app.py

import requests
import pandas as pd
import gradio as gr
import plotly.express as px
from datetime import datetime
import json
from web3 import Web3

OPTIMISM_RPC_URL = 'https://opt-mainnet.g.alchemy.com/v2/U5gnXPYxeyH43MJ9tP8ONBQHEDRav7H0'

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

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

# Contract address
contract_address = '0x3d77596beb0f130a4415df3D2D8232B3d3D31e44'

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

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

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

def fetch_and_aggregate_transactions():
    total_services = service_registry.functions.totalSupply().call()
    aggregated_transactions = []

    for service_id in range(1, total_services + 1):
        service = service_registry.functions.getService(service_id).call()
        
        # Extract the list of agent IDs from the service data
        agent_ids = service[-1]  # Assuming the last element is the list of agent IDs
        
        # Check if 25 is in the list of agent IDs
        if 25 in agent_ids:
            agent_address = service_registry.functions.getAgentInstances(service_id).call()[1][0]
            response_transfers = get_transfers("valory", agent_address)
            aggregated_transactions.extend(response_transfers["transfers"])
    
    return aggregated_transactions

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

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

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

        # Prepare row data
        rows.append({
            "transactionId": tx["transactionId"],
            "from_address": tx["fromAddress"],
            "to_address": tx["toAddress"],
            "sending_chain": tx["sending"]["chainId"],
            "receiving_chain": tx["receiving"]["chainId"],
            "sending_token_symbol": tx["sending"]["token"]["symbol"],
            "receiving_token_symbol": tx["receiving"]["token"]["symbol"],
            "sending_amount": sending_amount,
            "receiving_amount": receiving_amount,
            "sending_amount_usd": float(tx["sending"]["amountUSD"]),
            "receiving_amount_usd": float(tx["receiving"]["amountUSD"]),
            "sending_gas_used": int(tx["sending"]["gasUsed"]),
            "receiving_gas_used": int(tx["receiving"]["gasUsed"]),
            "sending_timestamp": sending_timestamp,
            "receiving_timestamp": receiving_timestamp,
            "date": sending_timestamp.date(),  # Group by day
            "week": sending_timestamp.strftime('%Y-%W')  # Group by week
        })
    
    df = pd.DataFrame(rows)
    return df

# Function to create visualizations based on the metrics
def create_visualizations():
    transactions_data = fetch_and_aggregate_transactions()
    df = process_transactions(transactions_data)

    # Map chain IDs to chain names
    chain_name_map = {
        10: "Optimism",
        8453: "Base",
        1: "Ethereum"
    }
    df["sending_chain"] = df["sending_chain"].map(chain_name_map)
    df["receiving_chain"] = df["receiving_chain"].map(chain_name_map)

    # Ensure that chain IDs are strings for consistent grouping
    df["sending_chain"] = df["sending_chain"].astype(str)
    df["receiving_chain"] = df["receiving_chain"].astype(str)
    df['date'] = pd.to_datetime(df['date'])
    # Total transactions per chain per day
    tx_per_chain = df.groupby(["date", "sending_chain"]).size().reset_index(name="transaction_count")
    fig_tx_chain = px.bar(
        tx_per_chain,
        x="date",
        y="transaction_count",
        color="sending_chain",
        title="Chain Daily Activity: Transactions",
        labels={"sending_chain": "Transaction Chain","transaction_count": "Daily Transaction Nr"},
        barmode="stack",
        color_discrete_sequence=["purple", "darkgreen"]
    )
    fig_tx_chain.update_layout(
        xaxis_title=None,
        yaxis=dict(tickmode='linear', tick0=0, dtick=1),
        xaxis=dict(
            tickmode='array',
            tickvals=tx_per_chain['date'],
            ticktext=tx_per_chain['date'].dt.strftime('%Y-%m-%d'),
            tickangle=0,
        ),
        bargap=0.8,
        height=700,
    )
    fig_tx_chain.update_xaxes(tickformat="%Y-%m-%d")
    # Identify swap transactions
    df["is_swap"] = df.apply(lambda x: x["sending_token_symbol"] != x["receiving_token_symbol"], axis=1)

