import requests
import pandas as pd
import gradio as gr
import plotly.graph_objects as go
import plotly.express as px
from datetime import datetime, timedelta
import json
from web3 import Web3
from app_trans_new import create_transcation_visualizations,create_active_agents_visualizations
from app_value_locked import fetch_daily_value_locked
OPTIMISM_RPC_URL = 'https://opt-mainnet.g.alchemy.com/v2/U5gnXPYxeyH43MJ9tP8ONBQHEDRav7H0'

# Initialize a Web3 instance
web3 = Web3(Web3.HTTPProvider(OPTIMISM_RPC_URL))

# Check if connection is successful
if not web3.is_connected():
    raise Exception("Failed to connect to the Optimism network.")

# Contract address
contract_address = '0x3d77596beb0f130a4415df3D2D8232B3d3D31e44'

# Load the ABI from the provided JSON file
with open('./contracts/service_registry_abi.json', 'r') as abi_file:
    contract_abi = json.load(abi_file)

# Now you can create the contract
service_registry = web3.eth.contract(address=contract_address, abi=contract_abi)

def get_transfers(integrator: str, wallet: str) -> str:
    url = f"https://li.quest/v1/analytics/transfers?integrator={integrator}&wallet={wallet}"
    headers = {"accept": "application/json"}
    response = requests.get(url, headers=headers)
    return response.json()

def load_activity_checker_contract(w3, staking_token_address):
    """
    Loads the Staking Token and Activity Checker contracts.

    :param w3: Web3 instance
    :param staking_token_address: Address of the staking token contract
    :return: Tuple of (Staking Token contract instance, Activity Checker contract instance)
    """
    try:
        # Load the ABI file for the Staking Token contract
        with open('./contracts/StakingToken.json', "r", encoding="utf-8") as file:
            staking_token_data = json.load(file)
        
        staking_token_abi = staking_token_data.get("abi", [])

        # Create the Staking Token contract instance
        staking_token_contract = w3.eth.contract(address=staking_token_address, abi=staking_token_abi)
        
        # Get the activity checker contract address from staking_token_contract
        activity_checker_address = staking_token_contract.functions.activityChecker().call()

        # Load the ABI file for the Activity Checker contract
        with open('./contracts/StakingActivityChecker.json', "r", encoding="utf-8") as file:
            activity_checker_data = json.load(file)
        
        activity_checker_abi = activity_checker_data.get("abi", [])

        # Create the Activity Checker contract instance
        activity_checker_contract = w3.eth.contract(address=activity_checker_address, abi=activity_checker_abi)
        
        return staking_token_contract, activity_checker_contract
    
    except Exception as e:
        print(f"An error occurred while loading the contracts: {e}")
        raise


def fetch_and_aggregate_transactions():
    total_services = service_registry.functions.totalSupply().call()
    aggregated_transactions = []
    daily_agent_counts = {}
    daily_agents_with_transactions = {}

    _staking_token_contract, activity_checker_contract = load_activity_checker_contract(web3, '0x88996bbdE7f982D93214881756840cE2c77C4992')

    for service_id in range(1, total_services + 1):
        service = service_registry.functions.getService(service_id).call()
        
        # Extract the list of agent IDs from the service data
        agent_ids = service[-1]  # Assuming the last element is the list of agent IDs

        # Check if 25 is in the list of agent IDs
        if 25 in agent_ids:
            agent_address = service_registry.functions.getAgentInstances(service_id).call()[1][0]
            response_transfers = get_transfers("valory", agent_address)
            transfers = response_transfers.get("transfers", [])
            if isinstance(transfers, list):
                aggregated_transactions.extend(transfers)
        
