Update app.py

app.py

@@ -1,99 +1,100 @@
-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
+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