Update app.py

app.py

@@ -32,12 +32,20 @@ class AudioAnalyzer:
         """Initialize with a temporary directory for file storage."""
         self.temp_dir = Path(temp_dir or tempfile.mkdtemp())
         self.temp_dir.mkdir(exist_ok=True)
+        self.plot_files = []  # Track plot files for cleanup
         logger.info(f"Initialized temporary directory: {self.temp_dir}")
 
     def cleanup(self) -> None:
-        """Remove temporary directory and its contents."""
+        """Remove temporary directory and plot files."""
+        for plot_file in self.plot_files:
+            if Path(plot_file).exists():
+                try:
+                    Path(plot_file).unlink()
+                    logger.info(f"Removed plot file: {plot_file}")
+                except Exception as e:
+                    logger.warning(f"Failed to remove plot file {plot_file}: {str(e)}")
         if self.temp_dir.exists():
-            shutil.rmtree(self.temp_dir)
+            shutil.rmtree(self.temp_dir, ignore_errors=True)
             logger.info(f"Cleaned up temporary directory: {self.temp_dir}")
 
     def download_youtube_audio(self, video_url: str, progress=gr.Progress()) -> Tuple[Optional[str], str]:
@@ -73,6 +81,25 @@ class AudioAnalyzer:
             logger.error(f"Unexpected error during download: {str(e)}")
             return None, f"Error: {str(e)}"
 
+    def save_plot(self, fig, filename: str) -> Optional[str]:
+        """Save matplotlib figure to a temporary file and verify existence."""
+        try:
+            # Use NamedTemporaryFile to ensure persistence
+            with tempfile.NamedTemporaryFile(suffix='.png', delete=False, dir=self.temp_dir) as tmp_file:
+                plot_path = tmp_file.name
+            fig.savefig(plot_path, dpi=300, bbox_inches='tight', format='png')
+            plt.close(fig)
+            if not Path(plot_path).exists():
+                logger.error(f"Plot file not created: {plot_path}")
+                return None
+            self.plot_files.append(plot_path)
+            logger.info(f"Saved plot: {plot_path}")
+            return str(plot_path)
+        except Exception as e:
+            logger.error(f"Error saving plot {filename}: {str(e)}")
+            plt.close(fig)
+            return None
+
     def extract_basic_features(self, audio_path: str, sr: int = 16000, max_duration: float = 60.0, 
                              progress=gr.Progress()) -> Tuple[Optional[str], Optional[str], Optional[str]]:
         """Extract basic audio features and generate visualizations."""
@@ -100,7 +127,7 @@ class AudioAnalyzer:
                 'zero_crossing_rate': librosa.feature.zero_crossing_rate(y)[0]
             }
 
-            progress(0.5, desc="Computing Mel spectrogram...")
+            progress(0.5, desc="Computing mel spectrogram...")
             hop_length = 512
             S_mel = librosa.feature.melspectrogram(y=y, sr=sr, hop_length=hop_length, n_mels=80)
             S_dB = librosa.power_to_db(S_mel, ref=np.max)
@@ -129,9 +156,9 @@ class AudioAnalyzer:
             axes[1, 1].legend()
 
             plt.tight_layout()
-            plot_path = self.temp_dir / f"basic_features_{np.random.randint(10000)}.png"
-            plt.savefig(plot_path, dpi=300, bbox_inches='tight')
-            plt.close()
+            plot_path = self.save_plot(fig, "basic_features")
+            if not plot_path:
+                return None, None, "Failed to save feature visualizations"
 
