Update app.py

app.py

@@ -1,64 +1,40 @@
-import os
+import gradio as gr
 import librosa
 import numpy as np
 import tensorflow as tf
-from flask import Flask, request, jsonify
 from tensorflow.keras.models import load_model
 
-# Load your pre-trained model (make sure it's in the same directory or provide the correct path)
-MODEL_PATH = 'voice_authentication_model.keras'
-model = load_model(MODEL_PATH)
+# Load your pre-trained model (make sure it's in the same directory or provide a path)
+model = load_model('voice_authentication_model.keras')  # Replace with your model's filename
 
-app = Flask(__name__)
+# Function to extract MFCC features and make a prediction
+def predict_user_or_non_user(audio):
+    # Load the audio file using librosa
+    y, sr = librosa.load(audio, sr=None)  # sr=None to keep the original sampling rate
+    
+    # Extract MFCC features from the audio
+    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)  # You can adjust n_mfcc as needed
+    mfccs = np.mean(mfccs.T, axis=0)  # Take the mean of MFCCs over time to reduce dimension
+    
+    # Reshape the MFCCs to match the input shape expected by the model
+    mfccs = mfccs.reshape(1, -1)  # Reshape to 1 sample, with the number of features
+    
+    # Predict the class (user or non-user)
+    prediction = model.predict(mfccs)
+    
+    # Convert prediction to readable format
+    if prediction > 0.5:
+        return "User"
+    else:
+        return "Non-User"
 
-# Helper function to extract MFCC features
-def extract_mfcc_features(audio_path):
-    try:
-        # Load the audio file using librosa
-        y, sr = librosa.load(audio_path, sr=None)  # sr=None keeps the original sampling rate
-        
-        # Extract MFCC features
-        mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)  # Adjust n_mfcc as needed
-        mfccs = np.mean(mfccs.T, axis=0)  # Average MFCCs over time to reduce dimensions
-        
-        # Reshape MFCCs for model input
-        return mfccs.reshape(1, -1)  # 1 sample with the number of features
-    except Exception as e:
-        raise ValueError(f"Error processing audio file: {e}")
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict_user_or_non_user,  # The function to call when an audio input is given
+    inputs=gr.Audio(type="filepath"),  # Corrected audio input setup
+    outputs="text",  # Output will be text (User or Non-user)
+    live=True  # Live mode so the interface updates in real-time
+)
 
-# Define the home route for basic health check
-@app.route('/')
-def home():
-    return "Voice Authentication API is running!"
-
-# Define the prediction endpoint
-@app.route('/predict', methods=['POST'])
-def predict():
-    try:
-        # Check if audio file is present in the request
-        if 'file' not in request.files:
-            return jsonify({"error": "No file provided. Please upload an audio file."}), 400
-        
-        # Save the uploaded file to a temporary location
-        audio_file = request.files['file']
-        file_path = os.path.join("temp_audio.wav")
-        audio_file.save(file_path)
-        
-        # Extract features from the audio file
-        mfccs = extract_mfcc_features(file_path)
-        
-        # Perform prediction
-        prediction = model.predict(mfccs)
-        
-        # Convert prediction to user-readable format
-        result = "User" if prediction > 0.5 else "Non-User"
-        
-        # Clean up the temporary file
-        os.remove(file_path)
-        
-        return jsonify({"prediction": result})
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
+# Launch the interface
+iface.launch() this is my app.py code