app.py

import requests
import pandas as pd
import gradio as gr
import plotly.graph_objects as go
import plotly.express as px
from datetime import datetime, timedelta
import json
from web3 import Web3
import os
from app_trans_new import create_transcation_visualizations,create_active_agents_visualizations
# 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.")

def get_transfers(integrator: str, wallet: str) -> str:
    url = f"https://li.quest/v1/analytics/transfers?&wallet={wallet}&fromTimestamp=1726165800"
    headers = {"accept": "application/json"}
    response = requests.get(url, headers=headers)
    return response.json()

def fetch_and_aggregate_transactions():
    aggregated_transactions = []
    daily_agent_counts = {}
    seen_agents = set()

    for chain_name, service_registry in service_registries.items():
        web3 = web3_instances[chain_name]
        total_services = service_registry.functions.totalSupply().call()
        for service_id in range(1, total_services + 1):
            service = service_registry.functions.getService(service_id).call()
            agent_ids = service[-1]

            if 40 in agent_ids or 25 in agent_ids:
                agent_address = service_registry.functions.getAgentInstances(service_id).call()[1][0]
                response_transfers = get_transfers("valory", agent_address)
                transfers = response_transfers.get("transfers", [])
                
                if isinstance(transfers, list):
                    aggregated_transactions.extend(transfers)
                
                # Track the daily number of agents
                current_date = ""
                creation_event = service_registry.events.CreateService.create_filter(from_block=0, argument_filters={'serviceId': service_id}).get_all_entries()
                if creation_event:
                    block_number = creation_event[0]['blockNumber']
                    block = web3.eth.get_block(block_number)
                    creation_timestamp = datetime.fromtimestamp(block['timestamp'])
                    date_str = creation_timestamp.strftime('%Y-%m-%d')
                    current_date = date_str
                    
                    # Ensure each agent is only counted once based on first registered date
                    if agent_address not in seen_agents:
                        seen_agents.add(agent_address)
                        if date_str not in daily_agent_counts:
                            daily_agent_counts[date_str] = set()
                        daily_agent_counts[date_str].add(agent_address)                                
    daily_agent_counts = {date: len(agents) for date, agents in daily_agent_counts.items()}
    return aggregated_transactions, daily_agent_counts

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

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

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

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

    return df_transactions, df_agents, df_agents_weekly

# Function to create visualizations based on the metrics
def create_visualizations():
    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)
    weekly_agents_df = df_agents.groupby('week').agg({'agent_count': 'sum'}).reset_index()
    weekly_agents_df.rename(columns={'agent_count': 'weekly_agent_count'}, inplace=True)

    merged_df = pd.merge(daily_agents_df, df_agents[['date', 'week']], on='date', how='left')
    weekly_merged_df = pd.merge(merged_df, weekly_agents_df, on='week', how='left')
    
    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, 'weekly_agent_count'] -= 1
    fig_agents_registered = go.Figure(data=[
        go.Bar(
            name='Daily nr of Registered Agents',
            x=weekly_merged_df['date'],
            y=weekly_merged_df['daily_agent_count'],
            opacity=0.7,
            marker_color='blue'
        ),
        go.Bar(
            name='Total Weekly Nr of Registered Agents',
            x=weekly_merged_df['date'],
            y=weekly_merged_df['weekly_agent_count'],
            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(
            tickmode='array',
            tickvals=weekly_merged_df['date'],
            ticktext=[d.strftime("%b %d") for d in weekly_merged_df['date']],
            tickangle=-45
        ),
        bargap=0.6,
        height=600,
        width=1200,
        margin=dict(l=50, r=50, t=50, b=50),
        showlegend=True,
        template='plotly_white'
    )

    return fig_swaps_chain, fig_bridges_chain, fig_agents_registered,fig_tvl

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

        fig_swaps_chain, fig_bridges_chain, fig_agents_registered,fig_tvl = create_visualizations()    
        with gr.Tab("Swaps Daily"):
            gr.Plot(fig_swaps_chain)
        
        with gr.Tab("Bridges Daily"):
            gr.Plot(fig_bridges_chain)
        
        with gr.Tab("Nr of Agents Registered"):
            gr.Plot(fig_agents_registered)

        with gr.Tab("DAA"):
            fig_agents_with_transactions_daily = create_active_agents_visualizations()
            gr.Plot(fig_agents_with_transactions_daily)    

        with gr.Tab("Total Value Locked"):
            gr.Plot(fig_tvl)     
        
    return demo

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