app.py

import os
import pickle
import joblib
import numpy as np
from flask_cors import CORS
from flask import Flask, request, render_template, jsonify
from werkzeug.utils import secure_filename
from extract import extract_features  # Import feature extractor
from fastapi import FastAPI

app = FastAPI()

CORS(app)  # Allow all cross-origin requests

# Set upload folder and allowed file types
UPLOAD_FOLDER = "uploads"
ALLOWED_EXTENSIONS = {"wav", "mp3", "ogg", "m4a"}
app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER
os.makedirs(UPLOAD_FOLDER, exist_ok=True)

# Load trained model, scaler, and feature list
model_path = "models/gender_model_lr.pkl"
scaler_path = "models/scaler_gender_model_lr.pkl"
feature_list_path = "models/feature_list.pkl"

model = joblib.load(model_path)
scaler = joblib.load(scaler_path)
with open(feature_list_path, "rb") as f:
    feature_list = pickle.load(f)

print("✅ Model, Scaler, and Feature List Loaded Successfully!")

# Function to check valid file extensions
def allowed_file(filename):
    return "." in filename and filename.rsplit(".", 1)[1].lower() in ALLOWED_EXTENSIONS

# Route to render the HTML interface
@app.route("/")
def index():
    return render_template("index.html")

# Route to handle file upload and prediction
@app.route("/predict", methods=["POST"])
def predict():
    if "audio" not in request.files:
        print("❌ No file uploaded")
        return jsonify({"error": "No file uploaded"}), 400
    
    file = request.files["audio"]
    print(f"📥 Received file: {file.filename}, Type: {file.content_type}")  # ✅ Debugging line

    if file.filename == "":
        return jsonify({"error": "No selected file"}), 400

    if file and allowed_file(file.filename):
        filename = secure_filename(file.filename)
        filepath = os.path.join(app.config["UPLOAD_FOLDER"], filename)
        file.save(filepath)

        print(f"🟢 Processing file: {filename}")

        try:
            # Extract features
            features = extract_features(filepath)
            if features is None:
                return jsonify({"error": "Feature extraction failed"}), 500
            
            print(f"🟢 Extracted {len(features)} features.")

            # Scale features
            features_scaled = scaler.transform([features])
            print("🟢 Features scaled successfully.")

            # Predict gender
            prediction = model.predict(features_scaled)[0]
            confidence = model.predict_proba(features_scaled)[0]
            print("🟢 Prediction completed.")

            # Format response
            result = {
                "gender": "Female" if prediction == 1 else "Male",
                "confidence": float(max(confidence)),
                "age_group": "Unknown"  # Temporary fix to avoid breaking frontend
            }

            print(f"✅ Result: {result}")

            return jsonify(result)

        except Exception as e:
            print(f"❌ Error: {e}")
            return jsonify({"error": str(e)}), 500
        finally:
            os.remove(filepath)  # Delete temp file

    return jsonify({"error": "Invalid file format"}), 400

# Run Flask app
if __name__ == "__main__":
    app.run(debug=True, use_reloader=False)  # Disable reloader