import requests
import pandas as pd
import gradio as gr
import plotly.graph_objects as go
import plotly.express as px
from datetime import datetime, timedelta
import json
# 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
# Comment out the import for now and replace with dummy functions
# from app_trans_new import create_transcation_visualizations,create_active_agents_visualizations
# Import APR visualization functions from the new module
from apr_visualization import generate_apr_visualizations

# 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 containers for plotly figures
                per_agent_graph = gr.Plot(label="APR Per Agent")
                combined_graph = gr.Plot(label="Combined APR (All Agents)")
                
                # Function to update both graphs
                def update_apr_graphs():
                    # Generate visualizations and get figure objects directly
                    per_agent_fig, combined_fig, _ = generate_apr_visualizations()
                    return per_agent_fig, combined_fig
                
                # Set up the button click event
                refresh_btn.click(
                    fn=update_apr_graphs,
                    inputs=[],
                    outputs=[per_agent_graph, combined_graph]
                )
                
                # Initialize the graphs on load
                # We'll use placeholder figures initially
                import plotly.graph_objects as go
                placeholder_fig = go.Figure()
                placeholder_fig.add_annotation(
                    text="Click 'Refresh APR Data' to load APR graphs", 
                    x=0.5, y=0.5, 
                    showarrow=False, 
                    font=dict(size=15)
                )
                per_agent_graph.value = placeholder_fig
                combined_graph.value = placeholder_fig
        
    return demo

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