Delete app.py

app.py

@@ -1,132 +0,0 @@
-import gradio as gr
-from gradio_client import Client
-import os
-import tempfile
-
-BACKEND_URL = os.environ.get("BACKEND_URL", "").strip()
-
-try:
-    client = Client(BACKEND_URL, headers={"ngrok-skip-browser-warning": "true"})
-    backend_available = True
-except:
-    client = None
-    backend_available = False
-
-def process_media(file_obj, webcam_img, model_type, conf_thresh, max_dets, task_type):
-    """Process media - backend expects both file and webcam paths"""
-    if not client:
-        return [gr.update()] * 5
-    
-    try:
-        # Convert webcam PIL to file path if present
-        webcam_path = None
-        if webcam_img is not None:
-            with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as tmp:
-                webcam_img.save(tmp, 'PNG')
-                webcam_path = tmp.name
-        
-        # Backend expects both parameters - use None for missing one
-        result = client.predict(
-            uploaded_file_obj=file_obj if file_obj else None,
-            webcam_image_pil=webcam_path if webcam_path else None,
-            model_type_choice=model_type,
-            conf_threshold_ui=conf_thresh,
-            max_detections_ui=max_dets,
-            task_type=task_type,
-            api_name="/process_media"
-        )
-        
-        # Cleanup
-        if webcam_path and os.path.exists(webcam_path):
-            os.unlink(webcam_path)
-        
-        return result
-        
-    except Exception as e:
-        print(f"Process error: {e}")
-        # Return error message in processed image slot
-        return [
-            gr.update(),  # raw image file
-            gr.update(),  # raw video file  
-            gr.update(),  # raw image webcam
-            gr.update(value=None, visible=True),  # processed image - show error
-            gr.update()   # processed video
-        ]
-
-# Simplified interface without complex preview forwarding
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🐵 PrimateFace Detection, Pose & Gaze Demo")
-    
-    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")
-                        # Simple local preview
-                        preview_img = gr.Image(label="Preview", visible=False)
-                    
-                    with gr.TabItem("Webcam"):
-                        input_webcam = gr.Image(sources=["webcam"], type="pil")
-                
-                clear_btn = gr.Button("Clear All")
-            
-            with gr.Column():
-                gr.Markdown("### Results")
-                output_image = gr.Image(label="Processed", visible=False)
-                output_video = gr.Video(label="Processed", visible=False)
-        
-        # Examples
-        gr.Examples(
-            examples=[["images/" + f] for f in [
-                "allocebus_000003.jpeg",
-                "tarsius_000120.jpeg", 
-                "nasalis_proboscis-monkey.png",
-                "macaca_000032.jpeg",
-                "mandrillus_000011.jpeg",
-                "pongo_000006.jpeg"
-            ]],
-            inputs=input_file
-        )
-        
-        submit_btn = gr.Button("Detect Faces", variant="primary")
-        
-        # 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 local preview for uploaded files
-        def show_preview(file):
-            if file and file.name.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp')):
-                return gr.update(value=file, visible=True)
-            return gr.update(visible=False)
-        
-        input_file.change(show_preview, inputs=[input_file], outputs=[preview_img])
-        
-        # Main processing - only use last 3 outputs (skip raw previews)
-        def process_and_extract_outputs(*args):
-            result = process_media(*args)
-            # Return only processed outputs
-            return result[-2:]  # Just processed image and video
-        
-        submit_btn.click(
-            process_and_extract_outputs,
-            inputs=[input_file, input_webcam, model_choice, conf_threshold, max_detections, task_type],
-            outputs=[output_image, output_video]
-        )
-        
-        # Simple clear
-        clear_btn.click(
-            lambda: [gr.update(value=None), gr.update(value=None), gr.update(visible=False), 
-                    gr.update(visible=False), gr.update(visible=False)],
-            outputs=[input_file, input_webcam, preview_img, output_image, output_video]
-        )
-
-demo.launch()