app.py

import os
import pickle
import joblib
import numpy as np
import traceback
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

# Initialize Flask app
app = Flask(__name__)
CORS(app)  # Allow all cross-origin requests

# Set upload folder and allowed file types
UPLOAD_FOLDER = "/tmp/uploads"  # Use a temporary directory for compatibility
ALLOWED_EXTENSIONS = {"wav", "mp3", "ogg", "m4a"}

# Ensure upload folder exists
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER

# Load trained model, scaler, and feature list
MODEL_DIR = os.path.join(os.getcwd(), "models")
model_path = os.path.join(MODEL_DIR, "gender_model_svm.pkl")
scaler_path = os.path.join(MODEL_DIR, "scaler_gender_model_svm.pkl")
feature_list_path = os.path.join(MODEL_DIR, "feature_list.pkl")

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

EXPECTED_FEATURE_COUNT = len(feature_list)  # Ensure features match model training
print(f"✅ Model, Scaler, and Feature List Loaded Successfully! (Expecting {EXPECTED_FEATURE_COUNT} features)")

# 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():
    try:
        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}")

            # Extract features
            features = extract_features(filepath)
            if features is None:
                print("❌ Feature extraction failed")
                return jsonify({"error": "Feature extraction failed"}), 500

            print(f"🟢 Extracted {len(features)} features")

            # Validate feature count
            if len(features) != EXPECTED_FEATURE_COUNT:
                print(f"❌ Feature count mismatch! Extracted: {len(features)}, Expected: {EXPECTED_FEATURE_COUNT}")
                return jsonify({"error": f"Feature count mismatch. Expected {EXPECTED_FEATURE_COUNT}, got {len(features)}"}), 500

            # 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 placeholder
            }

            print(f"✅ Prediction Result: {result}")
            return jsonify(result)

    except Exception as e:
        print(f"❌ ERROR: {e}")
        traceback.print_exc()  # 🔴 Print full error traceback
        return jsonify({"error": str(e)}), 500

    finally:
        # Cleanup: Remove the file to free storage
        if os.path.exists(filepath):
            os.remove(filepath)
            print(f"🗑️ Deleted temp file: {filepath}")

# Run Flask app
if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860, debug=True, use_reloader=False)