app.py

import requests
import pandas as pd
import gradio as gr
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
from datetime import datetime, timedelta
import json
# Commenting out blockchain-related imports that cause loading issues
# from web3 import Web3
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import random
import logging
from typing import List, Dict, Any
# Comment out the import for now and replace with dummy functions
# from app_trans_new import create_transcation_visualizations,create_active_agents_visualizations
# APR visualization functions integrated directly

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"

# Add a timezone adjustment function at the top of the file after imports
def adjust_timestamp(timestamp_dt, hours_offset=0):
    """
    Adjust a timestamp by the specified number of hours.
    Used to correct for timezone differences between environments.
    
    Args:
        timestamp_dt: datetime object to adjust
        hours_offset: number of hours to add (can be negative)
        
    Returns:
        Adjusted datetime object
    """
    if timestamp_dt is None:
        return None
    
    return timestamp_dt + timedelta(hours=hours_offset)

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:
            logger.warning(f"Null JSON value for agent_id: {attr.get('agent_id')}")
            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):
            logger.info(f"Parsing JSON string for agent_id: {attr.get('agent_id')}")
            json_data = json.loads(attr["json_value"])
        else:
            json_data = attr["json_value"]
        
        apr = json_data.get("apr")
        timestamp = json_data.get("timestamp")
        
        logger.info(f"Raw timestamp from API: {timestamp}, type: {type(timestamp)}")
        
        # Convert timestamp to datetime if it exists
        timestamp_dt = None
        if timestamp:
            # Just use the standard conversion without timezone specification
            timestamp_dt = datetime.fromtimestamp(timestamp)
            logger.info(f"Converted timestamp: {timestamp_dt}")
            
            # Log timezone information
            try:
                local_now = datetime.now()
                logger.info(f"Current local time: {local_now}")
                logger.info(f"Difference between API time and local time (hours): {(timestamp_dt - local_now).total_seconds() / 3600:.2f}")
            except Exception as e:
                logger.error(f"Error calculating time difference: {e}")
        else:
            logger.warning(f"No timestamp in data for agent_id: {attr.get('agent_id')}")
            
        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
    
    # Set the timezone offset between local and HF environments
    # Based on the logs, we're seeing ~6 hour difference
    # If HF is showing earlier times than local, use a negative value
    TIMEZONE_OFFSET_HOURS = -3  # Adjust based on observed differences
    
    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:
                # Apply timezone adjustment
                apr_data["timestamp"] = adjust_timestamp(apr_data["timestamp"], TIMEZONE_OFFSET_HOURS)
                logger.info(f"Adjusted timestamp: {apr_data['timestamp']}")
                
                # 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)
        
        # Log timestamp ranges for debugging
        if not global_df.empty:
            logger.info(f"DataFrame timestamp min: {global_df['timestamp'].min()}")
            logger.info(f"DataFrame timestamp max: {global_df['timestamp'].max()}")
        
        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 file for reference
        fig.write_html("modius_apr_combined_graph.html")
        fig.write_image("modius_apr_combined_graph.png")
        
        csv_file = None
        return 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)
    
    # Only create combined time series graph
    combined_fig = create_combined_time_series_graph(df)
    
    return 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
    
    # Debug: Print detailed info about the dataframe
    logger.info(f"Combined graph data - shape: {df.shape}")
    logger.info(f"Timestamp min: {df['timestamp'].min()}, timezone info: {getattr(df['timestamp'].min(), 'tzinfo', None)}")
    logger.info(f"Timestamp max: {df['timestamp'].max()}, timezone info: {getattr(df['timestamp'].max(), 'tzinfo', None)}")
    logger.info("Platform/Environment info:")
    logger.info(f"Host: {os.uname().nodename if hasattr(os, 'uname') else 'Unknown'}")
    logger.info(f"System: {os.name}")
    
    # Create a timestamp reference to identify the environment
    current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    logger.info(f"Environment check - current time: {current_time}")
    
