'].max():.1f}%")
+        
+        return daily_sums[['date', 'volume', 'cumulative_volume', 'day_over_day_pct', 'sma_7d']]
+    
+    def calculate_weekly_volume_changes(self, df: pd.DataFrame) -> pd.DataFrame:
+        """Calculate weekly volume averages and week-over-week percentage changes."""
+        if df.empty or 'volume' not in df.columns:
+            logger.warning("No volume data available for weekly calculation")
+            return pd.DataFrame()
+        
+        # Filter out excluded agents
+        excluded_agents = DATA_CONFIG.get("excluded_agents", [])
+        if excluded_agents:
+            original_count = len(df)
+            df = df[~df['agent_id'].isin(excluded_agents)]
+            filtered_count = original_count - len(df)
+            logger.info(f"Filtered out {filtered_count} records from excluded agents {excluded_agents} for weekly volume calculation")
+        
+        # Filter for records with volume data
+        volume_data = df[df['volume'].notna()].copy()
+        if volume_data.empty:
+            logger.warning("No valid volume data found")
+            return pd.DataFrame()
+        
+        # Ensure volume is float and timestamp is datetime
+        volume_data['volume'] = volume_data['volume'].astype(float)
+        volume_data = volume_data.sort_values('timestamp')
+        
+        logger.info(f"Processing {len(volume_data)} volume records for weekly aggregation")
+        
+        # Group by Monday-starting weeks and sum volume
+        volume_data['week_start'] = volume_data['timestamp'].dt.to_period('W-MON').dt.start_time
+        
+        # Sum volume for each week across all agents
+        weekly_sums = volume_data.groupby('week_start')['volume'].sum().reset_index()
+        weekly_sums = weekly_sums.sort_values('week_start')
+        
+        # Calculate weekly averages (sum ÷ 7)
+        weekly_sums['weekly_avg_volume'] = weekly_sums['volume'] / 7
+        
+        # Calculate week-over-week percentage changes
+        weekly_sums['prev_week_avg'] = weekly_sums['weekly_avg_volume'].shift(1)
+        weekly_sums['week_over_week_pct'] = ((weekly_sums['weekly_avg_volume'] / weekly_sums['prev_week_avg']) - 1) * 100
+        
+        # Set first week to 0% change as requested
+        weekly_sums.loc[0, 'week_over_week_pct'] = 0.0
+        
+        # Fill any NaN values with 0
+        weekly_sums['week_over_week_pct'] = weekly_sums['week_over_week_pct'].fillna(0.0)
+        
+        logger.info(f"Calculated weekly changes for {len(weekly_sums)} weeks")
+        logger.info(f"Week-over-week percentage range: {weekly_sums['week_over_week_pct'].min():.1f}% to {weekly_sums['week_over_week_pct'].max():.1f}%")
+        
+        return weekly_sums[['week_start', 'weekly_avg_volume', 'week_over_week_pct']]
+    
+    def aggregate_daily_medians(self, df: pd.DataFrame, value_columns: List[str]) -> pd.DataFrame:
+        """
+        Aggregate multiple values per day for each agent by taking the median.
+        This reduces outliers and provides one clean data point per agent per day.
+        
+        Args:
+            df: DataFrame with agent data
+            value_columns: List of columns to aggregate (e.g., ['apr', 'adjusted_apr'] or ['roi'])
+        
+        Returns:
+            DataFrame with daily median values for each agent
+        """
+        if df.empty:
+            logger.info("No data to aggregate")
+            return df
+        
+        # Create a copy to avoid modifying the original
+        data = df.copy()
+        
+        # Add date column for grouping
+        data['date'] = data['timestamp'].dt.date
+        
+        # Group by agent and date, then calculate median for each value column
+        grouping_cols = ['agent_id', 'agent_name', 'date']
+        
+        # Prepare aggregation dictionary
+        agg_dict = {}
+        for col in value_columns:
+            if col in data.columns:
+                agg_dict[col] = 'median'
+        
+        # Add other columns that should be preserved (take first value per group)
+        preserve_cols = ['metric_type', 'is_dummy', 'agent_hash']
+        for col in preserve_cols:
+            if col in data.columns:
+                agg_dict[col] = 'first'
+        
+        if not agg_dict:
+            logger.warning("No valid columns to aggregate")
+            return pd.DataFrame()
+        
+        # Perform aggregation
+        aggregated = data.groupby(grouping_cols).agg(agg_dict).reset_index()
+        
+        # Convert date back to datetime and set as timestamp
+        aggregated['timestamp'] = pd.to_datetime(aggregated['date'])
+        aggregated = aggregated.drop('date', axis=1)
+        
+        # Sort by agent and timestamp
+        aggregated = aggregated.sort_values(['agent_id', 'timestamp'])
+        
+        # Log aggregation results
+        original_count = len(data)
+        aggregated_count = len(aggregated)
+        reduction_pct = ((original_count - aggregated_count) / original_count * 100) if original_count > 0 else 0
+        
+        logger.info(f"Daily median aggregation completed:")
+        logger.info(f"  Original data points: {original_count}")
+        logger.info(f"  Aggregated data points: {aggregated_count}")
+        logger.info(f"  Data reduction: {reduction_pct:.1f}%")
+        logger.info(f"  Aggregated columns: {list(agg_dict.keys())}")
+        
+        # Log per-agent statistics
+        agent_stats = data.groupby('agent_id').size()
+        aggregated_stats = aggregated.groupby('agent_id').size()
+        
+        for agent_id in agent_stats.index:
+            original = agent_stats[agent_id]
+            final = aggregated_stats.get(agent_id, 0)
+            agent_name = data[data['agent_id'] == agent_id]['agent_name'].iloc[0]
+            logger.debug(f"  Agent {agent_name} ({agent_id}): {original} → {final} points")
+        
+        return aggregated
+    
+    def filter_high_apr_values(self, apr_df: pd.DataFrame, roi_df: pd.DataFrame = None) -> Tuple[pd.DataFrame, pd.DataFrame]:
+        """
+        Filter out APR values above the threshold and corresponding ROI values.
+        Forward fill gaps to maintain continuity in time series.
+        
+        Args:
+            apr_df: DataFrame with APR data
+            roi_df: DataFrame with ROI data (optional)
+        
+        Returns:
+            Tuple of (filtered_apr_df, filtered_roi_df)
+        """
+        max_apr_threshold = DATA_CONFIG.get("max_apr_threshold", 400)
+        
+        if apr_df.empty:
+            logger.info("No APR data to filter")
+            return apr_df, roi_df if roi_df is not None else pd.DataFrame()
+        
+        # Create copies to avoid modifying originals
+        apr_filtered = apr_df.copy()
+        roi_filtered = roi_df.copy() if roi_df is not None and not roi_df.empty else pd.DataFrame()
+        
+        # Identify high APR values (both regular and adjusted APR)
+        high_apr_mask = pd.Series(False, index=apr_filtered.index)
+        
+        if 'apr' in apr_filtered.columns:
+            high_apr_mask |= (apr_filtered['apr'] > max_apr_threshold)
+        
+        if 'adjusted_apr' in apr_filtered.columns:
+            high_apr_mask |= (apr_filtered['adjusted_apr'] > max_apr_threshold)
+        
+        high_apr_count = high_apr_mask.sum()
+        
+        if high_apr_count == 0:
+            logger.info(f"No APR values above {max_apr_threshold}% threshold found")
+            return apr_filtered, roi_filtered
+        
+        logger.info(f"Found {high_apr_count} APR values above {max_apr_threshold}% threshold")
+        
+        # Get the high APR records for ROI filtering
+        high_apr_records = apr_filtered[high_apr_mask][['agent_id', 'timestamp']].copy()
+        
+        # Remove high APR values
+        apr_filtered = apr_filtered[~high_apr_mask].copy()
+        
+        # Forward fill APR gaps for each agent
+        apr_filled_count = 0
+        for agent_id in apr_filtered['agent_id'].unique():
+            agent_mask = apr_filtered['agent_id'] == agent_id
+            agent_data = apr_filtered[agent_mask].sort_values('timestamp')
+            
+            if len(agent_data) > 0:
+                # Forward fill APR values
+                if 'apr' in apr_filtered.columns:
+                    filled_apr = agent_data['apr'].ffill()
+                    apr_filled_count += (filled_apr != agent_data['apr']).sum()
+                    apr_filtered.loc[agent_mask, 'apr'] = filled_apr
+                
+                # Forward fill adjusted APR values
+                if 'adjusted_apr' in apr_filtered.columns:
+                    filled_adj_apr = agent_data['adjusted_apr'].ffill()
+                    apr_filtered.loc[agent_mask, 'adjusted_apr'] = filled_adj_apr
+        
+        logger.info(f"Forward filled {apr_filled_count} APR gaps")
+        
+        # Filter corresponding ROI values if ROI data is provided
+        if not roi_filtered.empty and not high_apr_records.empty:
+            # Remove ROI values for the same agent-timestamp combinations
+            roi_before_count = len(roi_filtered)
+            
+            # Merge to identify which ROI records to remove
+            roi_to_remove = roi_filtered.merge(
+                high_apr_records,
+                on=['agent_id', 'timestamp'],
+                how='inner'
+            )
+            
+            roi_removed_count = len(roi_to_remove)
+            
+            if roi_removed_count > 0:
+                # Remove the matching ROI records
+                roi_filtered = roi_filtered.merge(
+                    high_apr_records,
+                    on=['agent_id', 'timestamp'],
+                    how='left',
+                    indicator=True
+                )
+                roi_filtered = roi_filtered[roi_filtered['_merge'] == 'left_only'].drop('_merge', axis=1)
+                
+                # Forward fill ROI gaps for each agent
+                roi_filled_count = 0
+                for agent_id in roi_filtered['agent_id'].unique():
+                    agent_mask = roi_filtered['agent_id'] == agent_id
+                    agent_data = roi_filtered[agent_mask].sort_values('timestamp')
+                    
+                    if len(agent_data) > 0 and 'roi' in roi_filtered.columns:
+                        filled_roi = agent_data['roi'].ffill()
+                        roi_filled_count += (filled_roi != agent_data['roi']).sum()
+                        roi_filtered.loc[agent_mask, 'roi'] = filled_roi
+                
+                logger.info(f"Removed {roi_removed_count} corresponding ROI values")
+                logger.info(f"Forward filled {roi_filled_count} ROI gaps")
+            else:
+                logger.info("No corresponding ROI values to remove")
+        
+        # Log final statistics
+        logger.info(f"High APR filtering completed:")
+        logger.info(f"  APR threshold: {max_apr_threshold}%")
+        logger.info(f"  APR values removed: {high_apr_count}")
+        logger.info(f"  Final APR data points: {len(apr_filtered)}")
+        if not roi_filtered.empty:
+            logger.info(f"  Final ROI data points: {len(roi_filtered)}")
+        
+        return apr_filtered, roi_filtered
+    
+    def save_to_csv(self, df: pd.DataFrame, filename: str) -> Optional[str]:
+        """Save DataFrame to CSV file."""
+        if df.empty:
+            logger.error(f"No data to save to {filename}")
+            return None
+        
+        try:
+            df.to_csv(filename, index=False)
+            logger.info(f"Data saved to {filename}")
+            return filename
+        except Exception as e:
+            logger.error(f"Error saving data to {filename}: {e}")
+            return None

