import streamlit as st
import cv2
import numpy as np
from PIL import Image
from ultralytics import YOLO

model = YOLO('yolo12n.pt')
	
# Streamlit 網頁介面
st.title('上傳圖檔執行YOLO物體偵測')
source_img = st.file_uploader(
	label = '選擇圖檔...',
	type = ('jpg', 'jpeg', 'png', 'bmp', 'webp')
)

col1, col2 = st.columns(2)

with col1:
    if source_img:
        uploaded_image = Image.open(source_img)
        st.image(image = source_img,
                 caption = '上傳圖檔',
                 width = 'stretch')

if source_img:
    if st.button('執行'):
        with st.spinner('執行中...'):
            res = model.predict(uploaded_image, conf = 0.6)                    
            res_plotted = res[0].plot()[:, :, ::-1]
            with col2:
                st.image(res_plotted,
                         caption = '偵測結果影像',
                         width = 'stretch')
                try:
                    with st.expander('偵測結果'):
                        for box in res[0].boxes:
                            cords = box.xyxy[0].tolist()
                            cords = [round(x) for x in cords]
                            class_id = int(box.cls[0].item())
                            conf = box.conf[0].item()
                            conf = round(conf * 100, 2)
                            st.write('分類編號:', class_id)
                            st.write('分類名稱:', model.names[class_id])
                            st.write('邊界框座標:', cords)
                            st.write('信心指數:', conf, '%')
                            st.write('------------------------')    
                except Exception as ex:
                    st.write('尚未有圖檔上傳!')
                    st.write(ex)