    # Add a title annotation with environment information to help identify which environment is which
    environment_tag = "LOCAL" if os.environ.get("GRADIO_SERVER_PORT") is None else "HUGGINGFACE"
    logger.info(f"Environment tag: {environment_tag}")
    
    # Debug: Print every data point with full details to verify consistency
    for idx, row in df.iterrows():
        logger.info(f"Data point {idx}: agent={row['agent_name']}, time={row['timestamp']}, apr={row['apr']}, type={row['metric_type']}")
    
    # Create Plotly figure
    fig = go.Figure()
    
    # Get unique agents
    unique_agents = df['agent_id'].unique()
    logger.info(f"Unique agents: {[df[df['agent_id'] == agent_id]['agent_name'].iloc[0] for agent_id in unique_agents]}")
    
    # 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')
        logger.info(f"Agent {agent_name} data points: {len(agent_data)}")
        logger.info(f"Agent {agent_name} timestamps: {agent_data['timestamp'].tolist()}")
        
        # 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']
        logger.info(f"Agent {agent_name} APR points: {len(apr_data)}")
        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']
        logger.info(f"Agent {agent_name} Performance points: {len(perf_data)}")
        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=f"APR and Performance Values for All Agents - {environment_tag} - {current_time}",
        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=50, b=20),  # Increased top margin for title
        hovermode="closest",
        annotations=[
            dict(
                text=f"Environment: {environment_tag} | Server Time: {current_time}",
                xref="paper", yref="paper",
                x=0.5, y=1.05,  # Positioned above the main title
                showarrow=False,
                font=dict(size=10, color="gray")
            )
        ]
    )
    
    # 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)

# Create dummy functions for the commented out imports
def create_transcation_visualizations():
    """Dummy implementation that returns a placeholder graph"""
    fig = go.Figure()
    fig.add_annotation(
        text="Blockchain data loading disabled - placeholder visualization", 
        x=0.5, y=0.5, xref="paper", yref="paper",
        showarrow=False, font=dict(size=20)
    )
    return fig

def create_active_agents_visualizations():
    """Dummy implementation that returns a placeholder graph"""
    fig = go.Figure()
    fig.add_annotation(
        text="Blockchain data loading disabled - placeholder visualization", 
        x=0.5, y=0.5, xref="paper", yref="paper",
        showarrow=False, font=dict(size=20)
    )
    return fig

# Comment out the blockchain connection code
"""
# Load environment variables from .env file
# RPC URLs
OPTIMISM_RPC_URL = os.getenv('OPTIMISM_RPC_URL')
MODE_RPC_URL = os.getenv('MODE_RPC_URL')

# Initialize Web3 instances
web3_instances = {
    'optimism': Web3(Web3.HTTPProvider(OPTIMISM_RPC_URL)),
    'mode': Web3(Web3.HTTPProvider(MODE_RPC_URL))
}

# Contract addresses for service registries
contract_addresses = {
    'optimism': '0x3d77596beb0f130a4415df3D2D8232B3d3D31e44',
    'mode': '0x3C1fF68f5aa342D296d4DEe4Bb1cACCA912D95fE'
}

# 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)

# Create the contract instances
service_registries = {
    chain_name: web3.eth.contract(address=contract_addresses[chain_name], abi=contract_abi)
    for chain_name, web3 in web3_instances.items()
}

# Check if connections are successful
for chain_name, web3_instance in web3_instances.items():
    if not web3_instance.is_connected():
        raise Exception(f"Failed to connect to the {chain_name.capitalize()} network.")
    else:
        print(f"Successfully connected to the {chain_name.capitalize()} network.")
"""

# Dummy blockchain functions to replace the commented ones
def get_transfers(integrator: str, wallet: str) -> str:
    """Dummy function that returns an empty result"""
    return {"transfers": []}

def fetch_and_aggregate_transactions():
    """Dummy function that returns empty data"""
    return [], {}