modius_performance/data/models.py

@@ -0,0 +1,148 @@
+"""
+Data models for the Modius Agent Performance application.
+"""
+from dataclasses import dataclass
+from datetime import datetime
+from typing import Optional, List, Dict, Any
+
+
+@dataclass
+class AgentMetric:
+    """Represents a single agent performance metric."""
+    agent_id: int
+    agent_name: str
+    timestamp: datetime
+    metric_type: str
+    apr: Optional[float] = None
+    adjusted_apr: Optional[float] = None
+    roi: Optional[float] = None
+    volume: Optional[float] = None
+    agent_hash: Optional[str] = None
+    is_dummy: bool = False
+
+
+@dataclass
+class AgentInfo:
+    """Represents basic agent information."""
+    agent_id: int
+    agent_name: str
+    type_id: int
+
+
+@dataclass
+class AgentType:
+    """Represents an agent type."""
+    type_id: int
+    type_name: str
+
+
+@dataclass
+class AttributeDefinition:
+    """Represents an attribute definition."""
+    attr_def_id: int
+    attr_name: str
+
+
+@dataclass
+class AgentStatistics:
+    """Represents statistical data for an agent."""
+    agent_id: int
+    agent_name: str
+    total_points: int
+    apr_points: int
+    performance_points: int
+    real_apr_points: int
+    real_performance_points: int
+    avg_apr: Optional[float] = None
+    avg_performance: Optional[float] = None
+    max_apr: Optional[float] = None
+    min_apr: Optional[float] = None
+    avg_adjusted_apr: Optional[float] = None
+    max_adjusted_apr: Optional[float] = None
+    min_adjusted_apr: Optional[float] = None
+    latest_timestamp: Optional[str] = None
+
+
+@dataclass
+class ChartData:
+    """Represents data for chart visualization."""
+    x_values: List[datetime]
+    y_values: List[float]
+    agent_name: str
+    metric_type: str
+    color: str
+    visible: bool = True
+
+
+@dataclass
+class MovingAverageData:
+    """Represents moving average data."""
+    timestamp: datetime
+    value: float
+    moving_avg: Optional[float] = None
+    adjusted_moving_avg: Optional[float] = None
+
+
+class APIResponse:
+    """Base class for API responses."""
+    
+    def __init__(self, data: Dict[str, Any], status_code: int = 200):
+        self.data = data
+        self.status_code = status_code
+        self.success = status_code == 200
+    
+    def is_success(self) -> bool:
+        return self.success
+    
+    def get_data(self) -> Dict[str, Any]:
+        return self.data if self.success else {}
+
+
+class AgentTypeResponse(APIResponse):
+    """Response for agent type API calls."""
+    
+    def get_agent_type(self) -> Optional[AgentType]:
+        if self.success and self.data:
+            return AgentType(
+                type_id=self.data.get('type_id'),
+                type_name=self.data.get('type_name')
+            )
+        return None
+
+
+class AttributeDefinitionResponse(APIResponse):
+    """Response for attribute definition API calls."""
+    
+    def get_attribute_definition(self) -> Optional[AttributeDefinition]:
+        if self.success and self.data:
+            return AttributeDefinition(
+                attr_def_id=self.data.get('attr_def_id'),
+                attr_name=self.data.get('attr_name')
+            )
+        return None
+
+
+class AgentsListResponse(APIResponse):
+    """Response for agents list API calls."""
+    
+    def get_agents(self) -> List[AgentInfo]:
+        if self.success and isinstance(self.data, list):
+            return [
+                AgentInfo(
+                    agent_id=agent.get('agent_id'),
+                    agent_name=agent.get('agent_name'),
+                    type_id=agent.get('type_id')
+                )
+                for agent in self.data
+                if agent.get('agent_id') and agent.get('agent_name')
+            ]
+        return []
+
+
+class AttributeValuesResponse(APIResponse):
+    """Response for attribute values API calls."""
+    
+    def get_attribute_values(self) -> List[Dict[str, Any]]:
+        if self.success and isinstance(self.data, list):
+            return self.data
+        return []