    # Total swaps per chain per day
    swaps_per_chain = df[df["is_swap"]].groupby(["date", "sending_chain"]).size().reset_index(name="swap_count")
    fig_swaps_chain = px.bar(
        swaps_per_chain,
        x="date",
        y="swap_count",
        color="sending_chain",
        title="Chain Daily Activity: Swaps",
        labels={"sending_chain": "Transaction Chain", "swap_count": "Daily Swap Nr"},
        barmode="stack",
        color_discrete_sequence=["purple", "darkgreen"]
    )
    fig_swaps_chain.update_layout(
        xaxis_title=None,
        yaxis=dict(tickmode='linear', tick0=0, dtick=1),
        xaxis=dict(
            tickmode='array',
            tickvals=swaps_per_chain['date'],
            ticktext=swaps_per_chain['date'].dt.strftime('%Y-%m-%d'),
            tickangle=0,
        ),
        bargap=0.8,
        height=700,
    )
    fig_swaps_chain.update_xaxes(tickformat="%Y-%m-%d")

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

    # Total bridges per chain per day
    bridges_per_chain = df[df["is_bridge"]].groupby(["date", "sending_chain"]).size().reset_index(name="bridge_count")
    fig_bridges_chain = px.bar(
        bridges_per_chain,
        x="date",
        y="bridge_count",
        color="sending_chain",
        title="Chain Daily Activity: Bridges",
        labels={"sending_chain": "Transaction Chain","bridge_count": "Daily Bridge Nr"},
        barmode="stack",
        color_discrete_sequence=["purple", "darkgreen"]
    )
    fig_bridges_chain.update_layout(
        xaxis_title=None,
        yaxis=dict(tickmode='linear', tick0=0, dtick=1),
        xaxis=dict(
            tickmode='array',
            tickvals=bridges_per_chain['date'],
            ticktext=bridges_per_chain['date'].dt.strftime('%Y-%m-%d'),
            tickangle=0,
        ),
        bargap=0.8,
        height=700,
    )
    fig_bridges_chain.update_xaxes(tickformat="%Y-%m-%d")
    # Investment per agent per day
    investment_per_agent = df.groupby(["date", "from_address", "sending_chain"])["sending_amount_usd"].sum().reset_index()
    fig_investment_agent = px.bar(
        investment_per_agent,
        x="date",
        y="sending_amount_usd",
        color="sending_chain",
        title="Amount of Investment (USD) per Day",
        labels={"sending_chain": "Transaction Chain","sending_amount_usd": "Investment Amount (USD)"},
        barmode="stack",
        color_discrete_sequence=["purple", "darkgreen"]
    )
    fig_investment_agent.update_layout(
        xaxis_title=None,
        yaxis=dict(
            title="Investment Amount (USD)",
            tickmode='auto',
            nticks=10,
            tickformat='.2f'  # Show 2 decimal places
        ),
        xaxis=dict(
            tickmode='array',
            tickvals=investment_per_agent['date'],
            ticktext=investment_per_agent['date'].dt.strftime('%Y-%m-%d'),
            tickangle=0,
        ),
        bargap=0.8,
        height=700,
    )
    fig_investment_agent.update_xaxes(tickformat="%Y-%m-%d")
    return fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent

# Gradio interface
def dashboard():
    with gr.Blocks() as demo:
        gr.Markdown("# Valory Transactions Dashboard")

        # Fetch and display visualizations
        with gr.Tab("Transactions"):
            fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent = create_visualizations()
            gr.Plot(fig_tx_chain)
        
        with gr.Tab("Swaps"):
            fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent = create_visualizations()
            gr.Plot(fig_swaps_chain)
        
        with gr.Tab("Bridges"):
            fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent = create_visualizations()
            gr.Plot(fig_bridges_chain)

        with gr.Tab("Investment"):
            fig_tx_chain, fig_swaps_chain, fig_bridges_chain, fig_investment_agent = create_visualizations()
            gr.Plot(fig_investment_agent)

    return demo

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