still debuggine, commented a couple of things out but still runnning slow and failing

app.py

@@ -65,26 +65,26 @@ def overlay_text_on_image(image, text_list, font=cv2.FONT_HERSHEY_SIMPLEX, font_
         cv2.putText(image, line, (image.shape[1] - text_width - margin, y), font, font_size, color, font_thickness, lineType=cv2.LINE_AA)
     return image
 
-def draw_cockpit(frame, top_pred,cnt):
-    # Bullet points:
-    high_danger_color = (255,0,0)
-    low_danger_color = yellowgreen = (154,205,50)
-
-    shark_sighted = 'Shark Detected: ' + str(top_pred['shark_sighted'])
-    human_sighted = 'Number of Humans: ' + str(top_pred['human_n'])
-    shark_size_estimate = 'Biggest shark size: ' + str(top_pred['biggest_shark_size'])
-    shark_weight_estimate = 'Biggest shark weight: ' + str(top_pred['biggest_shark_weight'])
-    danger_level = 'Danger Level: ' 
-    danger_level += 'High' if top_pred['dangerous_dist'] else 'Low'
-    danger_color = high_danger_color if top_pred['dangerous_dist'] else low_danger_color # 'orangered' for high danger , 'yellowgreen' for low danger'
+def draw_cockpit(frame, top_pred, cnt):
+#     # Bullet points:
+#     high_danger_color = (255,0,0)
+#     low_danger_color = yellowgreen = (154,205,50)
+
+#     shark_sighted = 'Shark Detected: ' + str(top_pred['shark_sighted'])
+#     human_sighted = 'Number of Humans: ' + str(top_pred['human_n'])
+#     shark_size_estimate = 'Biggest shark size: ' + str(top_pred['biggest_shark_size'])
+#     shark_weight_estimate = 'Biggest shark weight: ' + str(top_pred['biggest_shark_weight'])
+#     danger_level = 'Danger Level: ' 
+#     danger_level += 'High' if top_pred['dangerous_dist'] else 'Low'
+#     danger_color = high_danger_color if top_pred['dangerous_dist'] else low_danger_color # 'orangered' for high danger , 'yellowgreen' for low danger'
    
-   # Create a list of strings to plot
-    strings = [shark_sighted, human_sighted, shark_size_estimate, shark_weight_estimate, danger_level]
-    relative = max(frame.shape[0],frame.shape[1])
-    if top_pred['shark_sighted'] and (cnt % 2) == 0:
-        relative = max(frame.shape[0],frame.shape[1])
-        frame  = add_border(frame, color=danger_color, thickness=int(relative*0.025))
-    frame = overlay_text_on_image(frame, strings, font=cv2.FONT_HERSHEY_SIMPLEX, font_size=1.5, font_thickness=3, margin=int(relative*0.05), color=(255, 255, 255))
+#    # Create a list of strings to plot
+#     strings = [shark_sighted, human_sighted, shark_size_estimate, shark_weight_estimate, danger_level]
+#     relative = max(frame.shape[0],frame.shape[1])
+#     if top_pred['shark_sighted'] and (cnt % 2) == 0:
+#         relative = max(frame.shape[0],frame.shape[1])
+#         frame  = add_border(frame, color=danger_color, thickness=int(relative*0.025))
+#     frame = overlay_text_on_image(frame, strings, font=cv2.FONT_HERSHEY_SIMPLEX, font_size=1.5, font_thickness=3, margin=int(relative*0.05), color=(255, 255, 255))
     return frame
     
 def process_video(input_video, out_fps = 'auto', skip_frames = 7):
@@ -109,29 +109,41 @@ def process_video(input_video, out_fps = 'auto', skip_frames = 7):
     while iterating:
         print(cnt)
         if (cnt % skip_frames) == 0:
-            # flip frame vertically
+            # Get inference results and output masks
             display_frame, result = inference_frame_serial(frame)
             video.write(cv2.cvtColor(display_frame, cv2.COLOR_BGR2RGB))
+            
             #print(result)
+            print('start top_pred')
             top_pred = process_results_for_plot(predictions = result.numpy(),
                                                 classes = classes,
                                                 class_sizes = class_sizes_lower)
+            
             frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
             prediction_frame = cv2.cvtColor(display_frame, cv2.COLOR_BGR2RGB)
             
             frame = cv2.resize(frame, (int(width*4), int(height*4)))
             
-            if ((cnt*skip_frames) % 2==0): # and top_pred['shark_sighted']: 
-                prediction_frame = cv2.resize(prediction_frame, (int(width*4), int(height*4)))
-                frame = prediction_frame
+            # Only show the prediction frame every other frame
+            # print('first if')
+            # if ((cnt*skip_frames) % 2 == 0): # and top_pred['shark_sighted']: 
+            #     prediction_frame = cv2.resize(prediction_frame, (int(width*4), int(height*4)))
+            #     frame = prediction_frame
              
-            if  top_pred['shark_sighted']: 
-                frame = draw_cockpit(frame, top_pred,cnt*skip_frames)
+            # # Add cockpit to frame
+            # print('cockput if')
+            # if  top_pred['shark_sighted']: 
+            #     frame = draw_cockpit(frame, top_pred, cnt*skip_frames)
             
+            print('dashboard')
             pred_dashbord = prediction_dashboard(top_pred = top_pred)
             #print('sending frame')
+            print('finished dashboard')
             print(cnt)
-            yield prediction_frame,frame , None, pred_dashbord
+            # if cnt == 119:
+            #     import ipdb; ipdb.set_trace()
+            
+            yield prediction_frame, frame , None, pred_dashbord
         cnt += 1
         iterating, frame = cap.read()
         print('interating: ', iterating)
@@ -146,8 +158,8 @@ with gr.Blocks(theme=theme) as demo:
         output_video = gr.Video(label="Output Video")
         dashboard = gr.Image(label="Dashboard")
     
-    with gr.Row(height='200%',width='200%'):
-        original_frames = gr.Image(label="Original Frame", width='200%', height='200%').style( width=1000)
+    with gr.Row(height='100%',width='100%'):
+        original_frames = gr.Image(label="Original Frame", width='100%', height='100%')
         
     with gr.Row():
         paths = sorted(pathlib.Path('videos_example/').rglob('*.mp4'))