import gradio as gr
from ultralytics import YOLO
from ultralytics.solutions import ai_gym
import cv2

def process(video_path):
    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))

    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="pullup",
                        kpts_to_check=[6, 8, 10])

    frame_count = 0
    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            print("Video processing has been successfully completed.")
            break
        frame_count += 1
        results = model.track(im0, verbose=False)  # Tracking recommended
        im0 = gym_object.start_counting(im0, results, frame_count)
        video_writer.write(im0)

    cap.release()
    video_writer.release()
    cv2.destroyAllWindows()

    return "output_video.mp4"

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']

# 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
                    )

# Launch the demo!
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)