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

# Set up logging with appropriate verbosity
logging.basicConfig(
    level=logging.INFO,  # Use INFO level instead of DEBUG to reduce verbosity
    format="%(asctime)s - %(levelname)s - %(message)s",
    handlers=[
        logging.FileHandler("app_debug.log"),  # Log to file for persistence
        logging.StreamHandler()  # Also log to console
    ]
)
logger = logging.getLogger(__name__)

# Reduce third-party library logging
logging.getLogger("urllib3").setLevel(logging.WARNING)
logging.getLogger("httpx").setLevel(logging.WARNING)
logging.getLogger("matplotlib").setLevel(logging.WARNING)

# Log the startup information
logger.info("============= APPLICATION STARTING =============")
logger.info(f"Running from directory: {os.getcwd()}")

# Global variable to store the data for reuse
global_df = None

# Configuration
API_BASE_URL = "https://afmdb.autonolas.tech"
logger.info(f"Using API endpoint: {API_BASE_URL}")

def get_agent_type_by_name(type_name: str) -> Dict[str, Any]:
    """Get agent type by name"""
    url = f"{API_BASE_URL}/api/agent-types/name/{type_name}"
    logger.debug(f"Calling API: {url}")
    
    try:
        response = requests.get(url)
        logger.debug(f"Response status: {response.status_code}")
        
        if response.status_code == 404:
            logger.error(f"Agent type '{type_name}' not found")
            return None
            
        response.raise_for_status()
        result = response.json()
        logger.debug(f"Agent type response: {result}")
        return result
    except Exception as e:
        logger.error(f"Error in get_agent_type_by_name: {e}")
        return None

def get_attribute_definition_by_name(attr_name: str) -> Dict[str, Any]:
    """Get attribute definition by name"""
    url = f"{API_BASE_URL}/api/attributes/name/{attr_name}"
    logger.debug(f"Calling API: {url}")
    
    try:
        response = requests.get(url)
        logger.debug(f"Response status: {response.status_code}")
        
        if response.status_code == 404:
            logger.error(f"Attribute definition '{attr_name}' not found")
            return None
            
        response.raise_for_status()
        result = response.json()
        logger.debug(f"Attribute definition response: {result}")
        return result
    except Exception as e:
        logger.error(f"Error in get_attribute_definition_by_name: {e}")
        return None

def get_agents_by_type(type_id: int) -> List[Dict[str, Any]]:
    """Get all agents of a specific type"""
    url = f"{API_BASE_URL}/api/agent-types/{type_id}/agents/"
    logger.debug(f"Calling API: {url}")
    
    try:
        response = requests.get(url)
        logger.debug(f"Response status: {response.status_code}")
        
        if response.status_code == 404:
            logger.error(f"No agents found for type ID {type_id}")
            return []
            
        response.raise_for_status()
        result = response.json()
        logger.debug(f"Agents count: {len(result)}")
        logger.debug(f"First few agents: {result[:2] if result else []}")
        return result
    except Exception as e:
        logger.error(f"Error in get_agents_by_type: {e}")
        return []

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 = []
    logger.debug(f"Getting attributes for {len(agents)} agents with attr_def_id: {attr_def_id}")
    
    # 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
        url = f"{API_BASE_URL}/api/agents/{agent_id}/attributes/"
        logger.debug(f"Calling API for agent {agent_id}: {url}")
        
        try:
            response = requests.get(url, params={"limit": 1000})
            
            if response.status_code == 404:
                logger.error(f"No attributes found for agent ID {agent_id}")
                continue
            
            response.raise_for_status()
            agent_attrs = response.json()
            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)} APR attributes")
            
            if filtered_attrs:
                logger.debug(f"Sample attribute for agent {agent_id}: {filtered_attrs[0]}")
            
            all_attributes.extend(filtered_attrs)
        except requests.exceptions.RequestException as e:
            logger.error(f"Error fetching attributes for agent ID {agent_id}: {e}")
    
    logger.info(f"Total APR attributes found across all agents: {len(all_attributes)}")
    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, adjusted APR value, 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, "timestamp": None, "agent_id": agent_id, "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")  # Extract adjusted_apr if present
        timestamp = json_data.get("timestamp")
        
        logger.debug(f"Agent {agent_id}: Raw APR value: {apr}, adjusted APR value: {adjusted_apr}, timestamp: {timestamp}")
        
