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

def analyze_posture(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."

    posture_score = frame_count = 0
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        
        left_half = frame[:, :frame.shape[1]//2]
        right_half = cv2.flip(frame[:, frame.shape[1]//2:], 1)
        posture_score += np.sum(cv2.absdiff(left_half, right_half))
        frame_count += 1

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

    avg_posture_score = posture_score / frame_count if frame_count > 0 else 0
    posture_quality = "Good" if avg_posture_score < 1000000 else "Fair" if avg_posture_score < 2000000 else "Poor"

    return f"Posture quality: {posture_quality}\nAverage posture score: {avg_posture_score:.2f}"

def create_posture_analysis_tab():
    with gr.Column():
        video_input = gr.Video()
        analyze_button = gr.Button("Analyze")
        output = gr.Textbox(label="Analysis Results")
        
        analyze_button.click(analyze_posture, inputs=video_input, outputs=output)
        
        # Add examples
        gr.Examples(
            examples=["./assets/videos/fitness.mp4"],
            inputs=video_input
        )