Update app.py

app.py

@@ -1,184 +1,31 @@
 import gradio as gr
-from gradio_client import Client
 import os
-import requests
-from datetime import datetime
-import time
 
-# Configuration
 BACKEND_URL = os.environ.get("BACKEND_URL", "").strip()
-CHECK_INTERVAL = 60  # Check backend status every 60 seconds
 
-class BackendConnection:
-    def __init__(self):
-        self.client = None
-        self.last_check = 0
-        self.is_connected = False
-        self.error_message = ""
-    
-    def check_backend(self):
-        """Check if backend is accessible"""
-        current_time = time.time()
-        
-        # Only check every CHECK_INTERVAL seconds
-        if current_time - self.last_check < CHECK_INTERVAL and self.client:
-            return self.is_connected
-        
-        self.last_check = current_time
-        
-        if not BACKEND_URL:
-            self.error_message = "Backend URL not configured. Please set the BACKEND_URL secret."
-            self.is_connected = False
-            return False
-        
-        try:
-            # Try to connect to the backend
-            response = requests.get(f"{BACKEND_URL}/health", timeout=5)
-            if response.status_code == 200:
-                # Try to create client
-                self.client = Client(BACKEND_URL)
-                self.is_connected = True
-                self.error_message = ""
-                return True
-        except Exception as e:
-            self.error_message = f"Cannot connect to GPU server: {str(e)}"
-            self.is_connected = False
-            self.client = None
-        
-        return False
-
-# Global connection manager
-backend = BackendConnection()
-
-def process_with_backend(file_obj, webcam_img, model_type, conf_thresh, max_dets, task_type):
-    """Forward request to backend"""
-    if not backend.check_backend():
-        return (
-            gr.update(visible=False),  # raw_img_file
-            gr.update(visible=False),  # raw_vid_file
-            gr.update(visible=False),  # raw_img_webcam
-            gr.update(value=None, visible=True),  # processed_img
-            gr.update(visible=False),  # processed_vid
-            f"❌ GPU Server Offline: {backend.error_message}"
-        )
-    
-    try:
-        # Forward to backend
-        result = backend.client.predict(
-            file_obj,
-            webcam_img,
-            model_type,
-            conf_thresh,
-            max_dets,
-            task_type,
-            api_name="/predict"
-        )
-        return result
-    except Exception as e:
-        return (
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(visible=False),
-            gr.update(value=None, visible=True),
-            gr.update(visible=False),
-            f"❌ Processing Error: {str(e)}"
-        )
-
-# Create the interface
 with gr.Blocks(theme=gr.themes.Soft(), title="PrimateFace Demo") as demo:
-    # Header with status
-    with gr.Row():
-        gr.Markdown("# 🐵 PrimateFace Detection, Pose Estimation, and Gaze Demo")
-        status_indicator = gr.Markdown("🟡 Checking GPU server status...", elem_id="status")
-    
-    gr.Markdown("""
-    This demo showcases state-of-the-art primate face analysis including detection, 
-    68-point facial landmarks, and gaze estimation across multiple primate species.
-    """)
-    
-    # Check initial status
-    if backend.check_backend():
-        status_indicator.value = "🟢 GPU Server: Online"
-        
-        # Load the actual interface from backend
-        try:
-            # Get the interface configuration from backend
-            with gr.Row():
-                with gr.Column(scale=1):
-                    # Input section
-                    with gr.Tabs():
-                        with gr.TabItem("Upload File"):
-                            input_file = gr.File(label="Upload Image or Video", file_types=["image", "video"])
-                            display_raw_image_file = gr.Image(label="Preview", visible=False)
-                            display_raw_video_file = gr.Video(label="Preview", visible=False)
-                        
-                        with gr.TabItem("Webcam"):
-                            gr.Markdown("Click on the feed or press Enter to capture")
-                            input_webcam = gr.Image(sources=["webcam"], type="pil")
-                            display_raw_image_webcam = gr.Image(label="Captured", visible=False)
-                    
-                    clear_button = gr.Button("Clear All")
-                
-                with gr.Column(scale=1):
-                    # Output section
-                    gr.Markdown("### Processed Output")
-                    display_processed_image = gr.Image(label="Result", visible=False)
-                    display_processed_video = gr.Video(label="Result", visible=False)
-                    error_message = gr.Markdown(visible=False)
-            
-            # Controls
-            submit_button = gr.Button("🚀 Detect Faces", variant="primary", size="lg")
-            
-            with gr.Accordion("Advanced Settings", open=False):
-                model_choice = gr.Radio(
-                    choices=["MMDetection"], 
-                    value="MMDetection",
-                    label="Model",
-                    visible=False
-                )
-                task_type = gr.Dropdown(
-                    choices=["Face Detection", "Face Pose Estimation", "Gaze Estimation [experimental]"],
-                    value="Face Detection",
-                    label="Task"
-                )
-                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")
-            
-            # Wire up the interface
-            submit_button.click(
-                process_with_backend,
-                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, error_message]
-            )
-            
-        except Exception as e:
-            gr.Markdown(f"❌ Failed to load interface: {str(e)}")
-    
+    if BACKEND_URL:
+        gr.HTML(f'''
+        <div style="padding: 20px;">
+            <h1 style="text-align: center;">🐵 PrimateFace Detection, Pose & Gaze Demo</h1>
+            <p style="text-align: center; color: green;">✅ GPU Server Connected</p>
+        </div>
+        <iframe 
+            src="{BACKEND_URL}" 
+            width="100%" 
+            height="1000px" 
+            frameborder="0"
+            style="border: 1px solid #ccc; border-radius: 8px;">
+        </iframe>
+        ''')
     else:
-        status_indicator.value = "🔴 GPU Server: Offline"
-        gr.Markdown(f"""
-        ### 🔴 GPU Server is Currently Offline
-        
-        {backend.error_message}
+        gr.Markdown("""
+        # 🐵 PrimateFace Demo
         
-        The demo requires a GPU server for processing. Please check back later.
+        ### 🔴 Configuration Error
         
-        **Note:** The server may be temporarily unavailable for maintenance.
+        The BACKEND_URL environment variable is not set. 
+        Please configure it in the Space settings under "Repository secrets".
         """)
-    
-    # Add info section regardless of status
-    gr.Markdown("""
-    ---
-    ### Technical Details
-    - **Detection**: MMDetection (Cascade R-CNN R101-FPN)
-    - **Pose**: MMPose (HRNet-W18) with 68 facial landmarks  
-    - **Gaze**: Gazelle (DINOv2-based) experimental gaze estimation
-    - **GPU**: NVIDIA GPU with CUDA support
-    
-    ### About
-    Created by Felipe Parodi | [Project GitHub](https://github.com/KordingLab/PrimateFace) | [Personal](https://github.com/felipe-parodi)
-    """)
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()