        # Convert timestamp to datetime if it exists
        timestamp_dt = None
        if timestamp:
            timestamp_dt = datetime.fromtimestamp(timestamp)
            
        result = {"apr": apr, "adjusted_apr": adjusted_apr, "timestamp": timestamp_dt, "agent_id": agent_id, "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, "timestamp": None, "agent_id": attr.get('agent_id'), "is_dummy": False}

def fetch_apr_data_from_db():
    """
    Fetch APR data from database using the API.
    """
    global global_df
    
    logger.info("==== Starting APR data fetch ====")
    
    try:
        # Step 1: Find the Modius agent type
        logger.info("Finding 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"]
        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_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"]
        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})")
        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
        
        logger.info(f"Found {len(modius_agents)} Modius agents")
        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 = 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
        
        logger.info(f"Found {len(apr_attributes)} APR attributes total")
        
        # Step 5: Extract APR data
        logger.info("Extracting APR data from attributes")
        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
                
                # Include all APR values (including negative ones) EXCEPT zero and -100
                if apr_data["apr"] != 0 and apr_data["apr"] != -100:
                    apr_data["metric_type"] = "APR"
                    logger.debug(f"Agent {agent_name} ({attr['agent_id']}): APR value: {apr_data['apr']}")
                    # Add to the data list
                    apr_data_list.append(apr_data)
                else:
                    # Log that we're skipping zero or -100 values
                    logger.debug(f"Skipping value for agent {agent_name} ({attr['agent_id']}): {apr_data['apr']} (zero or -100)")
        
        # 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 the resulting dataframe
        logger.info(f"Created DataFrame with {len(global_df)} rows")
        logger.info(f"DataFrame columns: {global_df.columns.tolist()}")
        logger.info(f"APR statistics: min={global_df['apr'].min()}, max={global_df['apr'].max()}, mean={global_df['apr'].mean()}")
        
        # Log adjusted APR statistics if available
        if 'adjusted_apr' in global_df.columns and global_df['adjusted_apr'].notna().any():
            logger.info(f"Adjusted APR statistics: min={global_df['adjusted_apr'].min()}, max={global_df['adjusted_apr'].max()}, mean={global_df['adjusted_apr'].mean()}")
            logger.info(f"Number of records with adjusted_apr: {global_df['adjusted_apr'].notna().sum()} out of {len(global_df)}")
            
            # Log the difference between APR and adjusted APR
            valid_rows = global_df[global_df['adjusted_apr'].notna()]
            if not valid_rows.empty:
                avg_diff = (valid_rows['apr'] - valid_rows['adjusted_apr']).mean()
                max_diff = (valid_rows['apr'] - valid_rows['adjusted_apr']).max()
                min_diff = (valid_rows['apr'] - valid_rows['adjusted_apr']).min()
                logger.info(f"APR vs Adjusted APR difference: avg={avg_diff:.2f}, max={max_diff:.2f}, min={min_diff:.2f}")
        else:
            logger.info("No adjusted APR values found in the data")
            
        # All values are APR type (excluding zero and -100 values)
        logger.info("All values are APR type (excluding zero and -100 values)")
        logger.info(f"Agents count: {global_df['agent_name'].value_counts().to_dict()}")
        
        # Log the entire dataframe for debugging
        logger.debug("Final DataFrame contents:")
        for idx, row in global_df.iterrows():
            logger.debug(f"Row {idx}: {row.to_dict()}")
        
        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}")
        logger.exception("Exception details:")
        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 write_debug_info(df, fig):
    """Minimal debug info function"""
    try:
        # Just log minimal information
        logger.debug(f"Graph created with {len(df)} data points and {len(fig.data)} traces")
        return True
    except Exception as e:
        logger.error(f"Error writing debug info: {e}")
        return False

def create_combined_time_series_graph(df):
    """Create a time series graph showing average APR values across all agents"""
    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
    
