import gradio as gr
import cv2
import numpy as np
import tempfile
import os

def analyze_body_movement(video):
    with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as temp_file:
        video_path = video if isinstance(video, str) else temp_file.name
        if not isinstance(video, str):
            temp_file.write(video)

    cap = cv2.VideoCapture(video_path)
    if not cap.isOpened():
        return "Error: Unable to open video file."

    frame_count = movement_score = 0
    prev_gray = None

    while True:
        ret, frame = cap.read()
        if not ret:
            break
        
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        if prev_gray is not None:
            movement_score += np.sum(cv2.absdiff(prev_gray, gray))
        prev_gray = gray
        frame_count += 1

    cap.release()
    if not isinstance(video, str):
        os.unlink(video_path)

    avg_movement = movement_score / (frame_count - 1) if frame_count > 1 else 0
    movement_level = "Low" if avg_movement < 1000 else "Medium" if avg_movement < 5000 else "High"

    return f"Movement level: {movement_level}\nAverage movement score: {avg_movement:.2f}"

def create_body_movement_tab():
    with gr.Column():
        with gr.Row():
            with gr.Column():
                video_input = gr.Video()
                analyze_button = gr.Button("Analyze")
            output = gr.Textbox(label="Analysis Results")
        
        # Add the example here
        gr.Examples(["./assets/videos/fitness.mp4"], [video_input])
        
        analyze_button.click(analyze_body_movement, inputs=video_input, outputs=output)