import gradio as gr
from transformers import DetrImageProcessor, DetrForObjectDetection
import torch
from PIL import Image, ImageDraw

# โหลดโมเดลจาก Hugging Face
model_name = "facebook/detr-resnet-50"  # ตัวอย่างโมเดล DETR
processor = DetrImageProcessor.from_pretrained(model_name)
model = DetrForObjectDetection.from_pretrained(model_name)

# ฟังก์ชันตรวจจับวัตถุ/อวัยวะ
def detect_objects(image):
    # แปลงรูปเป็น tensor
    inputs = processor(images=image, return_tensors="pt")
    outputs = model(**inputs)

    # ดึงผลลัพธ์
    target_sizes = torch.tensor([image.size[::-1]])
    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.5)[0]

    draw = ImageDraw.Draw(image)
    boxes_info = []

    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        box = [round(i, 2) for i in box.tolist()]
        draw.rectangle(box, outline="red", width=3)
        boxes_info.append({
            "box": box,
            "label": model.config.id2label[label.item()],
            "score": round(score.item(), 3)
        })

    return image, boxes_info

# สร้าง UI ด้วย Gradio
with gr.Blocks() as demo:
    img_input = gr.Image(type="pil")
    output_img = gr.Image(type="pil")
    output_info = gr.JSON()

    btn = gr.Button("ตรวจจับอวัยวะ")
    btn.click(detect_objects, inputs=img_input, outputs=[output_img, output_info])

demo.launch()