Added Flask API for score submission

app.py

@@ -1,22 +1,42 @@
 import streamlit as st
 import pandas as pd
-from datetime import datetime, timedelta
-import random
+from flask import Flask, request, jsonify
+from datetime import datetime
 
-# In-memory storage for validator submissions
+# Initialize a Flask app to handle POST requests
+app = Flask(__name__)
+
+# In-memory storage for scores
 submitted_scores = []
 
-# Function to submit score
-def submit_score(validator_id, miner_id, score, model_type, timestamp):
-    submitted_scores.append({
-        "validator_id": validator_id,
-        "miner_id": miner_id,
-        "score": score,
-        "model_type": model_type,
-        "timestamp": timestamp
-    })
-
-# Function to calculate the average score for each miner
+@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:
@@ -30,30 +50,28 @@ def calculate_top_miners():
         miner_scores[miner_id]["HNR"] += score["HNR"]
         miner_scores[miner_id]["CLAP"] += score["CLAP"]
         miner_scores[miner_id]["count"] += 1
-    
-    # Calculate average score
+
     for miner_id, scores in miner_scores.items():
         scores["SNR"] /= scores["count"]
         scores["HNR"] /= scores["count"]
         scores["CLAP"] /= scores["count"]
     
-    # Sort by total score (sum of SNR, HNR, CLAP)
+    # 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]  # Return top 10 miners
+    return sorted_miners[:10]
 
-# Streamlit UI
-st.title("Top 10 Miner Scores")
-
-# Display top 10 miners
 if st.button("Display Top 10 Scores"):
     top_miners = calculate_top_miners()
     st.write("Top 10 Miners based on aggregated scores:")
     
-    # Prepare data for the table
+    # 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")

requirements.txt

@@ -0,0 +1,2 @@
+streamlit
+flask