app.py

import streamlit as st
import pandas as pd
from flask import Flask, request, jsonify
from datetime import datetime

# Initialize a Flask app to handle POST requests
app = Flask(__name__)

# In-memory storage for scores
submitted_scores = []

@app.route('/submit_score', methods=['POST'])
def submit_score():
    try:
        # Extract data from the incoming JSON request
        data = request.get_json()
        validator_id = data['validator_id']
        miner_id = data['miner_id']
        score = data['score']
        model_type = data['model_type']
        timestamp = data['timestamp']

        # Store the submitted score in memory
        submitted_scores.append({
            "validator_id": validator_id,
            "miner_id": miner_id,
            "score": score,
            "model_type": model_type,
            "timestamp": timestamp
        })

        return jsonify({"message": "Score submitted successfully"}), 200
    except Exception as e:
        return jsonify({"error": str(e)}), 400

# Run the Streamlit app
st.title("Top 10 Miner Scores")

# Function to calculate top miners
def calculate_top_miners():
    miner_scores = {}
    for entry in submitted_scores:
        miner_id = entry["miner_id"]
        score = entry["score"]
        
        if miner_id not in miner_scores:
            miner_scores[miner_id] = {"SNR": 0, "HNR": 0, "CLAP": 0, "count": 0}
        
        miner_scores[miner_id]["SNR"] += score["SNR"]
        miner_scores[miner_id]["HNR"] += score["HNR"]
        miner_scores[miner_id]["CLAP"] += score["CLAP"]
        miner_scores[miner_id]["count"] += 1

    for miner_id, scores in miner_scores.items():
        scores["SNR"] /= scores["count"]
        scores["HNR"] /= scores["count"]
        scores["CLAP"] /= scores["count"]
    
    # Sort and return top 10 miners
    sorted_miners = sorted(miner_scores.items(), key=lambda x: (x[1]["SNR"] + x[1]["HNR"] + x[1]["CLAP"]), reverse=True)
    return sorted_miners[:10]

if st.button("Display Top 10 Scores"):
    top_miners = calculate_top_miners()
    st.write("Top 10 Miners based on aggregated scores:")
    
    # Prepare data for table
    miner_data = []
    for miner, scores in top_miners:
        miner_data.append([miner, scores["SNR"], scores["HNR"], scores["CLAP"]])
    
    df = pd.DataFrame(miner_data, columns=["Miner ID", "SNR", "HNR", "CLAP"])
    st.table(df)

# This is necessary to run Flask alongside Streamlit on Hugging Face
if __name__ == '__main__':
    app.run(port=8501, host="0.0.0.0")