    # IMPORTANT: Force data types to ensure consistency
    df['apr'] = df['apr'].astype(float)  # Ensure APR is float
    df['metric_type'] = df['metric_type'].astype(str)  # Ensure metric_type is string
    
    # CRITICAL: Log the exact dataframe we're using for plotting to help debug
    logger.info(f"Graph data - shape: {df.shape}, columns: {df.columns}")
    logger.info(f"Graph data - unique agents: {df['agent_name'].unique().tolist()}")
    logger.info("Graph data - all positive APR values only")
    logger.info(f"Graph data - min APR: {df['apr'].min()}, max APR: {df['apr'].max()}")
    
    # Export full dataframe to CSV for debugging
    debug_csv = "debug_graph_data.csv"
    df.to_csv(debug_csv)
    logger.info(f"Exported graph data to {debug_csv} for debugging")
    
    # Write detailed data report
    with open("debug_graph_data_report.txt", "w") as f:
        f.write("==== GRAPH DATA REPORT ====\n\n")
        f.write(f"Total data points: {len(df)}\n")
        f.write(f"Timestamp range: {df['timestamp'].min()} to {df['timestamp'].max()}\n\n")
        
        # Output per-agent details
        unique_agents = df['agent_id'].unique()
        f.write(f"Number of agents: {len(unique_agents)}\n\n")
        
        for agent_id in unique_agents:
            agent_data = df[df['agent_id'] == agent_id]
            agent_name = agent_data['agent_name'].iloc[0]
            
            f.write(f"== Agent: {agent_name} (ID: {agent_id}) ==\n")
            f.write(f"  Total data points: {len(agent_data)}\n")
            
            apr_data = agent_data[agent_data['metric_type'] == 'APR']
            
            f.write(f"  APR data points: {len(apr_data)}\n")
            
            if not apr_data.empty:
                f.write(f"  APR values: {apr_data['apr'].tolist()}\n")
                f.write(f"  APR timestamps: {[ts.strftime('%Y-%m-%d %H:%M:%S') if ts is not None else 'None' for ts in apr_data['timestamp']]}\n")
            
            f.write("\n")
    
    logger.info("Generated detailed graph data report")
    
    # ENSURE THERE ARE NO CONFLICTING AXES OR TRACES
    # Create Plotly figure in a clean state
    fig = go.Figure()
    
    # Enable autoscaling instead of fixed ranges
    logger.info("Using autoscaling for axes ranges")
    
    # Add background shapes for APR and Performance regions
    min_time = df['timestamp'].min()
    max_time = df['timestamp'].max()
    
    # Add shape for positive APR region (above zero)
    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=min_time, x1=max_time,
        layer="below"
    )
    
    # Add shape for negative APR region (below zero)
    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=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
    )
    
    # MODIFIED: Calculate average APR values across all agents for each timestamp
    # Filter for APR data only
    apr_data = df[df['metric_type'] == 'APR'].copy()
    
    # 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']}")
    
    # Use the filtered data for all subsequent operations
    apr_data = apr_data_filtered
    
    # Group by timestamp and calculate mean APR
    avg_apr_data = apr_data.groupby('timestamp')['apr'].mean().reset_index()
    
    # Sort by timestamp
    avg_apr_data = avg_apr_data.sort_values('timestamp')
    
    # Log the average APR data
    logger.info(f"Calculated average APR data with {len(avg_apr_data)} points")
    for idx, row in avg_apr_data.iterrows():
        logger.info(f"  Average point {idx}: timestamp={row['timestamp']}, avg_apr={row['apr']}")
    
    # Calculate moving average based on a time window (2 hours)
    # Sort data by timestamp
    apr_data_sorted = apr_data.sort_values('timestamp')
    
    # Create a new dataframe for the moving average
    avg_apr_data_with_ma = avg_apr_data.copy()
    avg_apr_data_with_ma['moving_avg'] = None  # Initialize the moving average column
    
