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DocterXray-v1.2
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import spaces
import os
import sys
import subprocess

def install_packages():
    subprocess.check_call([sys.executable, "-m", "pip", "install", "unsloth-zoo"])
    subprocess.check_call([sys.executable, "-m", "pip", "install", "--no-deps", "git+https://github.com/unslothai/unsloth.git"])

try:
    install_packages()
except Exception as e:
    print(f"Failed to install packages: {e}")

# [แก้ 1] ย้าย environment variables มาไว้ก่อน imports
import os
os.environ['NVIDIA_VISIBLE_DEVICES'] = ''

import warnings
import torch
torch._dynamo.config.suppress_errors = True
torch._dynamo.config.verbose = False

# [แก้ 2] ย้าย imports มาไว้ที่ module level
from unsloth import FastVisionModel
from transformers import AutoModelForVision2Seq
from transformers import TextStreamer
import gradio as gr
from huggingface_hub import login
from PIL import Image

warnings.filterwarnings('ignore')

if 'HUGGING_FACE_HUB_TOKEN' in os.environ:
    print("กำลังเข้าสู่ระบบ Hugging Face Hub...")
    login(token=os.environ['HUGGING_FACE_HUB_TOKEN'])
else:
    print("คำเตือน: ไม่พบ HUGGING_FACE_HUB_TOKEN")

# [แก้ 3] เพิ่ม @spaces.GPU decorator
@spaces.GPU
def model_context():
    _tokenizer = None
    _model = None

    def init_models():
        nonlocal _tokenizer, _model
        try:
            print("กำลังโหลด tokenizer...")
            # [แก้ 4] ลบ imports ออกจาก function
            base_model, _tokenizer = FastVisionModel.from_pretrained(
                "unsloth/Llama-3.2-11B-Vision-Instruct",
                use_gradient_checkpointing = "unsloth"
            )
            print("โหลด tokenizer สำเร็จ")

            print("กำลังโหลดโมเดล fine-tuned...")
            # [แก้ 5] ลบ import ออกจาก function
            _model = AutoModelForVision2Seq.from_pretrained(
                "Aekanun/Llama-3.2-11B-Vision-Instruct-XRay",
                load_in_4bit=True,
                torch_dtype=torch.float16
            ).to('cuda')
            FastVisionModel.for_inference(_model)
            print("โหลดโมเดลสำเร็จ!")
            return True
        except Exception as e:
            print(f"เกิดข้อผิดพลาดในการโหลดโมเดล: {str(e)}")
            return False

    def decorator(func):
        def wrapper(*args, **kwargs):
            return func(_model, _tokenizer, *args, **kwargs)
        return wrapper

    return init_models, decorator

init_models, model_decorator = model_context()

@model_decorator
@spaces.GPU(duration=30)
def process_image(_model, _tokenizer, image):
    if image is None:
        return "กรุณาอัพโหลดรูปภาพ"
    
    try:
        if not isinstance(image, Image.Image):
            image = Image.fromarray(image)

        instruction = "You are an expert radiographer. Describe accurately what you see in this image."
        messages = [
            {"role": "user", "content": [
                {"type": "image"},
                {"type": "text", "text": instruction}
            ]}
        ]

        input_text = _tokenizer.apply_chat_template(messages, add_generation_prompt=True)
        inputs = _tokenizer(
            image,
            input_text,
            add_special_tokens=False,
            return_tensors="pt",
        ).to("cuda")

        text_streamer = TextStreamer(_tokenizer, skip_prompt=True)
        outputs = _model.generate(
            **inputs,
            streamer=text_streamer,
            max_new_tokens=128,
            use_cache=True,
            temperature=1.5,
            min_p=0.1
        )
        
        return _tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
        
    except Exception as e:
        return f"เกิดข้อผิดพลาด: {str(e)}"

print("กำลังเริ่มต้นแอปพลิเคชัน...")
if init_models():
    demo = gr.Interface(
        fn=process_image,
        inputs=gr.Image(type="pil"),
        outputs=gr.Textbox(),
        title="Medical Vision Analysis"
    )
    
    if __name__ == "__main__":
        demo.launch()
else:
    print("ไม่สามารถเริ่มต้นแอปพลิเคชันได้")