TB-OCR / app.py
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ABOUT = """
# TB-OCR Unofficial Demo
This is an unofficial demo of [yifeihu/TB-OCR-preview-0.1](https://huggingface.co/yifeihu/TB-OCR-preview-0.1).
Overview of TB-OCR:
> TB-OCR-preview (Text Block OCR), created by [Yifei Hu](https://x.com/hu_yifei), is an end-to-end OCR model handling text, math latex, and markdown formats all at once. The model takes a block of text as the input and returns clean markdown output. Headers are marked with `##`. Math expressions are guaranteed to be wrapped in brackets `\( inline math \) \[ display math \]` for easier parsing. This model does not require line-detection or math formula detection.
(From the [model card](https://huggingface.co/yifeihu/TB-OCR-preview-0.1))
"""
# check out https://huggingface.co/microsoft/Phi-3.5-vision-instruct for more details
import torch
from transformers import AutoModelForCausalLM, AutoProcessor
from PIL import Image
import requests
model_id = "yifeihu/TB-OCR-preview-0.1"
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map=DEVICE,
trust_remote_code=True,
torch_dtype="auto",
# _attn_implementation='flash_attention_2',
#load_in_4bit=True # Optional: Load model in 4-bit mode to save memory
)
processor = AutoProcessor.from_pretrained(model_id,
trust_remote_code=True,
num_crops=16
)
def phi_ocr(image_url):
question = "Convert the text to markdown format."
image = Image.open(image_url)
prompt_message = [{
'role': 'user',
'content': f'<|image_1|>\n{question}',
}]
prompt = processor.tokenizer.apply_chat_template(prompt_message, tokenize=False, add_generation_prompt=True)
inputs = processor(prompt, [image], return_tensors="pt").to(DEVICE)
generation_args = {
"max_new_tokens": 1024,
"temperature": 0.1,
"do_sample": False
}
generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args
)
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
response = response.split("<image_end>")[0] # remove the image_end token
return response
import gradio as gr
with gr.Blocks() as demo:
gr.Markdown(ABOUT)
with gr.Row():
with gr.Column():
img = gr.Image(label="Input image", type="filename")
btn = gr.Button("OCR")
with gr.Column():
out = gr.Markdown()
btn.click(phi_ocr, inputs=img, outputs=out)
demo.queue().launch()