modius_performance/ui/dashboard.py

@@ -0,0 +1,470 @@
+"""
+Main dashboard UI for the Modius Agent Performance application.
+"""
+import gradio as gr
+import plotly.graph_objects as go
+import logging
+from typing import Tuple, Optional
+
+from ..data.data_processor import DataProcessor
+from ..visualization.apr_charts import generate_apr_visualizations, generate_apr_vs_agent_hash_visualizations
+from ..visualization.roi_charts import generate_roi_visualizations
+from ..visualization.volume_charts import generate_volume_visualizations
+from ..utils.logging_config import get_logger
+
+logger = get_logger(__name__)
+
+
+class ModiusDashboard:
+    """Main dashboard class for the Modius Agent Performance application."""
+    
+    def __init__(self):
+        self.data_processor = DataProcessor()
+        self.global_df = None
+        self.global_roi_df = None
+        self.global_volume_df = None
+    
+    def create_dashboard(self) -> gr.Blocks:
+        """Create the main dashboard interface."""
+        with gr.Blocks() as demo:
+            gr.Markdown("# Average Modius Agent Performance")
+            
+            # Create tabs for different metrics
+            with gr.Tabs():
+                # APR Metrics tab
+                with gr.Tab("APR Metrics"):
+                    apr_tab = self._create_apr_tab()
+                
+                # ROI Metrics tab
+                with gr.Tab("ROI Metrics"):
+                    roi_tab = self._create_roi_tab()
+                
+                # Volume Metrics tab - COMMENTED OUT
+                # with gr.Tab("Volume Metrics"):
+                #     volume_tab = self._create_volume_tab()
+                
+                # Performance Graph tab - COMMENTED OUT
+                # with gr.Tab("Performance Graph"):
+                #     performance_tab = self._create_performance_tab()
+            
+            # Add custom CSS for responsive design
+            self._add_custom_css()
+        
+        return demo
+    
+    def _create_apr_tab(self) -> gr.Column:
+        """Create the APR metrics tab."""
+        with gr.Column() as apr_tab:
+            refresh_apr_btn = gr.Button("Refresh APR Data")
+            
+            with gr.Column():
+                combined_apr_graph = gr.Plot(label="APR for All Agents", elem_id="responsive_apr_plot")
+            
+            # Toggle controls
+            with gr.Row(visible=True):
+                gr.Markdown("##### Toggle Graph Lines", elem_id="apr_toggle_title")
+            
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row(elem_id="apr_toggle_container"):
+                        with gr.Column(scale=1, min_width=150):
+                            apr_toggle = gr.Checkbox(label="APR Average (7-Day MA)", value=True, elem_id="apr_toggle")
+                        
+                        with gr.Column(scale=1, min_width=150):
+                            adjusted_apr_toggle = gr.Checkbox(label="ETH Adjusted APR Average (7-Day MA)", value=True, elem_id="adjusted_apr_toggle")
+            
+            apr_status_text = gr.Textbox(label="Status", value="Ready", interactive=False)
+            
+            # Set up event handlers
+            self._setup_apr_events(refresh_apr_btn, combined_apr_graph, apr_toggle, adjusted_apr_toggle, apr_status_text)
+            
+            # Initialize with placeholder
+            apr_placeholder_fig = self._create_placeholder_chart("Click 'Refresh APR Data' to load APR graph")
+            combined_apr_graph.value = apr_placeholder_fig
+        
+        return apr_tab
+    
+    def _create_roi_tab(self) -> gr.Column:
+        """Create the ROI metrics tab."""
+        with gr.Column() as roi_tab:
+            refresh_roi_btn = gr.Button("Refresh ROI Data")
+            
+            with gr.Column():
+                combined_roi_graph = gr.Plot(label="ROI for All Agents", elem_id="responsive_roi_plot")
+            
+            # Toggle controls
+            with gr.Row(visible=True):
+                gr.Markdown("##### Toggle Graph Lines", elem_id="roi_toggle_title")
+            
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row(elem_id="roi_toggle_container"):
+                        with gr.Column(scale=1, min_width=150):
+                            roi_toggle = gr.Checkbox(label="ROI Average (7-Day MA)", value=True, elem_id="roi_toggle")
+            
+            roi_status_text = gr.Textbox(label="Status", value="Ready", interactive=False)
+            
+            # Set up event handlers
+            self._setup_roi_events(refresh_roi_btn, combined_roi_graph, roi_toggle, roi_status_text)
+            
+            # Initialize with placeholder
+            roi_placeholder_fig = self._create_placeholder_chart("Click 'Refresh ROI Data' to load ROI graph")
+            combined_roi_graph.value = roi_placeholder_fig
+        
+        return roi_tab
+    
+    def _create_volume_tab(self) -> gr.Column:
+        """Create the Volume metrics tab."""
+        with gr.Column() as volume_tab:
+            refresh_volume_btn = gr.Button("Refresh Volume Data")
+            
+            with gr.Column():
+                combined_volume_graph = gr.Plot(label="Daily Volume Change (%) with 7-Day SMA", elem_id="responsive_volume_plot")
+            
+            # Toggle controls
+            with gr.Row(visible=True):
+                gr.Markdown("##### Toggle Chart Display", elem_id="volume_toggle_title")
+            
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row(elem_id="volume_toggle_container"):
+                        with gr.Column(scale=1, min_width=150):
+                            volume_bars_toggle = gr.Checkbox(label="Daily Volume Bars", value=True, elem_id="volume_bars_toggle")
+                        
+                        with gr.Column(scale=1, min_width=150):
+                            volume_sma_toggle = gr.Checkbox(label="7-Day SMA Line", value=True, elem_id="volume_sma_toggle")
+            
+            volume_status_text = gr.Textbox(label="Status", value="Ready", interactive=False)
+            
+            # Set up event handlers
+            self._setup_volume_events(refresh_volume_btn, combined_volume_graph, volume_bars_toggle, volume_sma_toggle, volume_status_text)
+            
+            # Initialize with placeholder
+            volume_placeholder_fig = self._create_placeholder_chart("Click 'Refresh Volume Data' to load Volume graph")
+            combined_volume_graph.value = volume_placeholder_fig
+        
+        return volume_tab
+    
+    def _create_performance_tab(self) -> gr.Column:
+        """Create the Performance Graph tab."""
+        with gr.Column() as performance_tab:
+            refresh_apr_hash_btn = gr.Button("Refresh APR vs Agent Hash Data")
+            
+            with gr.Column():
+                apr_vs_agent_hash_graph = gr.Plot(label="APR vs Agent Hash", elem_id="responsive_apr_hash_plot")
+            
+            apr_hash_status_text = gr.Textbox(label="Status", value="Ready", interactive=False)
+            
+            # Set up event handlers
+            self._setup_performance_events(refresh_apr_hash_btn, apr_vs_agent_hash_graph, apr_hash_status_text)
+            
+            # Initialize with placeholder
+            apr_hash_placeholder_fig = self._create_placeholder_chart("Click 'Refresh APR vs Agent Hash Data' to load APR vs Agent Hash graph")
+            apr_vs_agent_hash_graph.value = apr_hash_placeholder_fig
+        
+        return performance_tab
+    
+    def _setup_apr_events(self, refresh_btn, graph, apr_toggle, adjusted_apr_toggle, status_text):
+        """Set up event handlers for APR tab."""
+        
+        def update_apr_graph(show_apr_ma=True, show_adjusted_apr_ma=True):
+            try:
+                combined_fig, _ = generate_apr_visualizations(self.data_processor)
+                
+                # Update visibility of traces based on toggle values
+                for trace in combined_fig.data:
+                    if 'Average APR (7d window)' in trace.name and 'Adjusted' not in trace.name:
+                        trace.visible = show_apr_ma
+                    elif 'Average ETH Adjusted APR (7d window)' in trace.name:
+                        trace.visible = show_adjusted_apr_ma
+                
+                return combined_fig
+            except Exception as e:
+                logger.exception("Error generating APR visualization")
+                return self._create_error_chart(f"Error: {str(e)}")
+        
+        def refresh_apr_data():
+            try:
+                logger.info("Manually refreshing APR data...")
+                self.global_df, self.global_roi_df = self.data_processor.fetch_apr_data_from_db()
+                
+                if self.global_df is None or len(self.global_df) == 0:
+                    logger.error("Failed to fetch APR data")
+                    return graph.value, "Error: Failed to fetch APR data. Check the logs for details."
+                
+                logger.info("Generating new APR visualization...")
+                new_graph = update_apr_graph(apr_toggle.value, adjusted_apr_toggle.value)
+                return new_graph, "APR data refreshed successfully"
+            except Exception as e:
+                logger.error(f"Error refreshing APR data: {e}")
+                return graph.value, f"Error: {str(e)}"
+        
+        # Set up event handlers
+        refresh_btn.click(
+            fn=refresh_apr_data,
+            inputs=[],
+            outputs=[graph, status_text]
+        )
+        
+        apr_toggle.change(
+            fn=update_apr_graph,
+            inputs=[apr_toggle, adjusted_apr_toggle],
+            outputs=[graph]
+        )
+        
+        adjusted_apr_toggle.change(
+            fn=update_apr_graph,
+            inputs=[apr_toggle, adjusted_apr_toggle],
+            outputs=[graph]
+        )
+    
+    def _setup_roi_events(self, refresh_btn, graph, roi_toggle, status_text):
+        """Set up event handlers for ROI tab."""
