Create app.py

app.py

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+import gradio as gr
+import torch
+import joblib
+import librosa
+import numpy as np
+
+# Load model (adjust paths as needed)
+model = torch.load("voice_recognition_fullmodel.pth")
+label_encoder = joblib.load("label_encoder.joblib")
+
+def predict(audio_file):
+    # Extract features
+    features = extract_features(audio_file)
+    if features is None:
+        return "Error processing audio"
+    
+    # Prepare input
+    input_tensor = torch.tensor(features).unsqueeze(0).unsqueeze(0).float()
+    
+    # Predict
+    with torch.no_grad():
+        outputs = model(input_tensor)
+        _, predicted = torch.max(outputs, 1)
+    
+    user = label_encoder.inverse_transform([predicted.item()])[0]
+    return f"Recognized user: {user}"
+
+# Create interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs="text",
+    title="Voice Recognition Security System",
+    description="Upload an audio file or record your voice to test user recognition."
+)
+
+iface.launch()