# Function to parse the transaction data and prepare it for visualization
def process_transactions_and_agents(data):
    """Dummy function that returns empty dataframes"""
    df_transactions = pd.DataFrame()
    df_agents = pd.DataFrame(columns=['date', 'agent_count'])
    df_agents_weekly = pd.DataFrame()
    return df_transactions, df_agents, df_agents_weekly

# Function to create visualizations based on the metrics
def create_visualizations():
    """
    # Commenting out the original visualization code temporarily for debugging
    transactions_data = fetch_and_aggregate_transactions()
    df_transactions, df_agents, df_agents_weekly = process_transactions_and_agents(transactions_data)

    # Fetch daily value locked data
    df_tvl = pd.read_csv('daily_value_locked.csv')

    # 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]

    chain_name_map = {
        "mode": "Mode",
        "base": "Base",
        "ethereum": "Ethereum",
        "optimism": "Optimism"
    }
    df_tvl_daily["chain_name"] = df_tvl_daily["chain_name"].map(chain_name_map)
    
    # Plot total value locked
    fig_tvl = px.bar(
        df_tvl_daily,
        x="date",
        y="total_value_locked_usd",
        color="chain_name",
        opacity=0.7,
        title="Total Volume Invested in Pools in Different Chains Daily",
        labels={"date": "Date","chain_name": "Transaction Chain", "total_value_locked_usd": "Total Volume Invested (USD)"},
        barmode='stack',
        color_discrete_map={
            "Mode": "orange",
            "Base": "purple",
            "Ethereum": "darkgreen",
            "Optimism": "blue"
        }
    )
    fig_tvl.update_layout(
        xaxis_title="Date",
        
        yaxis=dict(tickmode='linear', tick0=0, dtick=4),
        xaxis=dict(
            tickmode='array',
            tickvals=df_tvl_daily['date'],
            ticktext=df_tvl_daily['date'].dt.strftime('%b %d'),
            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=1200, # Specify width to prevent bars from being too wide
        showlegend=True,
        template='plotly_white'
    )
    fig_tvl.update_xaxes(tickformat="%b %d") 

    chain_name_map = {
        10: "Optimism",
        8453: "Base",
        1: "Ethereum",
        34443: "Mode"
    }

    df_transactions["sending_chain"] = df_transactions["sending_chain"].map(chain_name_map)
    df_transactions["receiving_chain"] = df_transactions["receiving_chain"].map(chain_name_map)

    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'])
    df_transactions["is_swap"] = df_transactions.apply(lambda x: x["sending_chain"] == x["receiving_chain"], axis=1)

    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",
        opacity=0.7,
        color_discrete_map={
            "Optimism": "blue",
            "Ethereum": "darkgreen",
            "Base": "purple",
            "Mode": "orange"
        }
    )
    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']],
            ticktext=[d.strftime('%m-%d') for d in swaps_per_chain['date']],
            tickangle=-45,
        ),
        bargap=0.6,
        bargroupgap=0.1,
        height=600,
        width=1200,
        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'
    )
    fig_swaps_chain.update_xaxes(tickformat="%m-%d")

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

    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",
        opacity=0.7,
        color_discrete_map={
            "Optimism": "blue",
            "Ethereum": "darkgreen",
            "Base": "purple",
            "Mode": "orange"
        }
    )
    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']],
            ticktext=[d.strftime('%m-%d') for d in bridges_per_chain['date']],
            tickangle=-45,
        ),
        bargap=0.6,
        bargroupgap=0.1,
        height=600,
        width=1200,
        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'
    )
    fig_bridges_chain.update_xaxes(tickformat="%m-%d")
    df_agents['date'] = pd.to_datetime(df_agents['date'])

    daily_agents_df = df_agents.groupby('date').agg({'agent_count': 'sum'}).reset_index()
    daily_agents_df.rename(columns={'agent_count': 'daily_agent_count'}, inplace=True)
    # Sort by date to ensure proper running total calculation
    daily_agents_df = daily_agents_df.sort_values('date')
    
    # Create week column
    daily_agents_df['week'] = daily_agents_df['date'].dt.to_period('W').apply(lambda r: r.start_time)
    
