Update app.py

app.py

@@ -60,6 +60,50 @@ def analyze_audio_files(files, folder_path):
             S_mean = np.mean(S, axis=1)
             freqs = np.linspace(0, sr / 2, len(S_mean))
 
+            # Plot the waveform
+            fig_waveform = plt.figure(figsize=(8, 4))
+            librosa.display.waveshow(y, sr=sr, alpha=0.5)
+            plt.title('Waveform', fontsize=14)
+            plt.xlabel('Time (s)', fontsize=12)
+            plt.ylabel('Amplitude', fontsize=12)
+            plt.tight_layout()
+            waveform_image = io.BytesIO()
+            plt.savefig(waveform_image, format='png', bbox_inches='tight')
+            plt.close(fig_waveform)
+            waveform_image.seek(0)
+            waveform_base64 = base64.b64encode(
+                waveform_image.read()).decode('utf-8')
+            waveform_html = f'<img src="data:image/png;base64,{waveform_base64}" alt="Waveform">'
+
+            # Calculate spectral features: spectral centroid, spectral bandwidth, and spectral rolloff
+            spectral_centroids = librosa.feature.spectral_centroid(y=y, sr=sr)[
+                0]
+            spectral_bandwidth = librosa.feature.spectral_bandwidth(y=y, sr=sr)[
+                0]
+            spectral_rolloff = librosa.feature.spectral_rolloff(
+                y=y, sr=sr, roll_percent=0.85)[0]
+            times = librosa.times_like(spectral_centroids)
+
+            # Plot the spectral features
+            fig_spectral_features = plt.figure(figsize=(8, 4))
+            plt.semilogy(times, spectral_centroids, label='Spectral Centroid')
+            plt.semilogy(times, spectral_bandwidth, label='Spectral Bandwidth')
+            plt.semilogy(times, spectral_rolloff,
+                         label='Spectral Rolloff', linestyle='--')
+            plt.title('Spectral Features', fontsize=14)
+            plt.xlabel('Time (s)', fontsize=12)
+            plt.ylabel('Hz', fontsize=12)
+            plt.legend(loc='upper right')
+            plt.tight_layout()
+            spectral_features_image = io.BytesIO()
+            plt.savefig(spectral_features_image,
+                        format='png', bbox_inches='tight')
+            plt.close(fig_spectral_features)
+            spectral_features_image.seek(0)
+            spectral_features_base64 = base64.b64encode(
+                spectral_features_image.read()).decode('utf-8')
+            spectral_features_html = f'<img src="data:image/png;base64,{spectral_features_base64}" alt="Spectral Features">'
+
             # Plot the frequency spectrum
             fig1 = plt.figure(figsize=(8, 4))
             plt.semilogx(freqs, 20 * np.log10(S_mean + 1e-10))  # Avoid log(0)
@@ -160,6 +204,10 @@ def analyze_audio_files(files, folder_path):
             # Combine text and images into HTML
             output_html += f"""
             {output_text}
+            <h4 style="font-size:20px;">Waveform</h4>
+            {waveform_html}
+            <h4 style="font-size:20px;">Spectral Features</h4>
+            {spectral_features_html}
             <h4 style="font-size:20px;">Frequency Spectrum</h4>
             {spectrum_html}
             <h4 style="font-size:20px;">Spectrogram</h4>