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import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer
import gradio as gr
# Dùng CPU thay vì GPU
device = torch.device("cpu")
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
def build_transform(input_size):
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
])
return transform
def load_image(image, input_size=448):
transform = build_transform(input_size=input_size)
pixel_values = transform(image).unsqueeze(0) # Thêm batch dimension
return pixel_values
# Load model trên CPU
model = AutoModel.from_pretrained(
"5CD-AI/Vintern-1B-v3_5",
torch_dtype=torch.float32, # Dùng float32 cho CPU
low_cpu_mem_usage=True,
trust_remote_code=True,
).eval().to(device)
tokenizer = AutoTokenizer.from_pretrained("5CD-AI/Vintern-1B-v3_5", trust_remote_code=True, use_fast=False)
def process_image(image):
pixel_values = load_image(image).to(device)
generation_config = dict(max_new_tokens=1024, do_sample=False, num_beams=3, repetition_penalty=2.5)
question = "<image>\nTrích xuất toàn bộ thông tin trong ảnh và trả về dạng text."
response, _ = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
return response
iface = gr.Interface(
fn=process_image,
inputs=gr.Image(type="pil"),
outputs="text",
title="Vietnamese Hand Writing ORC",
description="Extract all the information from the image and return it in text form."
)
if __name__ == "__main__":
iface.launch() |