    # Calculate running total within each week
    daily_agents_df['running_weekly_total'] = daily_agents_df.groupby('week')['daily_agent_count'].cumsum()
    
    # Create final merged dataframe
    weekly_merged_df = daily_agents_df.copy()
    adjustment_date = pd.to_datetime('2024-11-15')
    weekly_merged_df.loc[weekly_merged_df['date'] == adjustment_date, 'daily_agent_count'] -= 1
    weekly_merged_df.loc[weekly_merged_df['date'] == adjustment_date, 'running_weekly_total'] -= 1
    fig_agents_registered = go.Figure(data=[
        go.Bar(
            name='Daily nr of Registered Agents',
            x=weekly_merged_df['date'].dt.strftime("%b %d"),
            y=weekly_merged_df['daily_agent_count'],
            opacity=0.7,
            marker_color='blue'
        ),
        go.Bar(
            name='Weekly Nr of Registered Agents',
            x=weekly_merged_df['date'].dt.strftime("%b %d"),
            y=weekly_merged_df['running_weekly_total'],
            opacity=0.7,
            marker_color='purple'
        )
    ])

    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(
            categoryorder='array',
            categoryarray=weekly_merged_df['date'].dt.strftime("%b %d"),
            tickangle=-45
        ),
        bargap=0.3,
        height=600,
        width=1200,
        showlegend=True,
        legend=dict(
            yanchor="top",
            xanchor="right",
        ),
        template='plotly_white',
    )

    return fig_swaps_chain, fig_bridges_chain, fig_agents_registered,fig_tvl
    """
    # Placeholder figures for testing
    fig_swaps_chain = go.Figure()
    fig_swaps_chain.add_annotation(
        text="Blockchain data loading disabled - placeholder visualization", 
        x=0.5, y=0.5, xref="paper", yref="paper",
        showarrow=False, font=dict(size=20)
    )
    
    fig_bridges_chain = go.Figure()
    fig_bridges_chain.add_annotation(
        text="Blockchain data loading disabled - placeholder visualization", 
        x=0.5, y=0.5, xref="paper", yref="paper",
        showarrow=False, font=dict(size=20)
    )
    
    fig_agents_registered = go.Figure()
    fig_agents_registered.add_annotation(
        text="Blockchain data loading disabled - placeholder visualization", 
        x=0.5, y=0.5, xref="paper", yref="paper",
        showarrow=False, font=dict(size=20)
    )
    
    fig_tvl = go.Figure()
    fig_tvl.add_annotation(
        text="Blockchain data loading disabled - placeholder visualization", 
        x=0.5, y=0.5, xref="paper", yref="paper",
        showarrow=False, font=dict(size=20)
    )
    
    return fig_swaps_chain, fig_bridges_chain, fig_agents_registered, fig_tvl

# Gradio interface
def dashboard():
    with gr.Blocks() as demo:
        gr.Markdown("# Valory APR Metrics")
        
        # APR Metrics tab - the only tab
        with gr.Tab("APR Metrics"):
            with gr.Column():
                refresh_btn = gr.Button("Refresh APR Data")
                
                # Create container for plotly figure (combined graph only)
                combined_graph = gr.Plot(label="APR for All Agents")
                
                # Function to update the graph
                def update_apr_graph():
                    # Generate visualization and get figure object directly
                    combined_fig, _ = generate_apr_visualizations()
                    return combined_fig
                
                # Set up the button click event
                refresh_btn.click(
                    fn=update_apr_graph,
                    inputs=[],
                    outputs=[combined_graph]
                )
                
                # Initialize the graph on load
                # We'll use placeholder figure initially
                import plotly.graph_objects as go
                placeholder_fig = go.Figure()
                placeholder_fig.add_annotation(
                    text="Click 'Refresh APR Data' to load APR graph", 
                    x=0.5, y=0.5, 
                    showarrow=False, 
                    font=dict(size=15)
                )
                combined_graph.value = placeholder_fig
        
    return demo

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