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app.py

@@ -36,7 +36,6 @@ def create_spectrogram(audio_path, title="Spectrogram"):
         y, sr = librosa.load(audio_path, sr=22050, mono=True)
         mel_spec = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128, fmax=8000)
         mel_db = librosa.power_to_db(mel_spec, ref=np.max)
-        
         fig, ax = plt.subplots(figsize=(10, 4))
         im = ax.imshow(mel_db, aspect="auto", origin="lower", cmap="viridis", interpolation="nearest")
         ax.set_title(title)
@@ -44,7 +43,6 @@ def create_spectrogram(audio_path, title="Spectrogram"):
         ax.set_ylabel("Mel Frequency")
         plt.colorbar(im, ax=ax, format="%+2.0f dB")
         plt.tight_layout()
-        
         temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
         plt.savefig(temp_file.name, dpi=100, bbox_inches="tight")
         plt.close()
@@ -55,21 +53,19 @@ def create_spectrogram(audio_path, title="Spectrogram"):
 
 
 def predict_anomaly(audio_file, machine_type):
-    """Predict anomaly in audio."""
+    """Predict anomaly in audio. Returns (result_html, input_spec_path, ref_spec_path)."""
     if audio_file is None:
         return "Please upload an audio file.", None, None
-    
-    # Gradio Audio can return dict with "path" key in some versions
     if isinstance(audio_file, dict):
         audio_file = audio_file.get("path") or audio_file.get("name")
     if not audio_file:
         return "Please upload an audio file.", None, None
-    
+
     import random
     random.seed(hash(str(audio_file)) % 1000)
     is_anomaly = random.random() > 0.5
     confidence = random.uniform(0.75, 0.95)
-    
+
     if is_anomaly:
         label = "ANOMALY ✗"
         color = "#ff4444"
@@ -79,77 +75,54 @@ def predict_anomaly(audio_file, machine_type):
         label = "NORMAL ✓"
         color = "#44ff44"
         result = f'<div style="text-align: center; padding: 20px;"><h2 style="color: {color}; font-size: 48px;">{label}</h2><p>Confidence: {confidence:.1%}</p></div>'
-    
+
     input_spec = create_spectrogram(audio_file, f"Input - {machine_type}")
     ref_spec = None
-    
     examples_dir = Path(__file__).parent / "examples"
     if examples_dir.exists():
         ref_files = list(examples_dir.glob(f"{machine_type}_*.wav"))
         if ref_files:
             ref_spec = create_spectrogram(str(ref_files[0]), f"Reference - {machine_type}")
-    
-    return result, input_spec, ref_spec
 
+    return result, input_spec, ref_spec
 
-theme = gr.themes.Monochrome(primary_hue="red", secondary_hue="gray")
 
-with gr.Blocks(theme=theme, title="AnomalyMachine-50K Demo") as app:
-    gr.Markdown("""
+# Use Blocks only; no api_name to avoid broken API schema path that causes "No API found"
+with gr.Blocks(title="AnomalyMachine-50K Demo", theme=gr.themes.Monochrome(primary_hue="red", secondary_hue="gray")) as app:
+    gr.Markdown(f"""
     <div style="text-align: center; padding: 20px;">
-        <h1>🏭 AnomalyMachine-50K</h1>
+        <h1>AnomalyMachine-50K</h1>
         <p>Synthetic Industrial Machine Sound Anomaly Detection Dataset</p>
-        <p><a href="https://huggingface.co/datasets/mandipgoswami/AnomalyMachine-50K" target="_blank">View Dataset →</a></p>
+        <p><a href="{DATASET_URL}" target="_blank">View Dataset</a></p>
     </div>
     """)
-    
     with gr.Tabs():
-        with gr.Tab("🔍 Detect Anomaly"):
+        with gr.Tab("Detect Anomaly"):
             with gr.Row():
                 with gr.Column():
-                    audio_input = gr.Audio(label="Upload Audio", type="filepath", sources=["upload", "microphone"])
-                    machine_dropdown = gr.Dropdown(choices=DATASET_INFO["machines"], label="Machine Type", value=DATASET_INFO["machines"][0])
-                    predict_btn = gr.Button("Detect Anomaly", variant="primary")
-                
+                    audio_in = gr.Audio(label="Upload Audio", type="filepath", sources=["upload", "microphone"])
+                    machine_in = gr.Dropdown(choices=DATASET_INFO["machines"], label="Machine Type", value=DATASET_INFO["machines"][0])
+                    btn = gr.Button("Detect Anomaly", variant="primary")
                 with gr.Column():
-                    result_html = gr.HTML()
-            
+                    result_out = gr.HTML()
             with gr.Row():
-                input_spec = gr.Image(label="Input Spectrogram")
-                ref_spec = gr.Image(label="Reference Spectrogram")
-            
-            predict_btn.click(
-                fn=predict_anomaly,
-                inputs=[audio_input, machine_dropdown],
-                outputs=[result_html, input_spec, ref_spec],
-                api_name="predict",
-            )
-        
-        with gr.Tab("📊 Explore Dataset"):
+                spec_out = gr.Image(label="Input Spectrogram")
+                ref_out = gr.Image(label="Reference Spectrogram")
+            btn.click(fn=predict_anomaly, inputs=[audio_in, machine_in], outputs=[result_out, spec_out, ref_out])
+
+        with gr.Tab("Explore Dataset"):
             gr.Markdown(f"""
-            <div style="padding: 20px;">
-                <h3>Dataset Statistics</h3>
-                <ul>
-                    <li>Total Clips: {DATASET_INFO['total_clips']:,}</li>
-                    <li>Total Duration: {DATASET_INFO['total_hours']} hours</li>
-                    <li>Machine Types: {len(DATASET_INFO['machines'])}</li>
-                    <li>Normal: {DATASET_INFO['normal_ratio']:.0%} | Anomalous: {DATASET_INFO['anomalous_ratio']:.0%}</li>
-                </ul>
-                <p style="text-align: center;">
-                    <a href="https://huggingface.co/datasets/mandipgoswami/AnomalyMachine-50K" target="_blank">
-                        <button style="padding: 10px 20px; font-size: 16px;">📥 Download Dataset</button>
-                    </a>
-                </p>
-            </div>
+            **Dataset Statistics**
+            - Total Clips: {DATASET_INFO['total_clips']:,}
+            - Total Duration: {DATASET_INFO['total_hours']} hours
+            - Machine Types: {len(DATASET_INFO['machines'])}
+            - Normal: {DATASET_INFO['normal_ratio']:.0%} | Anomalous: {DATASET_INFO['anomalous_ratio']:.0%}
+
+            [Download Dataset]({DATASET_URL})
             """)
-    
-    gr.Markdown("""
-    <div style="text-align: center; padding: 20px; border-top: 1px solid #333; margin-top: 40px;">
-        <p style="color: #888; font-size: 14px;">
-            License: CC-BY 4.0 | 
-            <a href="https://huggingface.co/datasets/mandipgoswami/AnomalyMachine-50K" target="_blank">Dataset</a> | 
-            <a href="https://github.com/mandip42/anomaly-machine-50k" target="_blank">GitHub</a>
-        </p>
+    gr.Markdown(f"""
+    <div style="text-align: center; padding: 20px; border-top: 1px solid #333;">
+        <p style="color: #888;">License: CC-BY 4.0 | <a href="{DATASET_URL}" target="_blank">Dataset</a> | <a href="https://github.com/mandip42/anomaly-machine-50k" target="_blank">GitHub</a></p>
     </div>
     """)