    # Define the time window for the moving average (3 days)
    time_window = pd.Timedelta(days=3)
    logger.info(f"Calculating moving average with time window of {time_window}")
    
    # Calculate moving averages: one for APR and one for adjusted APR
    avg_apr_data_with_ma['moving_avg'] = None  # 3-day window for APR
    avg_apr_data_with_ma['adjusted_moving_avg'] = None  # 3-day window for adjusted APR
    
    # Calculate the moving averages for each timestamp
    for i, row in avg_apr_data_with_ma.iterrows():
        current_time = row['timestamp']
        window_start = current_time - time_window
        
        # Get all data points within the 3-day time window
        window_data = apr_data_sorted[
            (apr_data_sorted['timestamp'] >= window_start) & 
            (apr_data_sorted['timestamp'] <= current_time)
        ]
        
        # Calculate the average APR for the 3-day time window
        if not window_data.empty:
            avg_apr_data_with_ma.at[i, 'moving_avg'] = window_data['apr'].mean()
            logger.debug(f"APR time window {window_start} to {current_time}: {len(window_data)} points, avg={window_data['apr'].mean()}")
            
            # Calculate adjusted APR moving average if data exists
            if 'adjusted_apr' in window_data.columns and window_data['adjusted_apr'].notna().any():
                avg_apr_data_with_ma.at[i, 'adjusted_moving_avg'] = window_data['adjusted_apr'].mean()
                logger.debug(f"Adjusted APR time window {window_start} to {current_time}: {len(window_data)} points, avg={window_data['adjusted_apr'].mean()}")
        else:
            # If no data points in the window, use the current value
            avg_apr_data_with_ma.at[i, 'moving_avg'] = row['apr']
            logger.debug(f"No data points in time window for {current_time}, using current value {row['apr']}")
    
    logger.info(f"Calculated time-based moving averages with {len(avg_apr_data_with_ma)} points")
    
    # Plot individual agent data points with agent names in hover, but limit display for scalability
    if not apr_data.empty:
        # Group by agent to use different colors for each agent
        unique_agents = apr_data['agent_name'].unique()
        colors = px.colors.qualitative.Plotly[:len(unique_agents)]
        
        # Create a color map for agents
        color_map = {agent: colors[i % len(colors)] for i, agent in enumerate(unique_agents)}
        
        # Calculate the total number of data points per agent to determine which are most active
        agent_counts = apr_data['agent_name'].value_counts()
        
        # Determine how many agents to show individually (limit to top 5 most active)
        MAX_VISIBLE_AGENTS = 5
        top_agents = agent_counts.nlargest(min(MAX_VISIBLE_AGENTS, len(agent_counts))).index.tolist()
        
        logger.info(f"Showing {len(top_agents)} agents by default out of {len(unique_agents)} total agents")
        
        # Add data points for each agent, but only make top agents visible by default
        for agent_name in unique_agents:
            agent_data = apr_data[apr_data['agent_name'] == agent_name]
            
            # Explicitly convert to Python lists
            x_values = agent_data['timestamp'].tolist()
            y_values = agent_data['apr'].tolist()
            
            # Determine if this agent should be visible by default
            is_visible = agent_name in top_agents
            
            # Add data points as markers for APR
            fig.add_trace(
                go.Scatter(
                    x=x_values,
                    y=y_values,
                    mode='markers',  # Only markers for original data
                    marker=dict(
                        color=color_map[agent_name],
                        symbol='circle',
                        size=10,
                        line=dict(width=1, color='black')
                    ),
                    name=f'Agent: {agent_name} (APR)',
                    hovertemplate='Time: %{x}<br>APR: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>',
                    visible=is_visible  # Only top agents visible by default
                )
            )
            logger.info(f"Added APR data points for agent {agent_name} with {len(x_values)} points (visible: {is_visible})")
            
