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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
# Initialize Flask app
app = Flask(__name__)
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
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