+        
+        def update_roi_graph(show_roi_ma=True):
+            try:
+                combined_fig, _ = generate_roi_visualizations(self.data_processor)
+                
+                # Update visibility of traces based on toggle values
+                for trace in combined_fig.data:
+                    if trace.name == 'Average ROI (3d window)':
+                        trace.visible = show_roi_ma
+                
+                return combined_fig
+            except Exception as e:
+                logger.exception("Error generating ROI visualization")
+                return self._create_error_chart(f"Error: {str(e)}")
+        
+        def refresh_roi_data():
+            try:
+                logger.info("Manually refreshing ROI data...")
+                self.global_df, self.global_roi_df = self.data_processor.fetch_apr_data_from_db()
+                
+                if self.global_roi_df is None or len(self.global_roi_df) == 0:
+                    logger.error("Failed to fetch ROI data")
+                    return graph.value, "Error: Failed to fetch ROI data. Check the logs for details."
+                
+                logger.info("Generating new ROI visualization...")
+                new_graph = update_roi_graph(roi_toggle.value)
+                return new_graph, "ROI data refreshed successfully"
+            except Exception as e:
+                logger.error(f"Error refreshing ROI data: {e}")
+                return graph.value, f"Error: {str(e)}"
+        
+        # Set up event handlers
+        refresh_btn.click(
+            fn=refresh_roi_data,
+            inputs=[],
+            outputs=[graph, status_text]
+        )
+        
+        roi_toggle.change(
+            fn=update_roi_graph,
+            inputs=[roi_toggle],
+            outputs=[graph]
+        )
+    
+    def _setup_volume_events(self, refresh_btn, graph, volume_bars_toggle, volume_sma_toggle, status_text):
+        """Set up event handlers for Volume tab."""
+        
+        def update_volume_graph(show_bars=True, show_sma=True):
+            try:
+                combined_fig, _ = generate_volume_visualizations(self.data_processor)
+                
+                # Update visibility of traces based on toggle values
+                for trace in combined_fig.data:
+                    if trace.name == 'Daily Volume Change':
+                        trace.visible = show_bars
+                    elif trace.name == '7-Day SMA':
+                        trace.visible = show_sma
+                
+                return combined_fig
+            except Exception as e:
+                logger.exception("Error generating Volume visualization")
+                return self._create_error_chart(f"Error: {str(e)}")
+        
+        def refresh_volume_data():
+            try:
+                logger.info("Manually refreshing volume data...")
+                self.global_df, _ = self.data_processor.fetch_apr_data_from_db()
+                
+                if self.global_df is None or len(self.global_df) == 0:
+                    logger.error("Failed to fetch volume data")
+                    return graph.value, "Error: Failed to fetch volume data. Check the logs for details."
+                
+                logger.info("Generating new volume visualization...")
+                new_graph = update_volume_graph(volume_bars_toggle.value, volume_sma_toggle.value)
+                return new_graph, "Volume data refreshed successfully"
+            except Exception as e:
+                logger.error(f"Error refreshing volume data: {e}")
+                return graph.value, f"Error: {str(e)}"
+        
+        # Set up event handlers
+        refresh_btn.click(
+            fn=refresh_volume_data,
+            inputs=[],
+            outputs=[graph, status_text]
+        )
+        
+        volume_bars_toggle.change(
+            fn=update_volume_graph,
+            inputs=[volume_bars_toggle, volume_sma_toggle],
+            outputs=[graph]
+        )
+        
+        volume_sma_toggle.change(
+            fn=update_volume_graph,
+            inputs=[volume_bars_toggle, volume_sma_toggle],
+            outputs=[graph]
+        )
+    
+    def _setup_performance_events(self, refresh_btn, graph, status_text):
+        """Set up event handlers for Performance tab."""
+        
+        def update_apr_vs_agent_hash_graph():
+            try:
+                if self.global_df is None or self.global_df.empty:
+                    return self._create_error_chart("No data available. Please refresh APR data first.")
+                
+                fig, _ = generate_apr_vs_agent_hash_visualizations(self.global_df)
+                return fig
+            except Exception as e:
+                logger.exception("Error generating APR vs Agent Hash visualization")
+                return self._create_error_chart(f"Error: {str(e)}")
+        
+        def refresh_apr_vs_agent_hash_data():
+            try:
+                logger.info("Manually refreshing APR vs Agent Hash data...")
+                if self.global_df is None or self.global_df.empty:
+                    self.global_df, _ = self.data_processor.fetch_apr_data_from_db()
+                
+                if self.global_df is None or len(self.global_df) == 0:
+                    logger.error("Failed to fetch APR data for APR vs Agent Hash visualization")
+                    return graph.value, "Error: Failed to fetch APR data. Check the logs for details."
+                
+                if 'agent_hash' not in self.global_df.columns:
+                    logger.error("agent_hash column not found in DataFrame")
+                    return graph.value, "Error: agent_hash column not found in data. Check the logs for details."
+                
+                logger.info("Generating new APR vs Agent Hash visualization...")
+                new_graph = update_apr_vs_agent_hash_graph()
+                return new_graph, "APR vs Agent Hash data refreshed successfully"
+            except Exception as e:
+                logger.error(f"Error refreshing APR vs Agent Hash data: {e}")
+                return graph.value, f"Error: {str(e)}"
+        
+        # Set up event handlers
+        refresh_btn.click(
+            fn=refresh_apr_vs_agent_hash_data,
+            inputs=[],
+            outputs=[graph, status_text]
+        )
+    
+    def _create_placeholder_chart(self, message: str) -> go.Figure:
+        """Create a placeholder chart with a message."""
+        fig = go.Figure()
+        fig.add_annotation(
+            text=message, 
+            x=0.5, y=0.5, 
+            showarrow=False, 
+            font=dict(size=15)
+        )
+        return fig
+    
+    def _create_error_chart(self, message: str) -> go.Figure:
+        """Create an error chart with a message."""
+        fig = go.Figure()
+        fig.add_annotation(
+            text=message, 
+            x=0.5, y=0.5, 
+            showarrow=False, 
+            font=dict(size=15, color="red")
+        )
+        return fig
+    
+    def _add_custom_css(self):
+        """Add custom CSS for responsive design."""
+        gr.HTML("""
+        <style>
+            /* Make plots responsive */
+            #responsive_apr_plot, #responsive_roi_plot, #responsive_volume_plot, #responsive_apr_hash_plot {
+                width: 100% !important;
+                max-width: 100% !important;
+            }
+            #responsive_apr_plot > div, #responsive_roi_plot > div, #responsive_volume_plot > div, #responsive_apr_hash_plot > div {
+                width: 100% !important;
+                height: auto !important;
+                min-height: 500px !important;
+            }
+            
+            /* Toggle checkbox styling */
+            #apr_toggle .gr-checkbox {
+                accent-color: #e74c3c !important;
+            }
+            
+            #adjusted_apr_toggle .gr-checkbox {
+                accent-color: #2ecc71 !important;
+            }
+            
+            #roi_toggle .gr-checkbox {
+                accent-color: #3498db !important;
+            }
+            
+            #volume_toggle .gr-checkbox {
+                accent-color: #9b59b6 !important;
+            }
+            
+            /* Make the toggle section more compact */
+            #apr_toggle_title, #roi_toggle_title, #volume_toggle_title {
+                margin-bottom: 0;
+                margin-top: 10px;
+            }
+            
+            #apr_toggle_container, #roi_toggle_container, #volume_toggle_container {
+                margin-top: 5px;
+            }
+            
+            /* Style the checkbox labels */
+            .gr-form.gr-box {
+                border: none !important;
+                background: transparent !important;
+            }
+            
+            /* Make checkboxes and labels appear on the same line */
+            .gr-checkbox-container {
+                display: flex !important;
+                align-items: center !important;
+            }
+            
+            /* Add colored indicators */
+            #apr_toggle .gr-checkbox-label::before {
+                content: "●";
+                color: #e74c3c;
+                margin-right: 5px;
+            }
+            
+            #adjusted_apr_toggle .gr-checkbox-label::before {
+                content: "●";
+                color: #2ecc71;
+                margin-right: 5px;
+            }
+            
+            #roi_toggle .gr-checkbox-label::before {
+                content: "●";
+                color: #3498db;
+                margin-right: 5px;
+            }
+            
+            #volume_toggle .gr-checkbox-label::before {
+                content: "●";
+                color: #9b59b6;
+                margin-right: 5px;
+            }
+        </style>
+        """)
+
+
+def create_dashboard() -> gr.Blocks:
+    """Create and return the dashboard."""
+    dashboard = ModiusDashboard()
+    return dashboard.create_dashboard()

