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import gradio as gr | |
import requests | |
from PIL import Image | |
from transformers import PaliGemmaForConditionalGeneration, PaliGemmaProcessor | |
import spaces | |
import os | |
from huggingface_hub import login | |
login(os.getenv('hf_token')) | |
def infer_ocrvqa(image, question): | |
model = PaliGemmaForConditionalGeneration.from_pretrained("google/paligemma-3b-ft-ocrvqa-896").to("cuda") | |
processor = PaliGemmaProcessor.from_pretrained("google/paligemma-3b-ft-ocrvqa-896") | |
systemprompt = "Ты ассистент по анализу финансовых отчетов. Ниже приведены вопросы по данным на изображении. Необходимо отвечать на вопросы по суммам в таблицах максимально точно и обращать внимание на названия колонок таблиц. Вопросы: " | |
inputs = processor(images=image,text=systemprompt+question, return_tensors="pt").to("cuda") | |
predictions = model.generate(**inputs, max_new_tokens=100) | |
return processor.decode(predictions[0], skip_special_tokens=True)[len(question):].lstrip("\n") | |
def infer_doc(image, question): | |
model = PaliGemmaForConditionalGeneration.from_pretrained("google/paligemma-3b-ft-docvqa-896").to("cuda") | |
processor = PaliGemmaProcessor.from_pretrained("google/paligemma-3b-ft-docvqa-896") | |
inputs = processor(images=image, text=question, return_tensors="pt").to("cuda") | |
predictions = model.generate(**inputs, max_new_tokens=100) | |
return processor.decode(predictions[0], skip_special_tokens=True)[len(question):].lstrip("\n") | |
css = """ | |
#mkd { | |
height: 500px; | |
overflow: auto; | |
border: 1px solid #ccc; | |
} | |
""" | |
with gr.Blocks(css=css) as demo: | |
gr.HTML("<h1><center>PaliGemma для VQA/OCR 📄<center><h1>") | |
gr.HTML("<h3><center>Использование модели as is без файнтюнинга на документах. ⚡</h3>") | |
with gr.Tab(label="Ответы на вопросы по документам"): | |
with gr.Row(): | |
with gr.Column(): | |
input_img = gr.Image(label="Input Document") | |
question = gr.Text(label="Question") | |
submit_btn = gr.Button(value="Submit") | |
output = gr.Text(label="Answer") | |
submit_btn.click(infer_doc, [input_img, question], [output]) | |
with gr.Tab(label="Чтение текста со сканов"): | |
with gr.Row(): | |
with gr.Column(): | |
input_img = gr.Image(label="Input Document") | |
question = gr.Text(label="Question") | |
submit_btn = gr.Button(value="Submit") | |
output = gr.Text(label="Infer") | |
submit_btn.click(infer_ocrvqa, [input_img, question], [output]) | |
demo.launch(debug=True) |