Update app.py

app.py

@@ -1,147 +1,73 @@
 import gradio as gr
 from gradio_client import Client
 import os
-import tempfile
 
 BACKEND_URL = os.environ.get("BACKEND_URL", "").strip()
 
-# Create persistent client
+# Debug the backend API
 try:
     client = Client(BACKEND_URL, headers={"ngrok-skip-browser-warning": "true"})
     backend_available = True
-    print(f"Connected to backend at {BACKEND_URL}")
+    
+    # Print detailed API info
+    print("\n=== BACKEND API INFO ===")
+    api_info = client.view_api(all_endpoints=True)
+    print(api_info)
+    print("========================\n")
+    
 except Exception as e:
     client = None
     backend_available = False
-    print(f"Backend not available: {e}")
+    print(f"Backend error: {e}")
 
-def process_media(file_obj, webcam_img, model_type, conf_thresh, max_dets, task_type):
-    """Main processing function - expects 5 outputs"""
-    if not client:
-        return [gr.update()] * 5
-    
+def test_process():
+    """Test function to debug"""
     try:
-        # Handle webcam image - need to pass as file path
-        webcam_file = None
-        if webcam_img is not None:
-            with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as tmp:
-                webcam_img.save(tmp, 'PNG')
-                webcam_file = tmp.name
-        
-        # Call backend
+        # Try calling with minimal inputs
         result = client.predict(
-            file_obj,
-            webcam_file,
-            model_type,
-            conf_thresh,
-            max_dets,
-            task_type,
+            None,  # file
+            None,  # webcam
+            "MMDetection",  # model
+            0.25,  # conf
+            3,     # max det
+            "Face Detection",  # task
             fn_index=3
         )
-        
-        # Clean up
-        if webcam_file and os.path.exists(webcam_file):
-            os.unlink(webcam_file)
-        
-        # Backend returns 7 values but we only need the last 5
-        # Skip the first 2 (input_file and input_webcam updates)
-        if len(result) == 7:
-            return result[2:]  # Return only the display components
-        else:
-            return result[:5]  # Safety fallback
-            
+        print(f"Result: {result}")
+        return result
     except Exception as e:
-        print(f"Error in process_media: {e}")
-        return [gr.update()] * 5
+        print(f"Test error: {e}")
+        return str(e)
 
-# Build simplified interface
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🐵 PrimateFace Detection, Pose Estimation, and Gaze Demo")
+# Minimal test interface
+with gr.Blocks() as demo:
+    gr.Markdown("# PrimateFace Demo - Debug Mode")
     
-    if not backend_available:
-        gr.Markdown("### 🔴 GPU Server Offline")
-    else:
-        with gr.Row():
-            with gr.Column():
-                with gr.Tabs():
-                    with gr.TabItem("Upload"):
-                        input_file = gr.File(label="Upload Image/Video", file_types=["image", "video"])
-                        display_raw_image_file = gr.Image(visible=False)
-                        display_raw_video_file = gr.Video(visible=False)
-                    
-                    with gr.TabItem("Webcam"):
-                        input_webcam = gr.Image(sources=["webcam"], type="pil")
-                        display_raw_image_webcam = gr.Image(visible=False)
-                
-                clear_button = gr.Button("Clear All")
-            
-            with gr.Column():
-                gr.Markdown("### Output")
-                display_processed_image = gr.Image(visible=False)
-                display_processed_video = gr.Video(visible=False)
-        
-        # 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
-        )
+    if backend_available:
+        # Show API info
+        with gr.Accordion("API Info", open=True):
+            gr.Code(str(api_info), language="text")
         
-        submit_button = gr.Button("Detect Faces", variant="primary")
+        # Simple test
+        test_btn = gr.Button("Test Backend Connection")
+        output = gr.Textbox(label="Response")
         
-        # Controls
-        model_choice = gr.Radio(["MMDetection"], value="MMDetection", visible=False)
-        task_type = gr.Dropdown(
-            ["Face Detection", "Face Pose Estimation", "Gaze Estimation [experimental]"],
-            value="Face Detection"
-        )
-        conf_threshold = gr.Slider(0.05, 0.95, 0.25, step=0.05, label="Confidence")
-        max_detections = gr.Slider(1, 10, 3, step=1, label="Max Detections")
-        
-        # Simple preview handlers
-        input_file.change(
-            lambda f: (gr.update(value=f, visible=bool(f)), gr.update(visible=False)) if f and f.name.endswith(('.jpg','.jpeg','.png')) else (gr.update(visible=False), gr.update(value=f, visible=bool(f))),
-            inputs=[input_file],
-            outputs=[display_raw_image_file, display_raw_video_file]
-        )
-        
-        input_webcam.change(
-            lambda img: gr.update(value=img, visible=bool(img)),
-            inputs=[input_webcam],
-            outputs=[display_raw_image_webcam]
-        )
+        test_btn.click(test_process, outputs=output)
         
-        # Main processing
-        submit_button.click(
-            process_media,
-            inputs=[input_file, input_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
-        clear_button.click(
-            lambda: [gr.update(value=None)] * 7,
-            outputs=[
-                input_file,
-                input_webcam,
-                display_raw_image_file,
-                display_raw_video_file,
-                display_raw_image_webcam,
-                display_processed_image,
-                display_processed_video
-            ]
-        )
+        # Try using the client's automatic interface loading
+        gr.Markdown("---")
+        gr.Markdown("### Direct Backend Interface:")
+        try:
+            # Load the backend interface directly
+            backend_interface = gr.load(
+                name=BACKEND_URL,
+                src="spaces",
+                hf_token=None
+            )
+            backend_interface.render()
+        except Exception as e:
+            gr.Markdown(f"Could not load interface: {e}")
+    else:
+        gr.Markdown("### 🔴 Backend Offline")
 
 demo.launch()