app.py

import os
import warnings
import torch
import gc
from transformers import AutoModelForVision2Seq, AutoProcessor, BitsAndBytesConfig
from PIL import Image
import gradio as gr
from huggingface_hub import login

warnings.filterwarnings('ignore')
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

# Global variables
model = None
processor = None

if torch.cuda.is_available():
    torch.cuda.empty_cache()
    gc.collect()
    print("เคลียร์ CUDA cache เรียบร้อยแล้ว")

def load_model_and_processor():
    """โหลดโมเดลและ processor"""
    global model, processor
    print("กำลังโหลดโมเดลและ processor...")
    
    try:
        base_model_path = "meta-llama/Llama-3.2-11B-Vision-Instruct"
        hub_model_path = "Aekanun/thai-handwriting-llm"
        
        bnb_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_compute_dtype=torch.bfloat16
        )
        
        # โหลด processor จาก base model
        print("Loading processor...")
        processor = AutoProcessor.from_pretrained(
            base_model_path, 
            token=os.environ.get('HUGGING_FACE_HUB_TOKEN')
        )
        
        # โหลดโมเดลจาก Hub
        print("Loading model...")
        model = AutoModelForVision2Seq.from_pretrained(
            hub_model_path,
            device_map="auto",
            torch_dtype=torch.bfloat16,
            quantization_config=bnb_config,
            token=os.environ.get('HUGGING_FACE_HUB_TOKEN')
        )
        print("Model loaded successfully!")
        
        return True
    except Exception as e:
        print(f"เกิดข้อผิดพลาดในการโหลดโมเดล: {str(e)}")
        return False

def process_handwriting(image):
    global model, processor
    
    if image is None:
        return "กรุณาอัพโหลดรูปภาพ"
    
    try:
        if not isinstance(image, Image.Image):
            image = Image.fromarray(image)
            
        if image.mode != "RGB":
            image = image.convert("RGB")

        prompt = """Transcribe the Thai handwritten text from the provided image.
Only return the transcription in Thai language."""

        messages = [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt},
                    {"type": "image", "image": image}
                ],
            }
        ]

        text = processor.apply_chat_template(messages, tokenize=False)
        inputs = processor(text=text, images=image, return_tensors="pt")
        inputs = {k: v.to(model.device) for k, v in inputs.items()}

        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_new_tokens=256,
                do_sample=False,
                pad_token_id=processor.tokenizer.pad_token_id
            )

        transcription = processor.decode(outputs[0], skip_special_tokens=True)
        return transcription.strip()
        
    except Exception as e:
        return f"เกิดข้อผิดพลาด: {str(e)}"

print("กำลังเริ่มต้นแอปพลิเคชัน...")
if load_model_and_processor():
    demo = gr.Interface(
        fn=process_handwriting,
        inputs=gr.Image(type="pil", label="อัพโหลดรูปลายมือเขียนภาษาไทย"),
        outputs=gr.Textbox(label="ข้อความที่แปลงได้"),
        title="Thai Handwriting Recognition",
        description="อัพโหลดรูปภาพลายมือเขียนภาษาไทยเพื่อแปลงเป็นข้อความ"
    )

    if __name__ == "__main__":
        demo.launch()
else:
    print("ไม่สามารถเริ่มต้นแอปพลิเคชันได้")