             # Validate feature shapes
             for key in ['mfcc', 'spectral_centroid', 'spectral_rolloff', 'zero_crossing_rate']:
@@ -154,7 +181,7 @@ class AudioAnalyzer:
             """
 
             progress(1.0, desc="Analysis complete!")
-            return str(plot_path), summary, None
+            return plot_path, summary, None
 
         except Exception as e:
             logger.error(f"Error processing audio: {str(e)}")
@@ -177,9 +204,9 @@ class AudioAnalyzer:
             y_harm = librosa.effects.harmonic(y=y, margin=8)
             chroma_harm = librosa.feature.chroma_cqt(y=y_harm, sr=sr)
             chroma_filter = np.minimum(chroma_harm,
-                                    librosa.decompose.nn_filter(chroma_harm,
-                                                            aggregate=np.median,
-                                                            metric='cosine'))
+                                     librosa.decompose.nn_filter(chroma_harm,
+                                                                aggregate=np.median,
+                                                                metric='cosine'))
             chroma_smooth = scipy.ndimage.median_filter(chroma_filter, size=(1, 9))
             chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)
             chroma_cens = librosa.feature.chroma_cens(y=y, sr=sr)
@@ -200,13 +227,13 @@ class AudioAnalyzer:
                 axes[i].set_title(title)
 
             plt.tight_layout()
-            plot_path = self.temp_dir / f"chroma_features_{np.random.randint(10000)}.png"
-            plt.savefig(plot_path, dpi=300, bbox_inches='tight')
-            plt.close()
+            plot_path = self.save_plot(fig, "chroma_features")
+            if not plot_path:
+                return None, None, "Failed to save chroma visualizations"
 
             summary = "Chroma feature analysis complete! Visualizations show different chroma extraction methods for harmonic analysis."
             progress(1.0, desc="Chroma analysis complete!")
-            return str(plot_path), summary, None
+            return plot_path, summary, None
 
         except Exception as e:
             logger.error(f"Error processing chroma features: {str(e)}")
@@ -222,7 +249,7 @@ class AudioAnalyzer:
             progress(0.1, desc="Loading audio...")
             y, sr = librosa.load(audio_path, sr=sr)
 
-            progress(0.3, desc="Computing Mel spectrogram...")
+            progress(0.3, desc="Computing mel spectrogram...")
             hop_length = 512
             S_mel = librosa.feature.melspectrogram(y=y, sr=sr, hop_length=hop_length, n_mels=80)
             S_dB = librosa.power_to_db(S_mel, ref=np.max)
@@ -239,16 +266,16 @@ class AudioAnalyzer:
 
             for i in range(num_patches_to_show):
                 librosa.display.specshow(patches[..., i], y_axis='mel', x_axis='time', 
-                                        ax=axes[i], sr=sr, hop_length=hop_length)
+                                       ax=axes[i], sr=sr, hop_length=hop_length)
                 axes[i].set_title(f'Patch {i+1}')
 
             for i in range(num_patches_to_show, len(axes)):
                 axes[i].set_visible(False)
 
             plt.tight_layout()
-            plot_path = self.temp_dir / f"patches_{np.random.randint(10000)}.png"
-            plt.savefig(plot_path, dpi=300, bbox_inches='tight')
-            plt.close()
+            plot_path = self.save_plot(fig, "patches")
+            if not plot_path:
+                return None, None, "Failed to save patch visualizations"
 
             summary = f"""
 **Patch Generation Summary:**
@@ -260,7 +287,7 @@ class AudioAnalyzer:
             """
 
             progress(1.0, desc="Patch generation complete!")
-            return str(plot_path), summary, None
+            return plot_path, summary, None
 
         except Exception as e:
             logger.error(f"Error generating patches: {str(e)}")
@@ -334,11 +361,11 @@ def create_gradio_interface() -> gr.Blocks:
 
         gr.Markdown("""
         ### ℹ️ Usage Tips
-        - **Processing Limits**: 60s for basic features, 30s for chroma features to ensure fast response times
+        - **Processing Limits**: 60s for basic features, 30s for chroma features for fast response
         - **YouTube Downloads**: Ensure URLs are valid and respect YouTube's terms of service
-        - **Visualizations**: High-quality, suitable for research and educational use
-        - **Storage**: Temporary files are automatically cleaned up when the interface closes
-        - **Support**: For issues, check the [GitHub repository](https://github.com/your-repo) or contact the developer
+        - **Visualizations**: High-quality, suitable for research and education
+        - **Storage**: Temporary files are cleaned up when the interface closes
+        - **Support**: For issues, check the [GitHub repository](https://github.com/your-repo)
         """)
 
         # Event handlers