showastresult

app.py

@@ -104,22 +104,29 @@ music_analyzer = MusicAnalyzer()
 
 def extract_audio_features(audio_file):
     """Extract audio features from an audio file."""
-    # Load the audio file using utility function
-    y, sr = load_audio(audio_file, SAMPLE_RATE)
-    
-    # Get audio duration in seconds
-    duration = extract_audio_duration(y, sr)
-    
-    # Extract MFCCs for genre classification (may not be needed with the pipeline)
-    mfccs_mean = extract_mfcc_features(y, sr, n_mfcc=20)
-    
-    return {
-        "features": mfccs_mean,
-        "duration": duration,
-        "waveform": y,
-        "sample_rate": sr,
-        "path": audio_file  # Keep path for the pipeline
-    }
+    try:
+        # Load the audio file using utility function
+        y, sr = load_audio(audio_file, SAMPLE_RATE)
+        
+        if y is None or sr is None:
+            raise ValueError("Failed to load audio data")
+        
+        # Get audio duration in seconds
+        duration = extract_audio_duration(y, sr)
+        
+        # Extract MFCCs for genre classification (may not be needed with the pipeline)
+        mfccs_mean = extract_mfcc_features(y, sr, n_mfcc=20)
+        
+        return {
+            "features": mfccs_mean,
+            "duration": duration,
+            "waveform": y,
+            "sample_rate": sr,
+            "path": audio_file  # Keep path for the pipeline
+        }
+    except Exception as e:
+        print(f"Error extracting audio features: {str(e)}")
+        raise ValueError(f"Failed to extract audio features: {str(e)}")
 
 def classify_genre(audio_data):
     """Classify the genre of the audio using the loaded model."""
@@ -313,27 +320,46 @@ def process_audio(audio_file):
         audio_data = extract_audio_features(audio_file)
         
         # First check if it's music
-        is_music, ast_results = detect_music(audio_data)
+        try:
+            is_music, ast_results = detect_music(audio_data)
+        except Exception as e:
+            print(f"Error in music detection: {str(e)}")
+            return f"Error in music detection: {str(e)}", None, []
+            
         if not is_music:
-            return "The uploaded audio does not appear to be music. Please upload a music file.", None, None
+            return "The uploaded audio does not appear to be music. Please upload a music file.", None, ast_results
         
         # Classify genre
-        top_genres = classify_genre(audio_data)
-        
-        # Format genre results using utility function
-        genre_results = format_genre_results(top_genres)
+        try:
+            top_genres = classify_genre(audio_data)
+            # Format genre results using utility function
+            genre_results = format_genre_results(top_genres)
+        except Exception as e:
+            print(f"Error in genre classification: {str(e)}")
+            return f"Error in genre classification: {str(e)}", None, ast_results
         
         # Analyze music emotions and themes
-        emotion_results = music_analyzer.analyze_music(audio_file)
+        try:
+            emotion_results = music_analyzer.analyze_music(audio_file)
+        except Exception as e:
+            print(f"Error in emotion analysis: {str(e)}")
+            # Continue even if emotion analysis fails
+            emotion_results = {"summary": {"tempo": 0, "key": "Unknown", "mode": "", "primary_emotion": "Unknown", "primary_theme": "Unknown"}}
         
         # Generate lyrics based on top genre and emotion analysis
-        primary_genre, _ = top_genres[0]
-        lyrics = generate_lyrics(primary_genre, audio_data["duration"], emotion_results)
+        try:
+            primary_genre, _ = top_genres[0]
+            lyrics = generate_lyrics(primary_genre, audio_data["duration"], emotion_results)
+        except Exception as e:
+            print(f"Error generating lyrics: {str(e)}")
+            lyrics = f"Error generating lyrics: {str(e)}"
         
         return genre_results, lyrics, ast_results
     
     except Exception as e:
-        return f"Error processing audio: {str(e)}", None, None
+        error_msg = f"Error processing audio: {str(e)}"
+        print(error_msg)
+        return error_msg, None, []
 
 # Create Gradio interface
 with gr.Blocks(title="Music Genre Classifier & Lyrics Generator") as demo:
@@ -359,21 +385,34 @@ with gr.Blocks(title="Music Genre Classifier & Lyrics Generator") as demo:
             # Process audio and get genre, lyrics, and AST results
             genre_results, lyrics, ast_results = process_audio(audio_file)
             