modius_performance/utils/logging_config.py

@@ -0,0 +1,63 @@
+"""
+Logging configuration for the Modius Agent Performance application.
+"""
+import logging
+import logging.handlers
+from typing import Optional
+
+from ..config.constants import LOGGING_CONFIG
+
+
+def setup_logging(level: Optional[str] = None, log_file: Optional[str] = None) -> None:
+    """Set up logging configuration."""
+    log_level = level or LOGGING_CONFIG['level']
+    log_file = log_file or LOGGING_CONFIG['file']
+    log_format = LOGGING_CONFIG['format']
+    
+    # Convert string level to logging constant
+    if isinstance(log_level, str):
+        log_level = getattr(logging, log_level.upper())
+    
+    # Create logger
+    logger = logging.getLogger()
+    logger.setLevel(log_level)
+    
+    # Clear existing handlers
+    logger.handlers.clear()
+    
+    # Create formatter
+    formatter = logging.Formatter(log_format)
+    
+    # Console handler
+    console_handler = logging.StreamHandler()
+    console_handler.setLevel(log_level)
+    console_handler.setFormatter(formatter)
+    logger.addHandler(console_handler)
+    
+    # File handler with rotation
+    if log_file:
+        file_handler = logging.handlers.RotatingFileHandler(
+            log_file, 
+            maxBytes=10*1024*1024,  # 10MB
+            backupCount=5
+        )
+        file_handler.setLevel(log_level)
+        file_handler.setFormatter(formatter)
+        logger.addHandler(file_handler)
+    
+    # Reduce third-party library logging
+    logging.getLogger("urllib3").setLevel(logging.WARNING)
+    logging.getLogger("httpx").setLevel(logging.WARNING)
+    logging.getLogger("matplotlib").setLevel(logging.WARNING)
+    logging.getLogger("requests").setLevel(logging.WARNING)
+    
+    # Log startup information
+    logger.info("============= APPLICATION STARTING =============")
+    logger.info(f"Logging level set to: {logging.getLevelName(log_level)}")
+    if log_file:
+        logger.info(f"Logging to file: {log_file}")
+
+
+def get_logger(name: str) -> logging.Logger:
+    """Get a logger instance."""
+    return logging.getLogger(name)