            # Track the daily number of agents
            creation_event = service_registry.events.CreateService.create_filter(
                from_block=0, argument_filters={'serviceId': service_id, 'configHash': service[2]}
            ).get_all_entries()
            
            if creation_event:
                block_number = creation_event[0]['blockNumber']
                block = web3.eth.get_block(block_number)
                creation_timestamp = datetime.fromtimestamp(block['timestamp'])
                date_str = creation_timestamp.strftime('%Y-%m-%d')
                print("date_str",date_str)
                if date_str not in daily_agent_counts:
                    daily_agent_counts[date_str] = set()
                if date_str not in daily_agents_with_transactions:
                    daily_agents_with_transactions[date_str] = set()

                service_safe = service[1]
                print("agent_address",agent_address,"service_safe",service_safe)
                multisig_nonces = activity_checker_contract.functions.getMultisigNonces(service_safe).call()[0]
                if multisig_nonces > 0:
                    daily_agents_with_transactions[date_str].add(agent_address)
                daily_agent_counts[date_str].add(agent_address)
    
    # Convert set to count
    daily_agent_counts = {date: len(agents) for date, agents in daily_agent_counts.items()}
    daily_agents_with_transactions = {date: len(agents) for date, agents in daily_agents_with_transactions.items()}
    return aggregated_transactions, daily_agent_counts, daily_agents_with_transactions

# Function to parse the transaction data and prepare it for visualization
def process_transactions_and_agents(data):
    transactions, daily_agent_counts, daily_agents_with_transactions = data

    # Convert the data into a pandas DataFrame for easy manipulation
    rows = []
    for tx in transactions:
        # Normalize amounts
        sending_amount = float(tx["sending"]["amount"]) / (10 ** tx["sending"]["token"]["decimals"])
        receiving_amount = float(tx["receiving"]["amount"]) / (10 ** tx["receiving"]["token"]["decimals"])

        # Convert timestamps to datetime objects
        sending_timestamp = datetime.utcfromtimestamp(tx["sending"]["timestamp"])
        receiving_timestamp = datetime.utcfromtimestamp(tx["receiving"]["timestamp"])

        # Prepare row data
        rows.append({
            "transactionId": tx["transactionId"],
            "from_address": tx["fromAddress"],
            "to_address": tx["toAddress"],
            "sending_chain": tx["sending"]["chainId"],
            "receiving_chain": tx["receiving"]["chainId"],
            "sending_token_symbol": tx["sending"]["token"]["symbol"],
            "receiving_token_symbol": tx["receiving"]["token"]["symbol"],
            "sending_amount": sending_amount,
            "receiving_amount": receiving_amount,
            "sending_amount_usd": float(tx["sending"]["amountUSD"]),
            "receiving_amount_usd": float(tx["receiving"]["amountUSD"]),
            "sending_gas_used": int(tx["sending"]["gasUsed"]),
            "receiving_gas_used": int(tx["receiving"]["gasUsed"]),
            "sending_timestamp": sending_timestamp,
            "receiving_timestamp": receiving_timestamp,
            "date": sending_timestamp.date(),  # Group by day
            "week": sending_timestamp.strftime('%Y-%m-%d')  # Group by week
        })
    
    df_transactions = pd.DataFrame(rows)
    df_agents = pd.DataFrame(list(daily_agent_counts.items()), columns=['date', 'agent_count'])
    df_agents_with_transactions = pd.DataFrame(list(daily_agents_with_transactions.items()), columns=['date', 'agent_count_with_transactions'])

    # Convert the date column to datetime
    df_agents['date'] = pd.to_datetime(df_agents['date'])
    df_agents_with_transactions['date'] = pd.to_datetime(df_agents_with_transactions['date'])
    
    # Convert to week periods
    df_agents['week'] = df_agents['date'].dt.to_period('W').apply(lambda r: r.start_time)
    df_agents_with_transactions['week'] = df_agents_with_transactions['date'].dt.to_period('W').apply(lambda r: r.start_time)
    
    # Group by week
    df_agents_weekly = df_agents[['week', 'agent_count']].groupby('week').sum().reset_index()
    df_agents_with_transactions_weekly = df_agents_with_transactions[['week', 'agent_count_with_transactions']].groupby('week').sum().reset_index()

    return df_transactions, df_agents_weekly, df_agents_with_transactions_weekly, df_agents_with_transactions

# Function to create visualizations based on the metrics
def create_visualizations():
    transactions_data = fetch_and_aggregate_transactions()
    df_transactions, df_agents_weekly, df_agents_with_transactions_weekly, df_agents_with_transactions = process_transactions_and_agents(transactions_data)
    # Map chain IDs to chain names

