import cv2
from cvzone.PoseModule import PoseDetector
import gradio as gr

# Initialize pose detector
poseDetector = PoseDetector()

# Function to process video and detect poses
def process_video(video_path):
    cap = cv2.VideoCapture(video_path)
    posList = []
    output_frames = []

    while True:
        success, img = cap.read()
        if not success:
            break

        # Detect pose
        img = poseDetector.findPose(img)
        lmList, bboxInfo = poseDetector.findPosition(img)

        if bboxInfo:
            lmString = ''
            for lm in lmList:
                lmString += f'{lm[0]},{img.shape[0]-lm[1]},{lm[2]},'
            posList.append(lmString)

        # Convert frame to RGB for Gradio display
        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        output_frames.append(img_rgb)

    # Release video capture
    cap.release()

    # Save pose data to a file
    with open("AnimationFile.txt", "w") as f:
        f.writelines(["%s\n" % item for item in posList])

    # Return the processed frames as a video
    return output_frames

# Gradio interface
def gradio_interface(video):
    frames = process_video(video)
    return frames

# Create Gradio app
iface = gr.Interface(
    fn=gradio_interface,
    inputs=gr.Video(label="Input Video"),
    outputs=gr.Video(label="Processed Video"),
    title="Pose Detection with MediaPipe",
    description="Upload a video to detect human poses using MediaPipe and OpenCV.",
)

# Launch the app
iface.launch()