Update app.py

app.py

@@ -32,25 +32,32 @@ model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
 def analyze_voice(audio_file):
     """Analyze voice for health indicators."""
     try:
+        # Log audio file info
+        print(f"Processing audio file: {audio_file}")
+        
         # Load audio file
         audio, sr = librosa.load(audio_file, sr=16000)
+        print(f"Audio shape: {audio.shape}, Sampling rate: {sr}, Duration: {len(audio)/sr:.2f}s")
         
         # Process audio for Wav2Vec2
         inputs = processor(audio, sampling_rate=16000, return_tensors="pt", padding=True)
+        print(f"Input tensor shape: {inputs['input_values'].shape}")
+        
         with torch.no_grad():
             outputs = model(**inputs)
         
         # Extract features
         features = outputs.last_hidden_state.mean(dim=1).numpy()
+        print(f"Features shape: {features.shape}, Sample values: {features[0][:5]}")
         
-        # Mock health analysis (for testing)
+        # Mock health analysis
         respiratory_score = np.mean(features)
         mental_health_score = np.std(features)
         
-        # Debug: Print scores
+        # Log scores
         print(f"Respiratory Score: {respiratory_score:.4f}, Mental Health Score: {mental_health_score:.4f}")
         
-        # Adjusted thresholds for testing
+        # Threshold-based feedback
         feedback = ""
         if respiratory_score > 0.1:
             feedback += f"Possible respiratory issue detected (score: {respiratory_score:.4f}); consult a doctor. "
@@ -67,7 +74,7 @@ def analyze_voice(audio_file):
         if sf:
             store_in_salesforce(audio_file, feedback, respiratory_score, mental_health_score)
         
-        # Clean up temporary audio file (for HIPAA/GDPR compliance)
+        # Clean up temporary audio file
         try:
             os.remove(audio_file)
             print(f"Deleted temporary audio file: {audio_file}")
@@ -93,7 +100,7 @@ def store_in_salesforce(audio_file, feedback, respiratory_score, mental_health_s
 
 def test_with_sample_audio():
     """Test the app with a sample audio file."""
-    sample_audio_path = "audio_samples/sample.wav"  # Adjust if using common_voice_sample.wav
+    sample_audio_path = "audio_samples/sample.wav"
     if os.path.exists(sample_audio_path):
         return analyze_voice(sample_audio_path)
     return "Sample audio file not found."
@@ -108,5 +115,5 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    print(test_with_sample_audio())  # Run test on startup
+    print(test_with_sample_audio())
     iface.launch(server_name="0.0.0.0", server_port=7860)