modius_performance/visualization/apr_charts.py

@@ -0,0 +1,395 @@
+"""
+APR chart implementations.
+"""
+import plotly.graph_objects as go
+import pandas as pd
+import logging
+from datetime import datetime
+from typing import Dict, Any, Optional, Tuple
+
+from ..config.constants import DATE_RANGES, Y_AXIS_RANGES, FILE_PATHS
+from .base_chart import BaseChart
+
+logger = logging.getLogger(__name__)
+
+
+class APRChart(BaseChart):
+    """Chart for APR visualizations."""
+    
+    def create_chart(self, df: pd.DataFrame, **kwargs) -> go.Figure:
+        """Create APR time series chart."""
+        if df.empty:
+            return self._create_empty_chart("No APR data available")
+        
+        # Filter for APR data only
+        apr_data = df[df['metric_type'] == 'APR'].copy()
+        if apr_data.empty:
+            return self._create_empty_chart("No APR data available")
+        
+        # Apply daily median aggregation to reduce outliers
+        apr_data = self.data_processor.aggregate_daily_medians(apr_data, ['apr', 'adjusted_apr'])
+        if apr_data.empty:
+            return self._create_empty_chart("No APR data available after aggregation")
+        
+        # Apply high APR filtering (400% threshold) with forward filling
+        apr_data, _ = self.data_processor.filter_high_apr_values(apr_data)
+        if apr_data.empty:
+            return self._create_empty_chart("No APR data available after high APR filtering")
+        
+        # Save processed APR data for verification (after all processing steps)
+        processed_csv_path = self.data_processor.save_to_csv(apr_data, FILE_PATHS['apr_processed_csv'])
+        if processed_csv_path:
+            logger.info(f"Saved processed APR data to {processed_csv_path} for verification")
+            logger.info(f"Processed APR data contains {len(apr_data)} rows after agent exclusion, zero filtering, daily median aggregation, and high APR filtering")
+        
+        # Filter outliers (disabled but keeping for compatibility)
+        apr_data = self._filter_outliers(apr_data, 'apr')
+        
+        # Get time range
+        min_time = apr_data['timestamp'].min()
+        max_time = apr_data['timestamp'].max()
+        x_start_date = DATE_RANGES['apr_start']
+        
+        # Create figure
+        fig = self._create_base_figure()
+        
+        # Add background shapes
+        y_range = Y_AXIS_RANGES['apr']
+        self._add_background_shapes(fig, min_time, max_time, y_range['min'], y_range['max'])
+        self._add_zero_line(fig, min_time, max_time)
+        
+        # Calculate moving averages
+        avg_apr_data = self._calculate_moving_average(apr_data, 'apr')
+        
+        # Add individual agent data points
+        unique_agents = apr_data['agent_name'].unique()
+        color_map = self._get_color_map(unique_agents)
+        self._add_agent_data_points(fig, apr_data, 'apr', color_map)
+        
+        # Add APR moving average line
+        self._add_moving_average_line(
+            fig, avg_apr_data, 'apr', 
+            'Average APR (7d window)', 
+            self.colors['apr'], 
+            width=3
+        )
+        
+        # Add adjusted APR moving average if available
+        if 'adjusted_apr' in apr_data.columns and apr_data['adjusted_apr'].notna().any():
+            # Calculate adjusted APR moving average
+            adjusted_avg_data = self._calculate_moving_average(apr_data, 'adjusted_apr')
+            
+            # Handle missing values with forward fill (fix the column name)
+            import warnings
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore", FutureWarning)
+                # Fix: Use the correct column name 'moving_avg' not 'adjusted_moving_avg'
+                adjusted_avg_data['moving_avg'] = adjusted_avg_data['moving_avg'].ffill()
+            
+            self._add_moving_average_line(
+                fig, adjusted_avg_data, 'adjusted_apr',
+                'Average ETH Adjusted APR (7d window)',
+                self.colors['adjusted_apr'],
+                width=3
+            )
+        
+        # Update layout and axes
+        self._update_layout(
+            fig, 
+            title="Modius Agents",
+            y_axis_title=None,  # Remove single y-axis title to use region-specific labels
+            y_range=[y_range['min'], y_range['max']]
+        )
+        
+        # Find last valid date for x-axis range
+        last_valid_date = avg_apr_data['timestamp'].max() if not avg_apr_data.empty else max_time
+        
+        self._update_axes(
+            fig,
+            x_range=[x_start_date, last_valid_date],
+            y_range=[y_range['min'], y_range['max']]
+        )
+        
+        # Add region-specific annotations for positive and negative areas
+        self._add_region_annotations(fig, y_range)
+        
+        # Save chart
+        self._save_chart(
+            fig, 
+            FILE_PATHS['apr_graph_html'], 
+            FILE_PATHS['apr_graph_png']
+        )
+        
+        return fig
+    
+    def _add_region_annotations(self, fig: go.Figure, y_range: Dict[str, float]) -> None:
+        """Add annotations for positive and negative regions."""
+        # Annotation for negative region
+        fig.add_annotation(
+            x=-0.08,
+            y=-25,
+            xref="paper",
+            yref="y",
+            text="Percent drawdown [%]",
+            showarrow=False,
+            font=dict(
+                size=16, 
+                family=self.config['font_family'], 
+                color="black", 
+                weight="bold"
+            ),
+            textangle=-90,
+            align="center"
+        )
+        
+        # Annotation for positive region
+        fig.add_annotation(
+            x=-0.08,
+            y=50,
+            xref="paper",
+            yref="y",
+            text="Agent APR [%]",
+            showarrow=False,
+            font=dict(
+                size=16, 
+                family=self.config['font_family'], 
+                color="black", 
+                weight="bold"
+            ),
+            textangle=-90,
+            align="center"
+        )
+
+
+class APRHashChart(BaseChart):
+    """Chart for APR vs Agent Hash visualizations."""
+    
+    def create_chart(self, df: pd.DataFrame, **kwargs) -> go.Figure:
+        """Create APR vs agent hash bar chart."""
+        if df.empty:
+            return self._create_empty_chart("No agent hash data available")
+        
+        # Data is already filtered and processed by the calling function
+        # Just filter for data with agent hash
+        apr_data = df[df['agent_hash'].notna()].copy()
+        if apr_data.empty:
+            return self._create_empty_chart("No valid APR data with agent_hash found")
+        
+        logger.info(f"APR Hash Chart: Using {len(apr_data)} processed data points")
+        
+        # Create figure
+        fig = self._create_base_figure()
+        
+        # Get unique hashes and create version mapping
+        unique_hashes = apr_data['agent_hash'].unique()
+        version_map = self._create_version_map(unique_hashes)
+        
+        # Sort hashes by version
+        sorted_hashes = sorted(unique_hashes, key=lambda h: "1" if h.endswith("tby") else "2" if h.endswith("vq") else h)
+        
+        # Add zero line for bar chart
+        self._add_zero_line(fig, -0.5, len(version_map) - 0.5)
+        
+        # Version colors
+        version_colors = {
+            "v0.4.1": "rgba(31, 119, 180, 0.7)",
+            "v0.4.2": "rgba(44, 160, 44, 0.7)",
+        }
+        default_color = "rgba(214, 39, 40, 0.7)"
+        
+        # Aggregate data by version for bar chart
+        version_data = {}
+        version_stats = {}
+        
+        # Aggregate data by version
+        for agent_hash in sorted_hashes:
+            hash_data = apr_data[apr_data['agent_hash'] == agent_hash]
+            version = version_map[agent_hash]
+            
+            # Calculate statistics
+            apr_values = hash_data['apr'].tolist()
+            
+            # Store statistics for version comparison
+            if version not in version_stats:
+                version_stats[version] = {'apr_values': [], 'count': 0, 'hashes': []}
+            
+            version_stats[version]['apr_values'].extend(apr_values)
+            version_stats[version]['count'] += len(apr_values)
+            version_stats[version]['hashes'].append(agent_hash)
+        
+        # Create bar chart data
+        versions = list(version_stats.keys())
+        medians = []
+        colors = []
+        hover_texts = []
+        
+        for version in versions:
+            # Calculate median APR for this version
+            all_values = version_stats[version]['apr_values']
+            median_apr = pd.Series(all_values).median()
+            medians.append(median_apr)
+            
+            # Choose color
+            color = version_colors.get(version, default_color)
+            colors.append(color)
+            
+            # Create hover text
+            count = version_stats[version]['count']
+            mean_apr = pd.Series(all_values).mean()
+            min_apr = pd.Series(all_values).min()
+            max_apr = pd.Series(all_values).max()
+            
+            hover_text = (
+                f"Version: {version}<br>"
+                f"Median APR: {median_apr:.2f}%<br>"
+                f"Mean APR: {mean_apr:.2f}%<br>"
+                f"Min APR: {min_apr:.2f}%<br>"
+                f"Max APR: {max_apr:.2f}%<br>"
+                f"Data points: {count}"
+            )
+            hover_texts.append(hover_text)
+        
+        # Add bar chart
+        fig.add_trace(
+            go.Bar(
+                x=versions,
+                y=medians,
+                marker=dict(
+                    color=colors,
+                    line=dict(width=1, color='black')
+                ),
+                hoverinfo='text',
+                hovertext=hover_texts,
+                showlegend=False,
+                name="Median APR"
+            )
+        )
+        
+        # Add median value annotations on top of bars
+        for i, (version, median_apr) in enumerate(zip(versions, medians)):
+            fig.add_annotation(
+                x=version,
+                y=median_apr + (max(medians) * 0.05),  # 5% above the bar
+                text=f"{median_apr:.1f}%",
+                showarrow=False,
+                font=dict(
+                    family=self.config['font_family'],
+                    size=14,
+                    color="black",
+                    weight="bold"
+                )
+            )
+        
+        # Add version comparison annotation
+        self._add_version_comparison(fig, version_stats, len(versions))
+        
+        # Update layout
+        self._update_layout(
+            fig,
+            title="Performance Graph",
+            height=900
+        )
+        
+        # Update axes
+        self._update_axes(fig, y_auto=True)
+        
+        # Update x-axis for bar chart
+        fig.update_xaxes(
+            tickangle=-45
+        )
+        
+        # Save chart
+        self._save_chart(
+            fig,
+            FILE_PATHS['apr_hash_graph_html'],
+            FILE_PATHS['apr_hash_graph_png']
+        )
+        
+        return fig
+    
+    def _create_version_map(self, hashes: list) -> Dict[str, str]:
+        """Create version mapping for agent hashes."""
+        version_map = {}
+        for hash_val in hashes:
+            if hash_val.endswith("tby"):
+                version_map[hash_val] = "v0.4.1"
+            elif hash_val.endswith("vq"):
+                version_map[hash_val] = "v0.4.2"
+            else:
+                version_map[hash_val] = f"Hash: {hash_val[-6:]}"
+        return version_map
+    
+    def _add_version_comparison(self, fig: go.Figure, version_stats: Dict, num_hashes: int) -> None:
+        """Add version comparison annotation."""
+        if "v0.4.1" in version_stats and "v0.4.2" in version_stats:
+            v041_values = version_stats["v0.4.1"]["apr_values"]
+            v042_values = version_stats["v0.4.2"]["apr_values"]
+            
+            v041_median = pd.Series(v041_values).median()
+            v042_median = pd.Series(v042_values).median()
+            
+            improvement = v042_median - v041_median
+            change_text = "improvement" if improvement > 0 else "decrease"
+            
+            fig.add_annotation(
+                x=(num_hashes - 1) / 2,
+                y=90,
+                text=f"<b>Version Comparison:</b> {abs(improvement):.2f}% {change_text} from v0.4.1 to v0.4.2",
+                showarrow=False,
+                font=dict(
+                    family=self.config['font_family'],
+                    size=16,
+                    color="black",
+                    weight="bold"
+                ),
+                bgcolor="rgba(255, 255, 255, 0.9)",
+                bordercolor="black",
+                borderwidth=2,
+                borderpad=6,
+                opacity=0.9
+            )
+
+
+def generate_apr_visualizations(data_processor=None) -> Tuple[go.Figure, Optional[str]]:
+    """Generate APR visualizations."""
+    from ..data.data_processor import DataProcessor
+    
+    if data_processor is None:
+        data_processor = DataProcessor()
+    
+    # Fetch data
+    apr_df, _ = data_processor.fetch_apr_data_from_db()
+    
+    # Create chart
+    apr_chart = APRChart(data_processor)
+    fig, csv_path = apr_chart.generate_visualization(
+        apr_df, 
+        csv_filename=FILE_PATHS['apr_csv']
+    )
+    
+    return fig, csv_path
+
+
+def generate_apr_vs_agent_hash_visualizations(df: pd.DataFrame) -> Tuple[go.Figure, Optional[str]]:
+    """Generate APR vs agent hash visualizations."""
+    from ..data.data_processor import DataProcessor
+    
+    data_processor = DataProcessor()
+    
+    # Use the same processed data as the APR time series chart
+    # First apply the same filtering pipeline to get consistent data
+    apr_data = df[df['metric_type'] == 'APR'].copy()
+    if not apr_data.empty:
+        # Apply daily median aggregation
+        apr_data = data_processor.aggregate_daily_medians(apr_data, ['apr', 'adjusted_apr'])
+        # Apply high APR filtering
+        apr_data, _ = data_processor.filter_high_apr_values(apr_data)
+    
+    # Create chart using the processed data
+    apr_hash_chart = APRHashChart(data_processor)
+    fig, csv_path = apr_hash_chart.generate_visualization(
+        apr_data,
+        csv_filename=FILE_PATHS['apr_hash_csv']
+    )
+    
+    return fig, csv_path

