app.py

import gradio as gr
import torch
from transformers import AutoModelForVision2Seq, AutoProcessor, BitsAndBytesConfig
from PIL import Image

# Global variables for model and processor
model = None
processor = None

def load_model_and_processor():
    global model, processor
    
    model_path = "Aekanun/thai-handwriting-llm"
    base_model_path = "meta-llama/Llama-3.2-11B-Vision-Instruct"
    
    # BitsAndBytes config for 4-bit quantization
    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=torch.bfloat16
    )
    
    try:
        # Load processor from base model
        processor = AutoProcessor.from_pretrained(base_model_path)
        
        # Load fine-tuned model
        model = AutoModelForVision2Seq.from_pretrained(
            model_path,
            device_map="auto",
            torch_dtype=torch.bfloat16,
            quantization_config=bnb_config
        )
        return True
    except Exception as e:
        print(f"Error loading model: {str(e)}")
        return False

def process_handwriting(image):
    global model, processor
    
    if image is None:
        return "กรุณาอัพโหลดรูปภาพ"
    
    try:
        # Ensure image is in PIL format
        if not isinstance(image, Image.Image):
            image = Image.fromarray(image)
        
        # Prepare prompt and messages
        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}
                ],
            }
        ]
        
        # Process input
        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()}
        
        # Generate output
        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_new_tokens=256,
                do_sample=False,
                pad_token_id=processor.tokenizer.pad_token_id
            )
        
        # Decode output
        transcription = processor.decode(outputs[0], skip_special_tokens=True)
        return transcription
    
    except Exception as e:
        return f"เกิดข้อผิดพลาด: {str(e)}"

# Load model when starting
print("กำลังโหลดโมเดล...")
model_loaded = load_model_and_processor()

if model_loaded:
    # Create Gradio interface
    demo = gr.Interface(
        fn=process_handwriting,
        inputs=gr.Image(type="pil", label="อัพโหลดรูปลายมือเขียนภาษาไทย"),
        outputs=gr.Textbox(label="ข้อความที่แปลงได้"),
        title="Thai Handwriting to Text ด้วย LLaMA Vision",
        description="อัพโหลดรูปภาพลายมือเขียนภาษาไทยเพื่อแปลงเป็นข้อความ โดยใช้โมเดล LLaMA Vision ที่ fine-tune มาสำหรับภาษาไทย",
        examples=[["example1.jpg"], ["example2.jpg"]]
    )
    
    if __name__ == "__main__":
        demo.launch(share=True)
else:
    print("ไม่สามารถโหลดโมเดลได้ กรุณาตรวจสอบ log")