            # Add data points for adjusted APR if it exists
            if 'adjusted_apr' in agent_data.columns and agent_data['adjusted_apr'].notna().any():
                x_values_adj = agent_data['timestamp'].tolist()
                y_values_adj = agent_data['adjusted_apr'].tolist()
                
                fig.add_trace(
                    go.Scatter(
                        x=x_values_adj,
                        y=y_values_adj,
                        mode='markers',  # Only markers for original data
                        marker=dict(
                            color=color_map[agent_name],
                            symbol='diamond',  # Different symbol for adjusted APR
                            size=10,
                            line=dict(width=1, color='black')
                        ),
                        name=f'Agent: {agent_name} (Adjusted APR)',
                        hovertemplate='Time: %{x}<br>Adjusted APR: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>',
                        visible=is_visible  # Only top agents visible by default
                    )
                )
                logger.info(f"Added Adjusted APR data points for agent {agent_name} with {len(x_values_adj)} points (visible: {is_visible})")
        
        # Add APR moving average as a smooth line
        x_values_ma = avg_apr_data_with_ma['timestamp'].tolist()
        y_values_ma = avg_apr_data_with_ma['moving_avg'].tolist()
        
        # Create hover template for the APR moving average line
        hover_data_apr = []
        for idx, row in avg_apr_data_with_ma.iterrows():
            timestamp = row['timestamp']
            hover_data_apr.append(
                f"Time: {timestamp}<br>Moving Avg APR (3d window): {row['moving_avg']:.2f}"
            )
        
        fig.add_trace(
            go.Scatter(
                x=x_values_ma,
                y=y_values_ma,
                mode='lines',  # Only lines for moving average
                line=dict(color='red', width=2),  # Thinner line
                name='Moving Average APR (3d window)',
                hovertext=hover_data_apr,
                hoverinfo='text',
                visible=True  # Visible by default
            )
        )
        logger.info(f"Added 3-day moving average APR trace with {len(x_values_ma)} points")
        
        # Add adjusted APR moving average line if it exists
        if 'adjusted_moving_avg' in avg_apr_data_with_ma.columns and avg_apr_data_with_ma['adjusted_moving_avg'].notna().any():
            y_values_adj_ma = avg_apr_data_with_ma['adjusted_moving_avg'].tolist()
            
            # Create hover template for the adjusted APR moving average line
            hover_data_adj = []
            for idx, row in avg_apr_data_with_ma.iterrows():
                timestamp = row['timestamp']
                if pd.notna(row['adjusted_moving_avg']):
                    hover_data_adj.append(
                        f"Time: {timestamp}<br>Moving Avg Adjusted APR (3d window): {row['adjusted_moving_avg']:.2f}"
                    )
                else:
                    hover_data_adj.append(
                        f"Time: {timestamp}<br>Moving Avg Adjusted APR (3d window): N/A"
                    )
            
            fig.add_trace(
                go.Scatter(
                    x=x_values_ma,
                    y=y_values_adj_ma,
                    mode='lines',  # Only lines for moving average
                    line=dict(color='green', width=4),  # Thicker solid line for adjusted APR
                    name='Moving Average Adjusted APR (3d window)',
                    hovertext=hover_data_adj,
                    hoverinfo='text',
                    visible=True  # Visible by default
                )
            )
            logger.info(f"Added 3-day moving average Adjusted APR trace with {len(x_values_ma)} points")
        
        # Removed cumulative APR as requested
        logger.info("Cumulative APR graph line has been removed as requested")
    
    # Update layout - use simple boolean values everywhere
    # Increase the width and height for better visualization
    fig.update_layout(
        title="Babydegen agents",
        xaxis_title="Time",
        yaxis_title="Value",
        template="plotly_white",
        height=700,  # Increased height
        width=1400,  # Increased width
        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"
    )
    
    # Add a note about hidden agents if there are more than MAX_VISIBLE_AGENTS
    if len(unique_agents) > MAX_VISIBLE_AGENTS:
        fig.add_annotation(
            text=f"Note: Only showing top {MAX_VISIBLE_AGENTS} agents by default. Toggle others in legend.",
            xref="paper", yref="paper",
            x=0.5, y=1.05,
            showarrow=False,
            font=dict(size=12, color="gray"),
            align="center"
        )
    