        # Fetch daily value locked data
    df_tvl = fetch_daily_value_locked()

    # Calculate total value locked per chain per day
    df_tvl["total_value_locked_usd"] = df_tvl["amount0_usd"] + df_tvl["amount1_usd"]
    df_tvl_daily = df_tvl.groupby(["date", "chain_name"])["total_value_locked_usd"].sum().reset_index()
    df_tvl_daily['date'] = pd.to_datetime(df_tvl_daily['date'])

    # Filter out dates with zero total value locked
    df_tvl_daily = df_tvl_daily[df_tvl_daily["total_value_locked_usd"] > 0]
    # Plot total value locked
    # Plot total value locked
    fig_tvl = go.Figure()

    for chain_name, color in zip(["optimism", "base", "ethereum"], ["blue", "purple", "darkgreen"]):
        chain_data = df_tvl_daily[df_tvl_daily['chain_name'] == chain_name]
        fig_tvl.add_trace(go.Bar(
            x=chain_data['date'],
            y=chain_data['total_value_locked_usd'],
            name=chain_name.capitalize(),
            marker_color=color
        ))

    fig_tvl.update_layout(
        title="Total Volume Invested in Pools in Different Chains Daily",
        xaxis_title="Date",
        yaxis_title="Total Volume Invested (USD)",
        barmode='stack',
        xaxis=dict(
            tickmode='array',
            tickvals=df_tvl_daily['date'],
            ticktext=df_tvl_daily['date'].dt.strftime('%b %d'),
            tickangle=-45,
        ),
        height=600,
        width=1000,
        margin=dict(l=50, r=50, t=50, b=50),
        showlegend=True,
        legend=dict(
            yanchor="top",
            y=0.99,
            xanchor="right",
            x=0.99
        ),
        template='plotly_white'
    )

    chain_name_map = {
        10: "Optimism",
        8453: "Base",
        1: "Ethereum"
    }
    df_transactions["sending_chain"] = df_transactions["sending_chain"].map(chain_name_map)
    df_transactions["receiving_chain"] = df_transactions["receiving_chain"].map(chain_name_map)

    # Ensure that chain IDs are strings for consistent grouping
    df_transactions["sending_chain"] = df_transactions["sending_chain"].astype(str)
    df_transactions["receiving_chain"] = df_transactions["receiving_chain"].astype(str)
    df_transactions['date'] = pd.to_datetime(df_transactions['date'])

    # Identify swap transactions
    df_transactions["is_swap"] = df_transactions.apply(lambda x: x["sending_token_symbol"] != x["receiving_token_symbol"], axis=1)

    # Total swaps per chain per day
    swaps_per_chain = df_transactions[df_transactions["is_swap"]].groupby(["date", "sending_chain"]).size().reset_index(name="swap_count")
    fig_swaps_chain = px.bar(
        swaps_per_chain,
        x="date",
        y="swap_count",
        color="sending_chain",
        title="Chain Daily Activity: Swaps",
        labels={"sending_chain": "Transaction Chain", "swap_count": "Daily Swap Nr"},
        barmode="stack",
        color_discrete_map={
            "Optimism": "blue",
            "Ethereum": "darkgreen",
            "Base": "purple"
        }
    )
    fig_swaps_chain.update_layout(
        xaxis_title="Date",
        yaxis_title="Daily Swap Count",
        yaxis=dict(tickmode='linear', tick0=0, dtick=1),
        xaxis=dict(
            tickmode='array',
            tickvals=[d for d in swaps_per_chain['date'] if d.weekday() == 0],  # Show only Mondays
            ticktext=[d.strftime('%m-%d') for d in swaps_per_chain['date'] if d.weekday() == 0],
            tickangle=-45,
        ),
        bargap=0.6,  # Increase gap between bar groups (0-1)
        bargroupgap=0.1,  # Decrease gap between bars in a group (0-1)
        height=600,
        width=1000,  # Specify width to prevent bars from being too wide
        margin=dict(l=50, r=50, t=50, b=50),  # Add margins
        showlegend=True,
        legend=dict(
            yanchor="top",
            y=0.99,
            xanchor="right",
            x=0.99
        ),
        template='plotly_white'
    )
    fig_swaps_chain.update_xaxes(tickformat="%m-%d")

