import gradio as gr
from PIL import Image
import numpy as np
from ultralytics import YOLO
import cv2
import tempfile
import os

# 載入模型
model = YOLO('last.pt')  # 使用您的模型

def process_image(input_image):
    # 將輸入圖片轉換為 numpy array
    if isinstance(input_image, np.ndarray):
        image = input_image
    else:
        image = np.array(input_image)
    
    # 使用模型進行預測
    results = model.predict(image)
    
    # 獲取預測結果
    result = results[0]
    
    # 直接獲取繪製好的結果圖片
    result_image = result.plot()
    result_pil = Image.fromarray(result_image)
    
    return result_pil

def process_video(input_video):
    # 創建臨時檔案來保存處理後的影片
    temp_output = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
    output_path = temp_output.name
    temp_output.close()
    
    # 讀取輸入影片
    cap = cv2.VideoCapture(input_video)
    
    # 獲取影片屬性
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = int(cap.get(cv2.CAP_PROP_FPS))
    
    # 創建影片寫入器
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
    
    # 處理每一幀
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
            
        # 使用模型進行預測
        results = model.predict(frame)
        result = results[0]
        
        # 獲取處理後的幀
        result_frame = result.plot()
        
        # 寫入處理後的幀
        out.write(result_frame)
    
    # 釋放資源
    cap.release()
    out.release()
    
    return output_path

# 創建 Gradio 介面
with gr.Blocks() as demo:
    gr.Markdown("# 磁磚檢測系統")
    
    with gr.Tabs():
        with gr.TabItem("圖片檢測"):
            with gr.Row():
                input_image = gr.Image(label="上傳圖片")
                output_image = gr.Image(label="檢測結果")
            
            submit_btn = gr.Button("開始檢測")
            
            # 設置事件處理
            submit_btn.click(
                fn=process_image,
                inputs=input_image,
                outputs=output_image
            )
        
        with gr.TabItem("影片檢測"):
            with gr.Row():
                input_video = gr.Video(label="上傳影片")
            
            video_submit_btn = gr.Button("開始檢測")
            output_file = gr.File(label="下載處理後的影片")
            
            # 設置事件處理
            video_submit_btn.click(
                fn=process_video,
                inputs=input_video,
                outputs=output_file
            )

if __name__ == "__main__":
    demo.launch()