Update app.py

app.py

@@ -46,6 +46,24 @@ class LiveFeedState:
         self.logs = deque(maxlen=10)              # Last 10 log entries
         self.frame_count = 0                      # Frame counter
 
+def simple_test_generator():
+    import numpy as np
+    import time
+    img = np.zeros((480, 640, 3), dtype=np.uint8)  # black image
+    count = 0
+    while True:
+        count += 1
+        yield (
+            img,
+            f"Test metrics count: {count}",
+            f"Test logs count: {count}",
+            None,
+            "Test anomaly types",
+            [],
+            "Test timestamp"
+        )
+        time.sleep(0.5)        
+
 def live_feed_generator(video_type, confidence_threshold=0.9):
     """Generator for live feed with real-time detection."""
     state = LiveFeedState()
@@ -59,18 +77,26 @@ def live_feed_generator(video_type, confidence_threshold=0.9):
     cap = cv2.VideoCapture(video_path)
     fps = cap.get(cv2.CAP_PROP_FPS)
     frame_interval = 1 / fps  # Time between frames for real-time simulation
+    print(f"[DEBUG] Starting live feed for {video_type} at path: {video_path}")
 
     while cap.isOpened():
         ret, frame = cap.read()
+        print(f"[DEBUG] Frame read success: {ret}, frame count: {state.frame_count}")
         if not ret or frame is None or frame.size == 0:
-            print(f"Frame read failed or empty at frame count {state.frame_count}")
+            print("[DEBUG] End of video reached or cannot read frame. Looping video.")
             cap.set(cv2.CAP_PROP_POS_FRAMES, 0)  # Loop the video
             continue
         else:
             print(f"Frame shape: {frame.shape} at frame count {state.frame_count}")
 
         state.frame_count += 1
+        # Convert frame to PIL Image
         frame_pil = video_service.frame_to_pil(frame)
+        if frame_pil is None:
+            print("[DEBUG] Failed to convert frame to PIL image. Skipping frame.")
+            continue
+
+        print(f"[DEBUG] Processing frame {state.frame_count}")
 
         # Perform detection based on video type
         if video_type == "Thermal Feed":
@@ -86,10 +112,12 @@ def live_feed_generator(video_type, confidence_threshold=0.9):
             print(f"Detections for Frame {state.frame_count}: {detections}")
             alert_type = "General"
 
+        print(f"[DEBUG] Detections: {detections}")    
+
         # Draw detections on frame
         annotated_frame = video_service.draw_detections(frame, detections)
         annotated_frame_rgb = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)
-        print(f"Annotated frame shape (RGB): {annotated_frame_rgb.shape}, dtype: {annotated_frame_rgb.dtype}")
+        print(f"[DEBUG] Yielding frame {state.frame_count}, shape: {annotated_frame_rgb.shape}")
         cv2.imshow("Debug Frame", annotated_frame_rgb)
         cv2.waitKey(1)
         print(f"Annotated frame shape: {annotated_frame_rgb.shape}")
@@ -156,13 +184,14 @@ def live_feed_generator(video_type, confidence_threshold=0.9):
 
         # Yield updated UI components
         yield (
-            annotated_frame_rgb,
-            metrics_str,
-            "\n".join(state.logs),
-            trend_plot,
-            anomaly_types_str,
-            list(state.captured_events),
-            timestamp_str
+            gr.update(value=annotated_frame_rgb),
+            gr.update(value=metrics_str),
+            gr.update(value="\n".join(state.logs)),
+            gr.update(value=trend_plot),
+            gr.update(value=anomaly_types_str),
+            gr.update(value=list(state.captured_events)),
+            gr.update(value=timestamp_str)
+            
          )