app.py

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

model = YOLO("best0715.pt")

def detect(image, conf_threshold=0.3):
    results = model(image, conf=conf_threshold)
    
    if len(results) == 0 or results[0].boxes is None or len(results[0].boxes) == 0:
        return image, "No bear detected."
    
    # 先取得帶有標註的圖片（此時是 BGR 格式）
    plotted = results[0].plot()  # 這是 numpy array，BGR 格式
    
    # 手動轉成 RGB（因為 Gradio 和 PIL 顯示需要 RGB）
    plotted_rgb = cv2.cvtColor(plotted, cv2.COLOR_BGR2RGB)
    
    # 取得最高信心度的分數
    conf = results[0].boxes.conf[0].item()
    
    return Image.fromarray(plotted_rgb), f"Detected bear with confidence {conf:.2f}"

iface = gr.Interface(
    fn=detect,
    inputs=[gr.Image(type="pil"), gr.Slider(0.1, 0.9, value=0.3, label="Confidence Threshold")],
    outputs=[gr.Image(type="pil"), gr.Textbox(label="Detection Result")],
    title="Taiwanese Black Bear Detector",
    description="Upload an image for detection."
)

iface.launch(share=True)