Fix Gradio interface - replaced BarPlot with Dataframe, added debugging, improved event handlers

app.py

@@ -6,14 +6,24 @@ from PIL import Image
 import io
 from handler import EndpointHandler
 
-handler = EndpointHandler()
+# Initialize handler
+print("Initializing MobileCLIP handler...")
+try:
+    handler = EndpointHandler()
+    print(f"Handler initialized successfully! Device: {handler.device}")
+except Exception as e:
+    print(f"Error initializing handler: {e}")
+    handler = None
 
 def classify_image(image, top_k=10):
     """
     Main classification function for public interface.
     """
+    if handler is None:
+        return "Error: Handler not initialized", None
+        
     if image is None:
-        return None, "Please upload an image"
+        return "Please upload an image", None
     
     try:
         # Convert PIL image to base64
@@ -34,25 +44,28 @@ def classify_image(image, top_k=10):
             # Create formatted output
             output_text = "**Top {} Classifications:**\n\n".format(len(result))
             
-            # Create a dictionary for the bar chart
-            chart_data = {}
+            # Create data for bar chart (list of tuples)
+            chart_data = []
             
             for i, item in enumerate(result, 1):
                 score_pct = item['score'] * 100
                 output_text += f"{i}. **{item['label']}** (ID: {item['id']}): {score_pct:.2f}%\n"
-                chart_data[item['label']] = item['score']
+                chart_data.append((item['label'], item['score']))
             
-            return chart_data, output_text
+            return output_text, chart_data
         else:
-            return None, f"Error: {result.get('error', 'Unknown error')}"
+            return f"Error: {result.get('error', 'Unknown error')}", None
             
     except Exception as e:
-        return None, f"Error: {str(e)}"
+        return f"Error: {str(e)}", None
 
 def upsert_labels_admin(admin_token, new_items_json):
     """
     Admin function to add new labels.
     """
+    if handler is None:
+        return "Error: Handler not initialized"
+        
     if not admin_token:
         return "Error: Admin token required"
     
@@ -84,6 +97,9 @@ def reload_labels_admin(admin_token, version):
     """
     Admin function to reload a specific label version.
     """
+    if handler is None:
+        return "Error: Handler not initialized"
+        
     if not admin_token:
         return "Error: Admin token required"
     
@@ -114,17 +130,20 @@ def get_current_stats():
     """
     Get current label statistics.
     """
+    if handler is None:
+        return "Handler not initialized"
+        
     try:
         num_labels = len(handler.class_ids) if hasattr(handler, 'class_ids') else 0
         version = getattr(handler, 'labels_version', 1)
         device = handler.device if hasattr(handler, 'device') else "unknown"
         
         stats = f"""
-        **Current Statistics:**
-        - Number of labels: {num_labels}
-        - Labels version: {version}
-        - Device: {device}
-        - Model: MobileCLIP-B
+**Current Statistics:**
+- Number of labels: {num_labels}
+- Labels version: {version}
+- Device: {device}
+- Model: MobileCLIP-B
         """
         
         if hasattr(handler, 'class_names') and len(handler.class_names) > 0:
@@ -137,6 +156,7 @@ def get_current_stats():
         return f"Error getting stats: {str(e)}"
 
 # Create Gradio interface
+print("Creating Gradio interface...")
 with gr.Blocks(title="MobileCLIP Image Classifier") as demo:
     gr.Markdown("""
     # 🖼️ MobileCLIP-B Zero-Shot Image Classifier
@@ -161,29 +181,26 @@ with gr.Blocks(title="MobileCLIP Image Classifier") as demo:
                 classify_btn = gr.Button("🚀 Classify Image", variant="primary")
             
             with gr.Column():
-                output_chart = gr.BarPlot(
-                    label="Classification Confidence",
-                    x_label="Label",
-                    y_label="Confidence",
-                    vertical=False,
-                    height=400
-                )
                 output_text = gr.Markdown(label="Classification Results")
+                # Simplified bar chart using Dataframe
+                output_chart = gr.Dataframe(
+                    headers=["Label", "Confidence"],
+                    label="Classification Scores",
+                    interactive=False
+                )
         
-        gr.Examples(
-            examples=[
-                ["https://raw.githubusercontent.com/gradio-app/gradio/main/demo/image_classifier/examples/cheetah.jpg"],
-                ["https://raw.githubusercontent.com/gradio-app/gradio/main/demo/image_classifier/examples/elephant.jpg"],
-                ["https://raw.githubusercontent.com/gradio-app/gradio/main/demo/image_classifier/examples/giraffe.jpg"]
-            ],
-            inputs=input_image,
-            label="Example Images"
+        # Event handler for classification
+        classify_btn.click(
+            fn=classify_image,
+            inputs=[input_image, top_k_slider],
+            outputs=[output_text, output_chart]
         )
         
-        classify_btn.click(
-            classify_image,
+        # Also trigger on image upload
+        input_image.change(
+            fn=classify_image,
             inputs=[input_image, top_k_slider],
-            outputs=[output_chart, output_text]
+            outputs=[output_text, output_chart]
         )
     
     with gr.Tab("🔧 Admin Panel"):
@@ -203,7 +220,8 @@ with gr.Blocks(title="MobileCLIP Image Classifier") as demo:
             stats_display = gr.Markdown(value=get_current_stats())
             refresh_stats_btn = gr.Button("🔄 Refresh Stats")
             refresh_stats_btn.click(
-                get_current_stats,
+                fn=get_current_stats,
+                inputs=[],
                 outputs=stats_display
             )
         
@@ -227,7 +245,7 @@ with gr.Blocks(title="MobileCLIP Image Classifier") as demo:
             upsert_output = gr.Markdown()
             
             upsert_btn.click(
-                upsert_labels_admin,
+                fn=upsert_labels_admin,
                 inputs=[admin_token_input, new_items_input],
                 outputs=upsert_output
             )
@@ -243,7 +261,7 @@ with gr.Blocks(title="MobileCLIP Image Classifier") as demo:
             reload_output = gr.Markdown()
             
             reload_btn.click(
-                reload_labels_admin,
+                fn=reload_labels_admin,
                 inputs=[admin_token_input, version_input],
                 outputs=reload_output
             )
@@ -258,13 +276,13 @@ with gr.Blocks(title="MobileCLIP Image Classifier") as demo:
         - 🚀 **Fast inference**: < 30ms on GPU
         - 🏷️ **Dynamic labels**: Add/update labels without redeployment
         - 🔄 **Version control**: Track and reload label versions
-        - 📊 **Visual results**: Bar charts and confidence scores
+        - 📊 **Visual results**: Classification scores and confidence
         
         ### Environment Variables (set in Space Settings):
         - `ADMIN_TOKEN`: Secret token for admin operations
-        - `HF_LABEL_REPO`: Hub repository for label storage (e.g., "username/labels")
+        - `HF_LABEL_REPO`: Hub repository for label storage
         - `HF_WRITE_TOKEN`: Token with write permissions to label repo
-        - `HF_READ_TOKEN`: Token with read permissions (optional, defaults to write token)
+        - `HF_READ_TOKEN`: Token with read permissions (optional)
         
         ### Model Details:
         - **Architecture**: MobileCLIP-B with MobileOne blocks
@@ -276,5 +294,8 @@ with gr.Blocks(title="MobileCLIP Image Classifier") as demo:
         Model weights are licensed under Apple Sample Code License (ASCL).
         """)
 
+print("Gradio interface created successfully!")
+
 if __name__ == "__main__":
+    print("Launching Gradio app...")
     demo.launch()