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

# Initialize YOLO model
model = YOLO("yolov8n-pose.pt")

# Initialize AIGym object
gym_object = ai_gym.AIGym()

def count_workouts(input_video):
    # Temporary file to store output video
    output_path = tempfile.NamedTemporaryFile(suffix='.avi').name

    # Open input video
    cap = cv2.VideoCapture(input_video.name)
    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))

    # Initialize video writer for output video
    video_writer = cv2.VideoWriter(output_path,
                                    cv2.VideoWriter_fourcc(*'mp4v'),
                                    fps,
                                    (w, h))

    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
        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_path

# Gradio Interface
inputs = gr.inputs.Video(label="Upload a video")
outputs = gr.outputs.Video(label="Output Video")

gr.Interface(count_workouts, inputs, outputs, title="Workout Counter",
             description="Upload a video and get a video with workout counting annotations.").launch()



# from ultralytics import YOLO
# from ultralytics.solutions import ai_gym
# import cv2

# model = YOLO("yolov8n-pose.pt")
# cap = cv2.VideoCapture("pullups.mp4")
# 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.avi",
#                                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
#     results = model.track(im0, verbose=False)  # Tracking recommended
#     #results = model.predict(im0)  # Prediction also supported
#     im0 = gym_object.start_counting(im0, results, frame_count)
#     video_writer.write(im0)

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