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Running
on
Zero
Running
on
Zero
import os | |
import warnings | |
import torch | |
import gc | |
from transformers import AutoModelForVision2Seq, AutoProcessor | |
from peft import PeftModel | |
from PIL import Image | |
import gradio as gr | |
from huggingface_hub import login | |
import spaces # เพิ่ม import spaces | |
# Basic settings | |
warnings.filterwarnings('ignore') | |
# Global variables | |
model = None | |
processor = None | |
# Login to Hugging Face Hub | |
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") | |
def load_model_and_processor(): | |
"""โหลดโมเดลและ processor""" | |
global model, processor | |
print("กำลังโหลดโมเดลและ processor...") | |
try: | |
# Model paths | |
base_model_path = "meta-llama/Llama-3.2-11B-Vision-Instruct" | |
adapter_path = "Aekanun/thai-handwriting-llm" | |
# Load processor from base model | |
print("กำลังโหลด processor...") | |
processor = AutoProcessor.from_pretrained( | |
base_model_path, | |
use_auth_token=True | |
) | |
# Load base model | |
print("กำลังโหลด base model...") | |
base_model = AutoModelForVision2Seq.from_pretrained( | |
base_model_path, | |
device_map="auto", | |
torch_dtype=torch.float16, # เปลี่ยนกลับเป็น float16 | |
trust_remote_code=True, | |
use_auth_token=True | |
) | |
# Load adapter | |
print("กำลังโหลด adapter...") | |
model = PeftModel.from_pretrained( | |
base_model, | |
adapter_path, | |
device_map="auto", # ให้จัดการ device map อัตโนมัติ | |
torch_dtype=torch.float16, | |
use_auth_token=True | |
) | |
print("โหลดโมเดลสำเร็จ!") | |
return True | |
except Exception as e: | |
print(f"เกิดข้อผิดพลาดในการโหลดโมเดล: {str(e)}") | |
return False | |
# ใช้ GPU decorator กำหนดเวลาสูงสุด 30 วินาที | |
def process_handwriting(image): | |
"""ฟังก์ชันสำหรับ Gradio interface""" | |
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) | |
# Create prompt | |
prompt = """Transcribe the Thai handwritten text from the provided image. | |
Only return the transcription in Thai language.""" | |
# Create model inputs | |
messages = [ | |
{ | |
"role": "user", | |
"content": [ | |
{"type": "text", "text": prompt}, | |
{"type": "image", "image": image} | |
], | |
} | |
] | |
# Process with model | |
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 | |
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.strip() | |
except Exception as e: | |
return f"เกิดข้อผิดพลาด: {str(e)}" | |
# Initialize application | |
print("กำลังเริ่มต้นแอปพลิเคชัน...") | |
if load_model_and_processor(): | |
# Create Gradio interface | |
demo = gr.Interface( | |
fn=process_handwriting, | |
inputs=gr.Image(type="pil", label="อัพโหลดรูปลายมือเขียนภาษาไทย"), | |
outputs=gr.Textbox(label="ข้อความที่แปลงได้"), | |
title="Thai Handwriting Recognition and Vision-Language", | |
description="อัพโหลดรูปภาพลายมือเขียนภาษาไทยเพื่อแปลงเป็นข้อความ", | |
examples=[["example1.jpg"], ["example2.jpg"]] | |
) | |
if __name__ == "__main__": | |
demo.launch(show_error=True) | |
else: | |
print("ไม่สามารถเริ่มต้นแอปพลิเคชันได้") |