    # Use fixed tick spacing for clarity
    dtick = 5  # 5-unit ticks for better readability
    
    # Update y-axis with autoscaling and improved appearance
    fig.update_yaxes(
        showgrid=True, 
        gridwidth=1, 
        gridcolor='rgba(0,0,0,0.1)',
        # No fixed range - let Plotly autoscale
        autorange=True,  # Explicitly enable autoscaling
        tickformat=".2f",  # Format tick labels with 2 decimal places
        tickfont=dict(size=12)  # Larger font for tick labels
    )
    
    # Update x-axis with better formatting and autoscaling
    fig.update_xaxes(
        showgrid=True, 
        gridwidth=1, 
        gridcolor='rgba(0,0,0,0.1)',
        # No fixed range - let Plotly autoscale
        autorange=True,  # Explicitly enable autoscaling
        tickformat="%b %d, %H:%M",  # More detailed time format
        tickangle=-30,  # Angle the labels for better readability
        tickfont=dict(size=12)  # Larger font for tick labels
    )
    
    # SIMPLIFIED APPROACH: Do a direct plot without markers for comparison
    # This creates a simple, reliable fallback plot if the advanced one fails
    try:
        # Write detailed debug information before saving the figure
        write_debug_info(df, fig)
        
        # 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"
        try:
            fig.write_image(img_file)
            logger.info(f"Combined graph saved to {graph_file} and {img_file}")
        except Exception as e:
            logger.error(f"Error saving image: {e}")
            logger.info(f"Combined graph saved to {graph_file} only")
        
        # Return the figure object for direct use in Gradio
        return fig
    except Exception as e:
        # If the complex graph approach fails, create a simpler one
        logger.error(f"Error creating advanced graph: {e}")
        logger.info("Falling back to simpler graph")
        
        # 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=min_time, x1=max_time
        )
        
        # Define colors for the fallback graph
        fallback_colors = px.colors.qualitative.Plotly
        
        # Simply plot the average APR data with moving average
        if not avg_apr_data.empty:
            # Sort by timestamp
            avg_apr_data = avg_apr_data.sort_values('timestamp')
            
            # Calculate both moving averages for the fallback graph
            avg_apr_data_with_ma = avg_apr_data.copy()
            avg_apr_data_with_ma['moving_avg'] = None  # 2-hour window
            avg_apr_data_with_ma['infinite_avg'] = None  # Infinite window
            
            # Define the time window (6 hours)
            time_window = pd.Timedelta(hours=6)
            
            # Calculate the moving averages for each timestamp
            for i, row in avg_apr_data_with_ma.iterrows():
                current_time = row['timestamp']
                window_start = current_time - time_window
                
                # Get all data points within the 2-hour time window
                window_data = apr_data[
                    (apr_data['timestamp'] >= window_start) & 
                    (apr_data['timestamp'] <= current_time)
                ]
                
                # Get all data points up to the current timestamp (infinite window)
                infinite_window_data = apr_data[
                    apr_data['timestamp'] <= current_time
                ]
                
                # Calculate the average APR for the 2-hour time window
                if not window_data.empty:
                    avg_apr_data_with_ma.at[i, 'moving_avg'] = window_data['apr'].mean()
                else:
                    # If no data points in the window, use the current value
                    avg_apr_data_with_ma.at[i, 'moving_avg'] = row['apr']
                
                # Calculate the average APR for the infinite window
                if not infinite_window_data.empty:
                    avg_apr_data_with_ma.at[i, 'infinite_avg'] = infinite_window_data['apr'].mean()
                else:
                    avg_apr_data_with_ma.at[i, 'infinite_avg'] = row['apr']
            
            # Add data points for each agent, but only make top agents visible by default
            unique_agents = apr_data['agent_name'].unique()
            colors = px.colors.qualitative.Plotly[:len(unique_agents)]
            color_map = {agent: colors[i % len(colors)] for i, agent in enumerate(unique_agents)}
            