+            # Check if we got an error message instead of results
+            if isinstance(genre_results, str) and genre_results.startswith("Error"):
+                return genre_results, "Error in emotion analysis", "Error in audio classification", None
+            
             # Format emotion analysis results
-            emotion_results = music_analyzer.analyze_music(audio_file)
-            emotion_text = f"Tempo: {emotion_results['summary']['tempo']:.1f} BPM\n"
-            emotion_text += f"Key: {emotion_results['summary']['key']} {emotion_results['summary']['mode']}\n"
-            emotion_text += f"Primary Emotion: {emotion_results['summary']['primary_emotion']}\n"
-            emotion_text += f"Primary Theme: {emotion_results['summary']['primary_theme']}"
+            try:
+                emotion_results = music_analyzer.analyze_music(audio_file)
+                emotion_text = f"Tempo: {emotion_results['summary']['tempo']:.1f} BPM\n"
+                emotion_text += f"Key: {emotion_results['summary']['key']} {emotion_results['summary']['mode']}\n"
+                emotion_text += f"Primary Emotion: {emotion_results['summary']['primary_emotion']}\n"
+                emotion_text += f"Primary Theme: {emotion_results['summary']['primary_theme']}"
+            except Exception as e:
+                print(f"Error in emotion analysis: {str(e)}")
+                emotion_text = f"Error in emotion analysis: {str(e)}"
             
             # Format AST classification results
-            ast_text = "Audio Classification Results (AST Model):\n"
-            for result in ast_results[:5]:  # Show top 5 results
-                ast_text += f"{result['label']}: {result['score']*100:.2f}%\n"
+            if ast_results and isinstance(ast_results, list):
+                ast_text = "Audio Classification Results (AST Model):\n"
+                for result in ast_results[:5]:  # Show top 5 results
+                    ast_text += f"{result['label']}: {result['score']*100:.2f}%\n"
+            else:
+                ast_text = "No valid audio classification results available."
             
             return genre_results, emotion_text, ast_text, lyrics
         except Exception as e:
-            return f"Error: {str(e)}", "Error in emotion analysis", "Error in audio classification", None
+            error_msg = f"Error: {str(e)}"
+            print(error_msg)
+            return error_msg, "Error in emotion analysis", "Error in audio classification", None
     
     submit_btn.click(
         fn=display_results,

example.py

@@ -23,33 +23,44 @@ def main():
     # Call the main processing function
     genre_results, lyrics, ast_results = process_audio(audio_file)
     
-    # Get emotion analysis results
-    emotion_results = music_analyzer.analyze_music(audio_file)
-    
-    # Print results
-    print("\n" + "="*50)
-    print("GENRE CLASSIFICATION RESULTS:")
-    print("="*50)
-    print(genre_results)
+    # Check if we got an error message
+    if isinstance(genre_results, str) and genre_results.startswith("Error"):
+        print(f"Error processing audio: {genre_results}")
+        return
     
-    print("\n" + "="*50)
-    print("EMOTION ANALYSIS RESULTS:")
-    print("="*50)
-    print(f"Tempo: {emotion_results['summary']['tempo']:.1f} BPM")
-    print(f"Key: {emotion_results['summary']['key']} {emotion_results['summary']['mode']}")
-    print(f"Primary Emotion: {emotion_results['summary']['primary_emotion']}")
-    print(f"Primary Theme: {emotion_results['summary']['primary_theme']}")
+    # Get emotion analysis results
+    try:
+        emotion_results = music_analyzer.analyze_music(audio_file)
+        
+        # Print results
+        print("\n" + "="*50)
+        print("GENRE CLASSIFICATION RESULTS:")
+        print("="*50)
+        print(genre_results)
+        
+        print("\n" + "="*50)
+        print("EMOTION ANALYSIS RESULTS:")
+        print("="*50)
+        print(f"Tempo: {emotion_results['summary']['tempo']:.1f} BPM")
+        print(f"Key: {emotion_results['summary']['key']} {emotion_results['summary']['mode']}")
+        print(f"Primary Emotion: {emotion_results['summary']['primary_emotion']}")
+        print(f"Primary Theme: {emotion_results['summary']['primary_theme']}")
+    except Exception as e:
+        print(f"\nError in emotion analysis: {str(e)}")
     
     print("\n" + "="*50)
     print("AUDIO CLASSIFICATION RESULTS (AST):")
     print("="*50)
-    for result in ast_results[:5]:  # Show top 5 results
-        print(f"{result['label']}: {result['score']*100:.2f}%")
+    if ast_results and isinstance(ast_results, list) and len(ast_results) > 0:
+        for result in ast_results[:5]:  # Show top 5 results
+            print(f"{result['label']}: {result['score']*100:.2f}%")
+    else:
+        print("No audio classification results available.")
     
     print("\n" + "="*50)
     print("GENERATED LYRICS:")
     print("="*50)
-    print(lyrics)
+    print(lyrics if lyrics else "No lyrics generated.")
 
 if __name__ == "__main__":
     main()