modius_performance/visualization/base_chart.py

@@ -0,0 +1,365 @@
+"""
+Base chart class for creating visualizations.
+"""
+import plotly.graph_objects as go
+import plotly.express as px
+import pandas as pd
+import logging
+from datetime import datetime
+from typing import List, Dict, Any, Optional, Tuple
+from abc import ABC, abstractmethod
+
+from ..config.constants import CHART_CONFIG, CHART_COLORS, Y_AXIS_RANGES, FILE_PATHS
+from ..data.data_processor import DataProcessor
+
+logger = logging.getLogger(__name__)
+
+
+class BaseChart(ABC):
+    """Base class for all chart visualizations."""
+    
+    def __init__(self, data_processor: DataProcessor = None):
+        self.data_processor = data_processor or DataProcessor()
+        self.config = CHART_CONFIG
+        self.colors = CHART_COLORS
+        self.y_ranges = Y_AXIS_RANGES
+        self.file_paths = FILE_PATHS
+    
+    @abstractmethod
+    def create_chart(self, df: pd.DataFrame, **kwargs) -> go.Figure:
+        """Create the chart visualization."""
+        pass
+    
+    def _create_base_figure(self) -> go.Figure:
+        """Create a base figure with common settings."""
+        return go.Figure()
+    
+    def _add_background_shapes(self, fig: go.Figure, min_time: datetime, max_time: datetime, 
+                              y_min: float, y_max: float) -> None:
+        """Add background shapes for positive and negative regions."""
+        # Add shape for positive region (above zero)
+        fig.add_shape(
+            type="rect",
+            fillcolor=self.colors['positive_region'],
+            line=dict(width=0),
+            y0=0, y1=y_max,
+            x0=min_time, x1=max_time,
+            layer="below"
+        )
+        
+        # Add shape for negative region (below zero)
+        fig.add_shape(
+            type="rect",
+            fillcolor=self.colors['negative_region'],
+            line=dict(width=0),
+            y0=y_min, y1=0,
+            x0=min_time, x1=max_time,
+            layer="below"
+        )
+    
+    def _add_zero_line(self, fig: go.Figure, min_time: datetime, max_time: datetime) -> None:
+        """Add a zero line to the chart."""
+        fig.add_shape(
+            type="line",
+            line=dict(dash="solid", width=1.5, color=self.colors['zero_line']),
+            y0=0, y1=0,
+            x0=min_time, x1=max_time
+        )
+    
+    def _update_layout(self, fig: go.Figure, title: str, y_axis_title: str = None, 
+                      height: int = None, y_range: List[float] = None) -> None:
+        """Update the figure layout with common settings."""
+        fig.update_layout(
+            title=dict(
+                text=title,
+                font=dict(
+                    family=self.config['font_family'],
+                    size=self.config['title_size'],
+                    color="black",
+                    weight="bold"
+                )
+            ),
+            xaxis_title=None,
+            yaxis_title=None,
+            template=self.config['template'],
+            height=height or self.config['height'],
+            autosize=True,
+            legend=dict(
+                orientation="h",
+                yanchor="bottom",
+                y=1.02,
+                xanchor="right",
+                x=1,
+                groupclick="toggleitem",
+                font=dict(
+                    family=self.config['font_family'],
+                    size=self.config['legend_font_size'],
+                    color="black",
+                    weight="bold"
+                )
+            ),
+            margin=dict(r=30, l=120, t=40, b=50),
+            hovermode="closest"
+        )
+        
+        # Add y-axis annotation if provided
+        if y_axis_title:
+            fig.add_annotation(
+                x=-0.08,
+                y=0 if y_range is None else (y_range[0] + y_range[1]) / 2,
+                xref="paper",
+                yref="y",
+                text=y_axis_title,
+                showarrow=False,
+                font=dict(
+                    size=16, 
+                    family=self.config['font_family'], 
+                    color="black", 
+                    weight="bold"
+                ),
+                textangle=-90,
+                align="center"
+            )
+    
+    def _update_axes(self, fig: go.Figure, x_range: List[datetime] = None, 
+                    y_range: List[float] = None, y_auto: bool = False) -> None:
+        """Update the axes with common settings."""
+        # Update y-axis
+        y_axis_config = {
+            'showgrid': True,
+            'gridwidth': 1,
+            'gridcolor': 'rgba(0,0,0,0.1)',
+            'tickformat': ".2f",
+            'tickfont': dict(
+                size=self.config['axis_font_size'], 
+                family=self.config['font_family'], 
+                color="black", 
+                weight="bold"
+            ),
+            'title': None
+        }
+        
+        if y_auto:
+            y_axis_config['autorange'] = True
+        elif y_range:
+            y_axis_config['autorange'] = False
+            y_axis_config['range'] = y_range
+        
+        fig.update_yaxes(**y_axis_config)
+        
+        # Update x-axis
+        x_axis_config = {
+            'showgrid': True,
+            'gridwidth': 1,
+            'gridcolor': 'rgba(0,0,0,0.1)',
+            'tickformat': "%b %d",
+            'tickangle': -30,
+            'tickfont': dict(
+                size=self.config['axis_font_size'], 
+                family=self.config['font_family'], 
+                color="black", 
+                weight="bold"
+            ),
+            'title': None
+        }
+        
+        if x_range:
+            x_axis_config['autorange'] = False
+            x_axis_config['range'] = x_range
+        
+        fig.update_xaxes(**x_axis_config)
+    
+    def _add_agent_data_points(self, fig: go.Figure, df: pd.DataFrame, value_column: str,
+                              color_map: Dict[str, str], max_visible: int = None) -> None:
+        """Add individual agent data points to the chart."""
+        if df.empty:
+            return
+        
+        unique_agents = df['agent_name'].unique()
+        max_visible = max_visible or self.config['max_visible_agents']
+        
+        # Calculate agent activity to determine which to show by default
+        agent_counts = df['agent_name'].value_counts()
+        top_agents = agent_counts.nlargest(min(max_visible, len(agent_counts))).index.tolist()
+        
+        logger.info(f"Showing {len(top_agents)} agents by default out of {len(unique_agents)} total agents")
+        
+        for agent_name in unique_agents:
+            agent_data = df[df['agent_name'] == agent_name]
+            
+            x_values = agent_data['timestamp'].tolist()
+            y_values = agent_data[value_column].tolist()
+            
+            # Determine visibility
+            is_visible = False  # Hide all agent data points by default
+            
+            fig.add_trace(
+                go.Scatter(
+                    x=x_values,
+                    y=y_values,
+                    mode='markers',
+                    marker=dict(
+                        color=color_map.get(agent_name, 'gray'),
+                        symbol='circle',
+                        size=10,
+                        line=dict(width=1, color='black')
+                    ),
+                    name=f'Agent: {agent_name} ({value_column.upper()})',
+                    hovertemplate=f'Time: %{{x}}<br>{value_column.upper()}: %{{y:.2f}}<br>Agent: {agent_name}<extra></extra>',
+                    visible=is_visible
+                )
+            )
+            logger.info(f"Added {value_column} data points for agent {agent_name} with {len(x_values)} points (visible: {is_visible})")
+    
+    def _add_moving_average_line(self, fig: go.Figure, avg_data: pd.DataFrame, 
+                                value_column: str, line_name: str, color: str, 
+                                width: int = 2, hover_data: List[str] = None) -> None:
+        """Add a moving average line to the chart."""
+        if avg_data.empty or 'moving_avg' not in avg_data.columns:
+            return
+        
+        # Filter out NaT values before processing - be more aggressive
+        clean_data = avg_data.copy()
+        
+        # Remove rows with NaT timestamps more comprehensively
+        clean_data = clean_data.dropna(subset=['timestamp'])
+        clean_data = clean_data[clean_data['timestamp'].notna()]
+        clean_data = clean_data[~clean_data['timestamp'].isnull()]
+        
+        # Additional check for pandas NaT specifically
+        if hasattr(pd, 'NaT'):
+            clean_data = clean_data[clean_data['timestamp'] != pd.NaT]
+        
+        # Also filter out NaN moving averages
+        clean_data = clean_data.dropna(subset=['moving_avg'])
+        clean_data = clean_data[clean_data['moving_avg'].notna()]
+        
+        if clean_data.empty:
+            logger.warning("No valid timestamps found for " + str(line_name))
+            return
+        
+        x_values = clean_data['timestamp'].tolist()
+        y_values = clean_data['moving_avg'].tolist()
+        
+        # Create hover text without any f-strings to avoid strftime issues
+        if hover_data:
+            hover_text = hover_data
+        else:
+            hover_text = []
+            for _, row in clean_data.iterrows():
+                try:
+                    # Convert timestamp to string safely
+                    ts = row['timestamp']
+                    
+                    # More comprehensive NaT checking
+                    if pd.isna(ts) or pd.isnull(ts) or (hasattr(pd, 'NaT') and ts is pd.NaT):
+                        time_str = "Invalid Date"
+                    elif hasattr(ts, 'strftime'):
+                        try:
+                            time_str = ts.strftime('%Y-%m-%d %H:%M:%S')
+                        except (ValueError, TypeError):
+                            time_str = str(ts)
+                    else:
+                        time_str = str(ts)
+                    
+                    # Build hover text using string concatenation only
+                    hover_line = "Time: " + time_str + "<br>"
+                    
+                    # Safely format moving average value
+                    try:
+                        avg_val = row['moving_avg']
+                        if pd.isna(avg_val) or pd.isnull(avg_val):
+                            avg_str = "N/A"
+                        else:
+                            avg_str = "{:.2f}".format(float(avg_val))
+                    except (ValueError, TypeError):
+                        avg_str = "N/A"
+                    
+                    hover_line += "Avg " + value_column.upper() + " (7d window): " + avg_str
+                    hover_text.append(hover_line)
+                    
+                except Exception as e:
+                    logger.warning("Error formatting timestamp for hover text: " + str(e))
+                    # Fallback hover text
+                    hover_line = "Time: Invalid Date<br>"
+                    hover_line += "Avg " + value_column.upper() + " (3d window): N/A"
+                    hover_text.append(hover_line)
+        
+        fig.add_trace(
+            go.Scatter(
+                x=x_values,
+                y=y_values,
+                mode='lines',
+                line=dict(color=color, width=width, shape='spline', smoothing=1.3),
+                name=line_name,
+                hovertext=hover_text,
+                hoverinfo='text',
+                visible=True
+            )
+        )
+        logger.info("Added moving average line '" + str(line_name) + "' with " + str(len(x_values)) + " points")
+    
+    def _filter_outliers(self, df: pd.DataFrame, column: str) -> pd.DataFrame:
+        """Filter outliers from the data - DISABLED: Return data unchanged."""
+        # Outlier filtering disabled - return original data
+        logger.info(f"Outlier filtering disabled for {column} column - returning all data")
+        return df
+    
+    def _calculate_moving_average(self, df: pd.DataFrame, value_column: str) -> pd.DataFrame:
+        """Calculate moving average for the data."""
+        return self.data_processor.calculate_moving_average(df, value_column)
+    
+    def _save_chart(self, fig: go.Figure, html_filename: str, png_filename: str = None) -> None:
+        """Save the chart to HTML and optionally PNG."""
+        try:
+            fig.write_html(html_filename, include_plotlyjs='cdn', full_html=False)
+            logger.info(f"Chart saved to {html_filename}")
+            
+            if png_filename:
+                try:
+                    fig.write_image(png_filename)
+                    logger.info(f"Chart also saved to {png_filename}")
+                except Exception as e:
+                    logger.error(f"Error saving PNG image: {e}")
+                    logger.info(f"Chart saved to {html_filename} only")
+        except Exception as e:
+            logger.error(f"Error saving chart: {e}")
+    
+    def generate_visualization(self, df: pd.DataFrame, **kwargs) -> Tuple[go.Figure, Optional[str]]:
+        """Generate the complete visualization including chart and CSV export."""
+        if df.empty:
+            logger.info("No data available for visualization.")
+            fig = self._create_empty_chart("No data available")
+            return fig, None
+        
+        # Create the chart
+        fig = self.create_chart(df, **kwargs)
+        
+        # Save to CSV
+        csv_filename = kwargs.get('csv_filename')
+        if csv_filename:
+            csv_path = self.data_processor.save_to_csv(df, csv_filename)
+        else:
+            csv_path = None
+        
+        return fig, csv_path
+    
+    def _create_empty_chart(self, message: str) -> go.Figure:
+        """Create an empty chart with a message."""
+        fig = go.Figure()
+        fig.add_annotation(
+            x=0.5, y=0.5,
+            text=message,
+            font=dict(size=20),
+            showarrow=False
+        )
+        fig.update_layout(
+            xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+            yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
+        )
+        return fig
+    
+    def _get_color_map(self, agents: List[str]) -> Dict[str, str]:
+        """Generate a color map for agents."""
+        colors = px.colors.qualitative.Plotly[:len(agents)]
+        return {agent: colors[i % len(colors)] for i, agent in enumerate(agents)}