    # Identify bridge transactions
    # Identify bridge transactions
    df_transactions["is_bridge"] = df_transactions.apply(lambda x: x["sending_chain"] != x["receiving_chain"], axis=1)

    # Total bridges per chain per day
    bridges_per_chain = df_transactions[df_transactions["is_bridge"]].groupby(["date", "sending_chain"]).size().reset_index(name="bridge_count")
    fig_bridges_chain = px.bar(
        bridges_per_chain,
        x="date",
        y="bridge_count",
        color="sending_chain",
        title="Chain Daily Activity: Bridges",
        labels={"sending_chain": "Transaction Chain", "bridge_count": "Daily Bridge Nr"},
        barmode="stack",
        color_discrete_map={
            "Optimism": "blue",
            "Ethereum": "darkgreen",
            "Base": "purple"
        }
    )
    fig_bridges_chain.update_layout(
        xaxis_title="Date",
        yaxis_title="Daily Bridge Count",
        yaxis=dict(tickmode='linear', tick0=0, dtick=1),
        xaxis=dict(
            tickmode='array',
            tickvals=[d for d in bridges_per_chain['date'] if d.weekday() == 0],  # Show only Mondays
            ticktext=[d.strftime('%m-%d') for d in bridges_per_chain['date'] if d.weekday() == 0],
            tickangle=-45,
        ),
        bargap=0.6,  # Increase gap between bar groups (0-1)
        bargroupgap=0.1,  # Decrease gap between bars in a group (0-1)
        height=600,
        width=1000,  # Specify width to prevent bars from being too wide
        margin=dict(l=50, r=50, t=50, b=50),  # Add margins
        showlegend=True,
        legend=dict(
            yanchor="top",
            y=0.99,
            xanchor="right",
            x=0.99
        ),
        template='plotly_white'
    )
    fig_bridges_chain.update_xaxes(tickformat="%m-%d")

    # Nr of agents registered daily and weekly
    # Convert 'date' column to datetime
    df_agents_with_transactions['date'] = pd.to_datetime(df_agents_with_transactions['date'])

    # Calculate daily number of agents registered
    daily_agents_df = df_agents_with_transactions.groupby('date').size().reset_index(name='daily_agent_count')
    
    # Check for October 2, 2024 and update the value
    daily_agents_df.loc[daily_agents_df['date'] == '2024-10-02', 'daily_agent_count'] = 2
    
    # Calculate cumulative number of agents registered within the week up to each day
    df_agents_with_transactions['week_start'] = df_agents_with_transactions['date'].dt.to_period("W").apply(lambda r: r.start_time)
    cumulative_agents_df = df_agents_with_transactions.groupby(['week_start', 'date']).size().groupby(level=0).cumsum().reset_index(name='weekly_agent_count')
    
    # Check for October 2, 2024 and update the value
    cumulative_agents_df.loc[cumulative_agents_df['date'] == '2024-10-02', 'weekly_agent_count'] = 2

    # Combine the data to ensure both dataframes align for plotting
    combined_df = pd.merge(daily_agents_df, cumulative_agents_df, on='date', how='left')

    # Create the bar chart with side-by-side bars
    fig_agents_registered = go.Figure(data=[
        go.Bar(
            name='Daily nr of Registered Agents',
            x=combined_df['date'],
            y=combined_df['daily_agent_count'],
            marker_color='blue'
        ),
        go.Bar(
            name='Total Weekly Nr of Registered Agents',
            x=combined_df['date'],
            y=combined_df['weekly_agent_count'],
            marker_color='purple'
        )
    ])

    # Update layout to group bars side by side for each day
    fig_agents_registered.update_layout(
        xaxis_title='Date',
        yaxis_title='Number of Agents',
        title="Nr of Agents Registered",
        barmode='group',
        yaxis=dict(tickmode='linear', tick0=0, dtick=1),
        xaxis=dict(
            tickmode='array',
            tickvals=combined_df['date'],
            ticktext=[d.strftime("%b %d") for d in combined_df['date']],
            tickangle=-45
        ),
        bargap=0.6,  # Increase gap between bar groups (0-1)
        height=600,
        width=1000,  # Specify width to prevent bars from being too wide
        margin=dict(l=50, r=50, t=50, b=50),  # Add margins
        showlegend=True,
        legend=dict(
            yanchor="top",
            y=0.99,
            xanchor="right",
            x=0.99
        ),
        template='plotly_white'
    )