            # Calculate the total number of data points per agent
            agent_counts = apr_data['agent_name'].value_counts()
            
            # Determine how many agents to show individually (limit to top 5 most active)
            MAX_VISIBLE_AGENTS = 5
            top_agents = agent_counts.nlargest(min(MAX_VISIBLE_AGENTS, len(agent_counts))).index.tolist()
            
            for agent_name in unique_agents:
                agent_data = apr_data[apr_data['agent_name'] == agent_name]
                
                # Determine if this agent should be visible by default
                is_visible = agent_name in top_agents
                
                # Add data points as markers
                simple_fig.add_trace(
                    go.Scatter(
                        x=agent_data['timestamp'],
                        y=agent_data['apr'],
                        mode='markers',
                        name=f'Agent: {agent_name}',
                        marker=dict(
                            size=10, 
                            color=color_map[agent_name]
                        ),
                        hovertemplate='Time: %{x}<br>APR: %{y:.2f}<br>Agent: ' + agent_name + '<extra></extra>',
                        visible=is_visible  # Only top agents visible by default
                    )
                )
            
            # Add 2-hour moving average as a line
            simple_fig.add_trace(
                go.Scatter(
                    x=avg_apr_data_with_ma['timestamp'],
                    y=avg_apr_data_with_ma['moving_avg'],
                    mode='lines',
                    name='Moving Average APR (6h window)',
                    line=dict(width=2, color='red')  # Thinner line
                )
            )
            
            # Add infinite window moving average as another line
            simple_fig.add_trace(
                go.Scatter(
                    x=avg_apr_data_with_ma['timestamp'],
                    y=avg_apr_data_with_ma['infinite_avg'],
                    mode='lines',
                    name='Cumulative Average APR (all data)',
                    line=dict(width=4, color='green')  # Thicker solid line
                )
            )
        
        # Simplified layout with adjusted y-axis range and increased size
        simple_fig.update_layout(
            title="Babydegen agents",
            xaxis_title="Time",
            yaxis_title="Value",
                    yaxis=dict(
                        # No fixed range - let Plotly autoscale
                        autorange=True,  # Explicitly enable autoscaling
                        tickformat=".2f",  # Format tick labels with 2 decimal places
                        tickfont=dict(size=12)  # Larger font for tick labels
                    ),
            height=700,  # Increased height
            width=1400,  # Increased width
            template="plotly_white"  # Use a cleaner template
        )
        
        # Apply autoscaling to the x-axis for the fallback graph
        simple_fig.update_xaxes(
            autorange=True,  # Explicitly enable autoscaling
            tickformat="%b %d, %H:%M",
            tickangle=-30,
            tickfont=dict(size=12)
        )
        
        # Add a note about hidden agents if there are more than MAX_VISIBLE_AGENTS
        if len(unique_agents) > MAX_VISIBLE_AGENTS:
            simple_fig.add_annotation(
                text=f"Note: Only showing top {MAX_VISIBLE_AGENTS} agents by default. Toggle others in legend.",
                xref="paper", yref="paper",
                x=0.5, y=1.05,
                showarrow=False,
                font=dict(size=12, color="gray"),
                align="center"
            )
        
        # Return the simple figure
        return simple_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}")
    
    # Log detailed statistics about adjusted APR
    if 'adjusted_apr' in df.columns and df['adjusted_apr'].notna().any():
        adjusted_stats = stats_df[stats_df['avg_adjusted_apr'].notna()]
        logger.info(f"Agents with adjusted APR data: {len(adjusted_stats)} out of {len(stats_df)}")
        
        for _, row in adjusted_stats.iterrows():
            if row['agent_id'] != 'ALL':  # Skip the overall stats row
                logger.info(f"Agent {row['agent_name']} adjusted APR stats: avg={row['avg_adjusted_apr']:.2f}, min={row['min_adjusted_apr']:.2f}, max={row['max_adjusted_apr']:.2f}")
        