modius_performance/visualization/roi_charts.py

@@ -0,0 +1,150 @@
+"""
+ROI chart implementations.
+"""
+import plotly.graph_objects as go
+import pandas as pd
+import logging
+from datetime import datetime
+from typing import Dict, Any, Optional, Tuple
+
+from ..config.constants import DATE_RANGES, Y_AXIS_RANGES, FILE_PATHS
+from .base_chart import BaseChart
+
+logger = logging.getLogger(__name__)
+
+
+class ROIChart(BaseChart):
+    """Chart for ROI visualizations."""
+    
+    def create_chart(self, df: pd.DataFrame, **kwargs) -> go.Figure:
+        """Create ROI time series chart."""
+        if df.empty:
+            return self._create_empty_chart("No ROI data available")
+        
+        # Filter for ROI data only
+        roi_data = df[df['metric_type'] == 'ROI'].copy()
+        if roi_data.empty:
+            return self._create_empty_chart("No ROI data available")
+        
+        # ROI data is already in percentage format from corrected CSV
+        # No conversion needed
+        
+        # Apply daily median aggregation to reduce outliers
+        roi_data = self.data_processor.aggregate_daily_medians(roi_data, ['roi'])
+        if roi_data.empty:
+            return self._create_empty_chart("No ROI data available after aggregation")
+        
+        # Apply high APR filtering to ROI data (need to get APR data first to determine what to filter)
+        # Fetch APR data to determine which agent-timestamp combinations to filter
+        apr_df, _ = self.data_processor.fetch_apr_data_from_db()
+        if not apr_df.empty:
+            # Apply same processing to APR data to get the filtering reference
+            apr_processed = apr_df[apr_df['metric_type'] == 'APR'].copy()
+            apr_processed = self.data_processor.aggregate_daily_medians(apr_processed, ['apr', 'adjusted_apr'])
+            
+            # Apply high APR filtering to both datasets
+            _, roi_data = self.data_processor.filter_high_apr_values(apr_processed, roi_data)
+        
+        if roi_data.empty:
+            return self._create_empty_chart("No ROI data available after high APR filtering")
+        
+        # Save processed ROI data for verification (after all processing steps)
+        processed_csv_path = self.data_processor.save_to_csv(roi_data, FILE_PATHS['roi_processed_csv'])
+        if processed_csv_path:
+            logger.info(f"Saved processed ROI data to {processed_csv_path} for verification")
+            logger.info(f"Processed ROI data contains {len(roi_data)} rows after agent exclusion, daily median aggregation, and high APR filtering")
+        
+        # Filter outliers (disabled but keeping for compatibility)
+        roi_data = self._filter_outliers(roi_data, 'roi')
+        
+        # Get time range
+        min_time = roi_data['timestamp'].min()
+        max_time = roi_data['timestamp'].max()
+        x_start_date = min_time  # Use actual start date for ROI
+        
+        # Calculate average runtime for title
+        fixed_start_date = DATE_RANGES['feb_start']
+        agent_runtimes = {}
+        for agent_id in roi_data['agent_id'].unique():
+            agent_data = roi_data[roi_data['agent_id'] == agent_id]
+            agent_name = agent_data['agent_name'].iloc[0]
+            last_report = agent_data['timestamp'].max()
+            runtime_days = (last_report - fixed_start_date).total_seconds() / (24 * 3600)
+            agent_runtimes[agent_id] = {
+                'agent_name': agent_name,
+                'last_report': last_report,
+                'runtime_days': runtime_days
+            }
+        
+        avg_runtime = sum(data['runtime_days'] for data in agent_runtimes.values()) / len(agent_runtimes) if agent_runtimes else 0
+        
+        # Create figure
+        fig = self._create_base_figure()
+        
+        # Add background shapes
+        y_range = Y_AXIS_RANGES['roi']
+        self._add_background_shapes(fig, min_time, max_time, y_range['min'], y_range['max'])
+        self._add_zero_line(fig, min_time, max_time)
+        
+        # Calculate moving averages
+        avg_roi_data = self._calculate_moving_average(roi_data, 'roi')
+        
+        # Add individual agent data points
+        unique_agents = roi_data['agent_name'].unique()
+        color_map = self._get_color_map(unique_agents)
+        self._add_agent_data_points(fig, roi_data, 'roi', color_map)
+        
+        # Add ROI moving average line
+        self._add_moving_average_line(
+            fig, avg_roi_data, 'roi', 
+            'Average ROI (3d window)', 
+            self.colors['roi'], 
+            width=2
+        )
+        
+        # Update layout and axes
+        title = f"Modius Agents ROI (over avg. {avg_runtime:.1f} days runtime)"
+        self._update_layout(
+            fig, 
+            title=title,
+            y_axis_title="ROI [%]",
+            y_range=[y_range['min'], y_range['max']]
+        )
+        
+        # Find last valid date for x-axis range
+        last_valid_date = avg_roi_data['timestamp'].max() if not avg_roi_data.empty else max_time
+        
+        self._update_axes(
+            fig,
+            x_range=[x_start_date, last_valid_date],
+            y_range=[y_range['min'], y_range['max']]
+        )
+        
+        # Save chart
+        self._save_chart(
+            fig, 
+            FILE_PATHS['roi_graph_html'], 
+            FILE_PATHS['roi_graph_png']
+        )
+        
+        return fig
+
+
+def generate_roi_visualizations(data_processor=None) -> Tuple[go.Figure, Optional[str]]:
+    """Generate ROI visualizations."""
+    from ..data.data_processor import DataProcessor
+    
+    if data_processor is None:
+        data_processor = DataProcessor()
+    
+    # Fetch data
+    _, roi_df = data_processor.fetch_apr_data_from_db()
+    
+    # Create chart
+    roi_chart = ROIChart(data_processor)
+    fig, csv_path = roi_chart.generate_visualization(
+        roi_df, 
+        csv_filename=FILE_PATHS['roi_csv']
+    )
+    
+    return fig, csv_path

modius_performance/visualization/volume_charts.py

@@ -0,0 +1,138 @@
+"""
+Volume chart implementations.
+"""
+import plotly.graph_objects as go
+import pandas as pd
+import logging
+from datetime import datetime
+from typing import Dict, Any, Optional, Tuple
+
+from ..config.constants import Y_AXIS_RANGES, FILE_PATHS
+from .base_chart import BaseChart
+
+logger = logging.getLogger(__name__)
+
+
+class VolumeChart(BaseChart):
+    """Chart for Volume visualizations."""
+    
+    def create_chart(self, df: pd.DataFrame, **kwargs) -> go.Figure:
+        """Create Cumulative Volume bar chart."""
+        if df.empty:
+            return self._create_empty_chart("No volume data available")
+        
+        # Calculate daily volume changes using the new method
+        daily_data = self.data_processor.calculate_daily_volume_changes(df)
+        if daily_data.empty:
+            return self._create_empty_chart("No daily volume data available")
+        
+        # Create figure
+        fig = self._create_base_figure()
+        
+        # Get time range
+        min_time = daily_data['date'].min()
+        max_time = daily_data['date'].max()
+        
+        # Determine bar colors based on daily volume (all bars will be blue for cumulative volume)
+        bar_colors = ['blue'] * len(daily_data)
+        
+        # Create custom hover text for bars
+        bar_hover_text = []
+        for _, row in daily_data.iterrows():
+            date_str = row['date'].strftime('%Y-%m-%d')
+            bar_hover_text.append(
+                f"Date: {date_str}<br>"
+                f"Daily Volume: {row['volume']:.2f}<br>"
+                f"Cumulative Volume: {row['cumulative_volume']:.2f}<br>"
+                f"Day-over-Day Change: {row['day_over_day_pct']:.1f}%<br>"
+                f"7-Day SMA: {row['sma_7d']:.1f}%"
+            )
+        
+        # Add cumulative volume bar chart
+        fig.add_trace(
+            go.Bar(
+                x=daily_data['date'],
+                y=daily_data['cumulative_volume'],
+                marker=dict(
+                    color=bar_colors,
+                    line=dict(width=1, color='black'),
+                    opacity=0.7
+                ),
+                name='Daily Volume Change',
+                hovertext=bar_hover_text,
+                hoverinfo='text',
+                customdata=daily_data[['volume', 'day_over_day_pct']].values
+            )
+        )
+        
+        # Add 7-Day SMA line
+        fig.add_trace(
+            go.Scatter(
+                x=daily_data['date'],
+                y=daily_data['sma_7d'],
+                mode='lines',
+                line=dict(color='red', width=3),
+                name='7-Day SMA',
+                hovertemplate='Date: %{x}<br>7-Day SMA: %{y:.1f}%<extra></extra>',
+                yaxis='y2'
+            )
+        )
+        
+        # Update layout and axes
+        self._update_layout(
+            fig, 
+            title="Daily Volume Change (%) with 7-Day SMA",
+            y_axis_title="Cumulative Volume"
+        )
+        
+        # Update axes
+        self._update_axes(
+            fig,
+            x_range=[min_time, max_time],
+            y_auto=True
+        )
+        
+        # Update primary y-axis to show volume format
+        fig.update_yaxes(tickformat=".2f")
+        
+        # Add secondary y-axis for SMA
+        fig.update_layout(
+            yaxis2=dict(
+                title="Percentage Change (%)",
+                overlaying='y',
+                side='right',
+                tickformat=".1f"
+            )
+        )
+        
+        # Save chart
+        self._save_chart(
+            fig, 
+            FILE_PATHS['volume_graph_html'], 
+            FILE_PATHS['volume_graph_png']
+        )
+        
+        return fig
+
+
+def generate_volume_visualizations(data_processor=None) -> Tuple[go.Figure, Optional[str]]:
+    """Generate Volume visualizations."""
+    from ..data.data_processor import DataProcessor
+    
+    if data_processor is None:
+        data_processor = DataProcessor()
+    
+    # Fetch data
+    apr_df, _ = data_processor.fetch_apr_data_from_db()
+    
+    # Filter for records with volume data
+    volume_df = apr_df[apr_df['volume'].notna()].copy()
+    
+    # Create chart
+    volume_chart = VolumeChart(data_processor)
+    fig, csv_path = volume_chart.generate_visualization(
+        volume_df, 
+        csv_filename=FILE_PATHS['volume_csv']
+    )
+    
+    return fig, csv_path

requirements.txt

@@ -1,8 +0,0 @@
-web3
-gradio
-plotly
-pandas
-requests
-python-dotenv
-kaleido
-matplotlib