Update app.py

app.py

@@ -4,43 +4,74 @@ import os
 
 BACKEND_URL = os.environ.get("BACKEND_URL", "").strip()
 
-# Create persistent client
+# Create persistent client and check available endpoints
 try:
     client = Client(BACKEND_URL, headers={"ngrok-skip-browser-warning": "true"})
     backend_available = True
-except:
+    
+    # Debug: Check what endpoints are available
+    print("Available endpoints:")
+    for endpoint in client.endpoints:
+        print(f"  - {endpoint}")
+        
+except Exception as e:
     client = None
     backend_available = False
+    print(f"Backend not available: {e}")
 
-def forward_to_backend(fn_name, *args):
-    """Generic function to forward any call to backend"""
+def process_media(file_obj, webcam_img, model_type, conf_thresh, max_dets, task_type):
+    """Main processing function"""
     if not client:
-        return [gr.update() for _ in range(7)]
+        return [None] * 5
+    
     try:
-        return client.predict(*args, api_name=fn_name)
+        # The main endpoint is likely at index 0 or 1
+        # Try to find the process_media endpoint
+        result = client.predict(
+            file_obj,
+            webcam_img,
+            model_type,
+            conf_thresh,
+            max_dets,
+            task_type,
+            fn_index=1  # Usually the main function is at index 1
+        )
+        return result
     except Exception as e:
-        print(f"Error calling {fn_name}: {e}")
-        return [gr.update() for _ in range(7)]
-
-# Wrapper functions
-def handle_file_upload_preview(file_obj):
-    return forward_to_backend("/handle_file_upload_preview", file_obj)
-
-def handle_webcam_capture(snapshot):
-    return forward_to_backend("/handle_webcam_capture", snapshot)
-
-def process_media(file_obj, webcam_img, model_type, conf_thresh, max_dets, task_type):
-    return forward_to_backend("/process_media", file_obj, webcam_img, model_type, conf_thresh, max_dets, task_type)
+        print(f"Error in process_media: {e}")
+        # Return empty updates for all outputs
+        return [
+            gr.update(visible=False),  # display_raw_image_file
+            gr.update(visible=False),  # display_raw_video_file
+            gr.update(visible=False),  # display_raw_image_webcam
+            gr.update(visible=False),  # display_processed_image
+            gr.update(visible=False)   # display_processed_video
+        ]
 
-def clear_all_media_and_outputs():
-    return forward_to_backend("/clear_all_media_and_outputs")
+def handle_file_preview(file_obj):
+    """Handle file upload and show preview"""
+    if file_obj is None:
+        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
+    
+    # Check if it's an image or video
+    if file_obj.name.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.webp')):
+        return gr.update(value=file_obj.name, visible=True), gr.update(visible=False), gr.update(visible=False)
+    elif file_obj.name.lower().endswith(('.mp4', '.avi', '.mov', '.mkv', '.webm')):
+        return gr.update(visible=False), gr.update(value=file_obj.name, visible=True), gr.update(visible=False)
+    else:
+        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
 
-def handle_example_select(evt: gr.SelectData):
-    """Handle example selection locally and update input_file"""
-    # Extract the image path from the dataset
-    if isinstance(evt.value, dict) and 'image' in evt.value:
-        return evt.value['image']
-    return None
+def clear_all():
+    """Clear all inputs and outputs"""
+    return [
+        gr.update(value=None),      # input_file
+        gr.update(value=None),      # input_webcam
+        gr.update(visible=False),   # display_raw_image_file
+        gr.update(visible=False),   # display_raw_video_file
+        gr.update(visible=False),   # display_raw_image_webcam
+        gr.update(visible=False),   # display_processed_image
+        gr.update(visible=False)    # display_processed_video
+    ]
 
 # Build the interface
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
@@ -49,78 +80,93 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     if not backend_available:
         gr.Markdown("### 🔴 GPU Server Offline - Please check back later")
     else:
-        gr.Markdown("Upload an image/video or use your webcam. For webcam, press 'Enter' to take a snapshot.")
-        gr.Markdown("Click 'Detect Faces' for results.")
+        gr.Markdown("Upload an image/video or use your webcam. Click 'Detect Faces' for results.")
         
         with gr.Row():
             with gr.Column(scale=1):
                 with gr.Tabs():
                     with gr.TabItem("Upload File"):
-                        input_file = gr.File(label="Upload Image or Video Here", file_types=["image", ".mp4", ".avi", ".mov", ".mkv", ".webm", ".gif"])
-                        display_raw_image_file = gr.Image(label="Raw Image Preview", type="pil", interactive=False, visible=False)
-                        display_raw_video_file = gr.Video(label="Raw Video Preview", interactive=False, visible=False)
+                        input_file = gr.File(label="Upload Image or Video Here", file_types=["image", "video"])
+                        display_raw_image_file = gr.Image(label="Raw Image Preview", type="filepath", visible=False)
+                        display_raw_video_file = gr.Video(label="Raw Video Preview", visible=False)
                     