        # Calculate weekly average daily active agents
    df_agents_with_transactions['day_of_week'] = df_agents_with_transactions['date'].dt.dayofweek
    df_agents_with_transactions_weekly_avg = df_agents_with_transactions.groupby(['week', 'day_of_week'])['agent_count_with_transactions'].mean().reset_index()
    df_agents_with_transactions_weekly_avg = df_agents_with_transactions_weekly_avg.groupby('week')['agent_count_with_transactions'].mean().reset_index()
    # Number of agents with transactions per week
    fig_agents_with_transactions_daily = px.bar(
        df_agents_with_transactions_weekly,
        x="week",
        y="agent_count_with_transactions",
        title="Daily Active Agents: Weekly Average Nr of agents with at least 1 transaction daily",
        labels={"week": "Week of", "agent_count_with_transactions": "Number of Agents with Transactions"},
        color_discrete_sequence=["darkgreen"]
    )
    fig_agents_with_transactions_daily.update_layout(
        title=dict(
        x=0.5,y=0.95,xanchor='center',yanchor='top'),  # Adjust vertical position and Center the title
        yaxis=dict(tickmode='linear', tick0=0, dtick=1),
        xaxis=dict(
            tickmode='array',
            tickvals=df_agents_with_transactions_weekly_avg['week'],
            ticktext=df_agents_with_transactions_weekly_avg['week'].dt.strftime('%b %d'),
            tickangle=0
        ),
        bargap=0.6,  # Increase gap between bar groups (0-1)
        bargroupgap=0.1,  # Decrease gap between bars in a group (0-1)
        height=600,
        width=1000,  # Specify width to prevent bars from being too wide
        margin=dict(l=50, r=50, t=50, b=50),  # Add margins
        showlegend=True,
        legend=dict(
            yanchor="top",
            y=0.99,
            xanchor="right",
            x=0.99
        )
    )

    return fig_swaps_chain, fig_bridges_chain, fig_agents_registered, fig_agents_with_transactions_daily,fig_tvl

# Gradio interface
def dashboard():
    with gr.Blocks() as demo:
        gr.Markdown("# Valory Transactions Dashboard")
        with gr.Tab("Chain Daily activity"):
            fig_tx_chain = create_transcation_visualizations()
            gr.Plot(fig_tx_chain)

        fig_swaps_chain, fig_bridges_chain, fig_agents_registered, fig_agents_with_transactions_daily,fig_tvl = create_visualizations()    
        #Fetch and display visualizations
        with gr.Tab("Swaps Daily"):
            gr.Plot(fig_swaps_chain)
        
        with gr.Tab("Bridges Daily"):
            #fig_swaps_chain, fig_bridges_chain, fig_agents_daily, fig_agents_with_transactions_daily,fig_tvl = create_visualizations()
            gr.Plot(fig_bridges_chain)
        
        with gr.Tab("Nr of Agents Registered"):
            #fig_swaps_chain, fig_bridges_chain, fig_agents_daily, fig_agents_with_transactions_daily,fig_tvl = create_visualizations()
            gr.Plot(fig_agents_registered)
        
        with gr.Tab("DAA"):
            fig_agents_with_transactions_daily = create_active_agents_visualizations()
            #fig_swaps_chain, fig_bridges_chain, fig_agents_daily, fig_agents_with_transactions_daily,fig_tvl = create_visualizations()
            gr.Plot(fig_agents_with_transactions_daily)

        with gr.Tab("Total Value Locked"):
            #fig_swaps_chain, fig_bridges_chain, fig_agents_daily, fig_agents_with_transactions_daily, fig_tvl,fig_tvl = create_visualizations()
            gr.Plot(fig_tvl) 

    return demo

# Launch the dashboard
if __name__ == "__main__":
    dashboard().launch()