Update main.py

main.py

@@ -1,6 +1,6 @@
 import gradio as gr
 from ultralytics import YOLO
-from ultralytics.solutions import ai_gym
+import ai_gym
 import cv2
 
 def process(video_path):
@@ -27,7 +27,7 @@ def process(video_path):
             print("Video processing has been successfully completed.")
             break
         frame_count += 1
-        results = model.track(im0, verbose=False)  # Tracking recommended
+        results = model.track(im0, verbose=True)  # Tracking recommended
         im0 = gym_object.start_counting(im0, results, frame_count)
         video_writer.write(im0)
 
@@ -41,15 +41,16 @@ title = "Push-up Counter"
 description = "This app counts the number of push-ups in a video."
 # inputs = gr.inputs.Video(label='Input Video')
 # outputs = gr.outputs.Video(label='Processed Video')
-# example_list = ['pullups.mp4']
+example_list = ['Examples/PULL-UPS.mp4','Examples/PUSH-UPS.mp4']
 
 # Create the Gradio demo
 demo = gr.Interface(fn=process,
                     inputs=gr.Video(label='Input Video'),  
                     outputs=gr.Video(label='Output Video')
-                    # title=title,
-                    # description=description,
-                    # examples=example_list
+                    title=title,
+                    description=description,
+                    examples=example_list,
+                    cache_examples=True,
                     )
 
 # Launch the demo!
@@ -60,66 +61,3 @@ demo.launch(show_api=True)
 
 
 
-
-
-# import gradio as gr
-# from ultralytics import YOLO
-# from ultralytics.solutions import ai_gym
-# import cv2
-# import tempfile
-# from PIL import Image
-# import subprocess
-
-# # Function to upgrade pip
-# def upgrade_pip():
-#     subprocess.run(['pip', 'install', '--upgrade', 'pip'])
-
-# # Function to process video
-# def process(video_path):
-#     upgrade_pip()  # Upgrade pip before executing the main function
-#     model = YOLO("yolov8n-pose.pt")
-#     cap = cv2.VideoCapture(video_path)
-#     assert cap.isOpened(), "Error reading video file"
-#     w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
-
-#     temp_dir = tempfile.mkdtemp()  # Create a temporary directory to store processed frames
-#     video_writer = cv2.VideoWriter("output_video.mp4",
-#                                    cv2.VideoWriter_fourcc(*'mp4v'),
-#                                    fps,
-#                                    (w, h))
-
-#     gym_object = ai_gym.AIGym()  # init AI GYM module
-#     gym_object.set_args(line_thickness=2,
-#                         view_img=False,  # Set view_img to False to prevent displaying the video in real-time
-#                         pose_type="pushup",
-#                         kpts_to_check=[6, 8, 10])
-
-#     frame_count = 0
-#     while cap.isOpened():
-#         success, im0 = cap.read()
-#         if not success:
-#             print("Video frame is empty or video processing has been successfully completed.")
-#             break
-#         frame_count += 1
-#         if frame_count % 5 == 0:  # Process every 5th frame
-#             results = model.track(im0, verbose=False)  # Tracking recommended
-#             im0 = gym_object.start_counting(im0, results, frame_count)
-#             # Save processed frame as an image in the temporary directory
-#             cv2.imwrite(f"{temp_dir}/{frame_count}.jpg", im0)
-
-#     # Use PIL to create the final video from the processed frames
-#     images = [Image.open(f"{temp_dir}/{i}.jpg") for i in range(1, frame_count + 1)]
-#     images[0].save("output_video.mp4", save_all=True, append_images=images[1:], duration=1000/fps, loop=0)
-
-#     cap.release()
-#     cv2.destroyAllWindows()
-
-#     return "output_video.mp4"
-
-# # Create the Gradio demo
-# demo = gr.Interface(fn=process,
-#                     inputs=gr.Video(label='Input Video'),  
-#                     outputs=gr.Video(label='Processed Video'))
-
-# # Launch the demo!
-# demo.launch(show_api=False)