        # Log overall adjusted APR stats
        overall_row = stats_df[stats_df['agent_id'] == 'ALL']
        if not overall_row.empty and pd.notna(overall_row['avg_adjusted_apr'].iloc[0]):
            logger.info(f"Overall adjusted APR stats: avg={overall_row['avg_adjusted_apr'].iloc[0]:.2f}, min={overall_row['min_adjusted_apr'].iloc[0]:.2f}, max={overall_row['max_adjusted_apr'].iloc[0]:.2f}")
    
    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]
        
        # 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),
            '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,
            '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 else None
        }
        stats_list.append(stats)
    
    # Generate overall statistics
    apr_only = df[df['metric_type'] == 'APR']
    perf_only = df[df['metric_type'] == 'Performance']
    
    # Check if adjusted_apr exists and has non-null values for overall stats
    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),
        '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,
        '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 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

# Modify dashboard function to remove the diagnostics section
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")
                
                # Create compact toggle controls at the bottom of the graph
                with gr.Row(visible=True):
                    gr.Markdown("##### Toggle Graph Lines", elem_id="toggle_title")
                
                with gr.Row():
                    with gr.Column():
                        with gr.Row(elem_id="toggle_container"):
                            with gr.Column(scale=1, min_width=150):
                                apr_toggle = gr.Checkbox(label="APR Moving Average", value=True, elem_id="apr_toggle")
                            
                            with gr.Column(scale=1, min_width=150):
                                adjusted_apr_toggle = gr.Checkbox(label="Adjusted APR Moving Average", value=True, elem_id="adjusted_apr_toggle")
                
                # Add custom CSS for styling the toggle checkboxes
                gr.HTML("""
                <style>
                    /* Style for toggle checkboxes */
                    #apr_toggle .gr-checkbox {
                        accent-color: #e74c3c !important;
                    }
                    
                    #adjusted_apr_toggle .gr-checkbox {
                        accent-color: #2ecc71 !important;
                    }
                    
                    /* Make the toggle section more compact */
                    #toggle_title {
                        margin-bottom: 0;
                        margin-top: 10px;
                    }
                    
                    #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;
                    }
                </style>
                """)
                
                # Function to update the graph
                def update_apr_graph(show_apr_ma=True, show_adjusted_apr_ma=True):
                    # Generate visualization and get figure object directly
                    try:
                        combined_fig, _ = generate_apr_visualizations()
                        
                        # Update visibility of traces based on toggle values
                        for i, trace in enumerate(combined_fig.data):
                            # Check if this is a moving average trace
                            if trace.name == 'Moving Average APR (3d window)':
                                trace.visible = show_apr_ma
                            elif trace.name == 'Moving Average Adjusted APR (3d window)':
                                trace.visible = show_adjusted_apr_ma
                        
                        return combined_fig
                    except Exception as e:
                        logger.exception("Error generating APR visualization")
                        # Create error figure
                        error_fig = go.Figure()
                        error_fig.add_annotation(
                            text=f"Error: {str(e)}", 
                            x=0.5, y=0.5, 
                            showarrow=False, 
                            font=dict(size=15, color="red")
                        )
                        return error_fig
                
                # Initialize the graph on load with a placeholder
                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
                
                # Function to update the graph based on toggle states
                def update_graph_with_toggles(apr_visible, adjusted_apr_visible):
                    return update_apr_graph(apr_visible, adjusted_apr_visible)
                
                # Function to update the graph without parameters (for refresh button)
                def refresh_graph():
                    return update_apr_graph(apr_toggle.value, adjusted_apr_toggle.value)
                
                # Set up the button click event
                refresh_btn.click(fn=refresh_graph, inputs=None, outputs=[combined_graph])
                
                # Set up the toggle switch events
                apr_toggle.change(
                    fn=update_graph_with_toggles,
                    inputs=[apr_toggle, adjusted_apr_toggle],
                    outputs=[combined_graph]
                )
                
                adjusted_apr_toggle.change(
                    fn=update_graph_with_toggles,
                    inputs=[apr_toggle, adjusted_apr_toggle],
                    outputs=[combined_graph]
                )
        
    return demo

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