Update app.py

app.py

@@ -2,50 +2,139 @@ import gradio as gr
 import numpy as np
 import time
 
-def process_image(img):
-    """Placeholder function - replace with your backend integration"""
-    if img is None:
-        return "No image provided", "", ""
-    time.sleep(1)  # Simulate processing
-    return "Processing Complete", "Real Face", "Confidence: 95%"
+def process_video_frame(frame):
+    """
+    Placeholder function for processing each video frame
+    Replace this with your actual face spoofing detection logic
+    """
+    if frame is None:
+        return "No frame received", "", ""
+    
+    # Simulate processing time (remove this in production)
+    time.sleep(0.1)
+    
+    # Placeholder result - replace with your actual detection
+    is_real = np.random.choice([True, False], p=[0.7, 0.3])
+    confidence = np.random.uniform(0.8, 1.0)
+    
+    result = "Real Face" if is_real else "Spoof Detected"
+    status = "Processing live feed..."
+    conf_text = f"{confidence:.2%}"
+    
+    return status, result, conf_text
 
 with gr.Blocks() as demo:
-    gr.Markdown("# Face Spoofing Detection System")
-    
-    with gr.Tabs():
-        with gr.Tab("Webcam Detection"):
-            webcam = gr.Image(label="Webcam Feed")
-            webcam_status = gr.Textbox(label="Status", value="Ready")
-            webcam_result = gr.Textbox(label="Detection Result")
-            webcam_conf = gr.Textbox(label="Confidence Score")
-            webcam_button = gr.Button("Analyze")
-            
-            webcam_button.click(
-                fn=process_image,
-                inputs=webcam,
-                outputs=[webcam_status, webcam_result, webcam_conf]
-            )
-
-        with gr.Tab("Image Upload"):
-            image_input = gr.Image(label="Upload Image")
-            image_status = gr.Textbox(label="Status", value="Ready")
-            image_result = gr.Textbox(label="Detection Result")
-            image_conf = gr.Textbox(label="Confidence Score")
-            image_button = gr.Button("Analyze")
+    gr.Markdown("# Real-Time Face Spoofing Detection")
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            # Main video feed
+            video_feed = gr.Image(label="Live Camera Feed")
+        
+        with gr.Column(scale=1):
+            # Status and results
+            status_text = gr.Textbox(label="Status", value="Waiting for camera...")
+            result_text = gr.Textbox(label="Detection Result")
+            confidence_text = gr.Textbox(label="Confidence Score")
             
-            image_button.click(
-                fn=process_image,
-                inputs=image_input,
-                outputs=[image_status, image_result, image_conf]
-            )
-
+            # Control buttons
+            start_button = gr.Button("Start Detection", variant="primary")
+            stop_button = gr.Button("Stop", variant="secondary")
+    
     gr.Markdown("""
     ### Instructions:
-    1. Choose either Webcam or Image Upload tab
-    2. For webcam: Allow camera access and take a photo
-    3. For images: Upload an image from your device
-    4. Click Analyze to process the image
+    1. Allow camera access when prompted
+    2. Click 'Start Detection' to begin real-time analysis
+    3. Click 'Stop' to pause the detection
+    
+    ### Note:
+    - Keep your face centered and well-lit
+    - Maintain a stable position for better results
+    - Detection results update in real-time
     """)
+    
+    # Update interval in seconds (adjust based on your needs)
+    PROCESS_INTERVAL = 0.1
+    
+    # Handle start/stop functionality
+    def start_detection():
+        return gr.update(value="Processing live feed...")
+    
+    def stop_detection():
+        return gr.update(value="Detection stopped")
+    
+    # Event handlers
+    start_button.click(
+        fn=start_detection,
+        outputs=status_text,
+        every=PROCESS_INTERVAL
+    )
+    
+    # Process frames when detection is running
+    video_feed.change(
+        fn=process_video_frame,
+        inputs=[video_feed],
+        outputs=[status_text, result_text, confidence_text],
+        every=PROCESS_INTERVAL
+    )
+    
+    stop_button.click(
+        fn=stop_detection,
+        outputs=status_text,
+        cancels=[start_button, video_feed]
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()
+
+# import gradio as gr
+# import numpy as np
+# import time
+
+# def process_image(img):
+#     """Placeholder function - replace with your backend integration"""
+#     if img is None:
+#         return "No image provided", "", ""
+#     time.sleep(1)  # Simulate processing
+#     return "Processing Complete", "Real Face", "Confidence: 95%"
+
+# with gr.Blocks() as demo:
+#     gr.Markdown("# Face Spoofing Detection System")
+    
+#     with gr.Tabs():
+#         with gr.Tab("Webcam Detection"):
+#             webcam = gr.Image(label="Webcam Feed")
+#             webcam_status = gr.Textbox(label="Status", value="Ready")
+#             webcam_result = gr.Textbox(label="Detection Result")
+#             webcam_conf = gr.Textbox(label="Confidence Score")
+#             webcam_button = gr.Button("Analyze")
+            
+#             webcam_button.click(
+#                 fn=process_image,
+#                 inputs=webcam,
+#                 outputs=[webcam_status, webcam_result, webcam_conf]
+#             )
+
+#         with gr.Tab("Image Upload"):
+#             image_input = gr.Image(label="Upload Image")
+#             image_status = gr.Textbox(label="Status", value="Ready")
+#             image_result = gr.Textbox(label="Detection Result")
+#             image_conf = gr.Textbox(label="Confidence Score")
+#             image_button = gr.Button("Analyze")
+            
+#             image_button.click(
+#                 fn=process_image,
+#                 inputs=image_input,
+#                 outputs=[image_status, image_result, image_conf]
+#             )
+
+#     gr.Markdown("""
+#     ### Instructions:
+#     1. Choose either Webcam or Image Upload tab
+#     2. For webcam: Allow camera access and take a photo
+#     3. For images: Upload an image from your device
+#     4. Click Analyze to process the image
+#     """)
+
+# if __name__ == "__main__":
+#     demo.launch()