                     with gr.TabItem("Webcam"):
-                        gr.Markdown("**Using the Webcam:** Click on feed or press Enter to capture")
-                        input_webcam = gr.Image(sources=["webcam"], type="pil", label="Live Webcam")
-                        display_raw_image_webcam = gr.Image(label="Captured Snapshot Preview", type="pil", interactive=False, visible=False)
+                        input_webcam = gr.Image(sources=["webcam"], type="pil", label="Click to capture")
+                        display_raw_image_webcam = gr.Image(label="Captured", type="pil", visible=False)
                 
                 clear_all_button = gr.Button("Clear All Inputs & Outputs")
             
             with gr.Column(scale=1):
                 gr.Markdown("### Processed Output")
-                display_processed_image = gr.Image(label="Processed Image", type="pil", interactive=False, visible=False)
-                display_processed_video = gr.Video(label="Processed Video", interactive=False, visible=False)
-        
-        # Example images - host them on HF Space
-        example_paths = [
-            "images/allocebus_000003.jpeg",
-            "images/tarsius_000120.jpeg", 
-            "images/nasalis_proboscis-monkey.png",
-            "images/macaca_000032.jpeg",
-            "images/mandrillus_000011.jpeg",
-            "images/pongo_000006.jpeg"
-        ]
+                display_processed_image = gr.Image(label="Processed Image", type="pil", visible=False)
+                display_processed_video = gr.Video(label="Processed Video", visible=False)
         
-        example_dataset = gr.Dataset(
-            components=["image"],
-            samples=[[path] for path in example_paths],
-            label="Example Images (Click to use)",
-            samples_per_page=6
+        # Examples
+        gr.Examples(
+            examples=[
+                ["images/allocebus_000003.jpeg"],
+                ["images/tarsius_000120.jpeg"],
+                ["images/nasalis_proboscis-monkey.png"],
+                ["images/macaca_000032.jpeg"],
+                ["images/mandrillus_000011.jpeg"],
+                ["images/pongo_000006.jpeg"]
+            ],
+            inputs=input_file,
+            label="Example Images"
         )
         
         submit_button = gr.Button("Detect Faces", variant="primary", scale=2)
         
         with gr.Column():
             gr.Markdown("### Detection Controls")
-            model_choice_radio = gr.Radio(choices=["MMDetection"], value="MMDetection", label="Inferencer", visible=False)
-            task_type_dropdown = gr.Dropdown(
-                choices=["Face Detection", "Face Pose Estimation", "Gaze Estimation [experimental]"],
+            model_choice = gr.Radio(["MMDetection"], value="MMDetection", visible=False)
+            task_type = gr.Dropdown(
+                ["Face Detection", "Face Pose Estimation", "Gaze Estimation [experimental]"],
                 value="Face Detection",
                 label="Select Task"
             )
-            conf_slider = gr.Slider(minimum=0.05, maximum=0.95, value=0.25, step=0.05, label="Confidence Threshold")
-            max_det_slider = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="Max Detections")
+            conf_threshold = gr.Slider(0.05, 0.95, 0.25, step=0.05, label="Confidence Threshold")
+            max_detections = gr.Slider(1, 10, 3, step=1, label="Max Detections")
         
-        # Define outputs
-        file_preview_outputs = [display_raw_image_file, display_raw_video_file, input_file, display_processed_image, display_processed_video]
-        webcam_outputs = [display_raw_image_webcam, input_webcam, display_processed_image, display_processed_video]
-        process_outputs = [display_raw_image_file, display_raw_video_file, display_raw_image_webcam, display_processed_image, display_processed_video]
-        clear_outputs = [input_file, input_webcam, display_raw_image_file, display_raw_video_file, display_raw_image_webcam, display_processed_image, display_processed_video]
-        
-        # Wire events
-        input_file.change(handle_file_upload_preview, inputs=[input_file], outputs=file_preview_outputs)
-        input_webcam.change(handle_webcam_capture, inputs=[input_webcam], outputs=webcam_outputs)
+        # Simple file preview
+        input_file.change(
+            handle_file_preview,
+            inputs=[input_file],
+            outputs=[display_raw_image_file, display_raw_video_file, input_file]
+        )
         
-        # Handle example selection
-        example_dataset.select(handle_example_select, outputs=[input_file])
+        # Webcam capture
+        input_webcam.change(
+            lambda img: gr.update(value=img, visible=True) if img else gr.update(visible=False),
+            inputs=[input_webcam],
+            outputs=[display_raw_image_webcam]
+        )
         
+        # Process button
         submit_button.click(
             process_media,
-            inputs=[input_file, display_raw_image_webcam, model_choice_radio, conf_slider, max_det_slider, task_type_dropdown],
-            outputs=process_outputs
+            inputs=[input_file, display_raw_image_webcam, model_choice, conf_threshold, max_detections, task_type],
+            outputs=[
+                display_raw_image_file,
+                display_raw_video_file,
+                display_raw_image_webcam,
+                display_processed_image,
+                display_processed_video
+            ]
         )
         
-        clear_all_button.click(clear_all_media_and_outputs, outputs=clear_outputs)
+        # Clear button
+        clear_all_button.click(
+            clear_all,
+            outputs=[
+                input_file,
+                input_webcam,
+                display_raw_image_file,
+                display_raw_video_file,
+                display_raw_image_webcam,
+                display_processed_image,
+                display_processed_video
+            ]
+        )
 
 demo.launch()