import gradio as gr
# from PIL import Image
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor


model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-docvqa-large")
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-docvqa-large")

def process_document(image, question):
    # image = Image.open(image)
    inputs = processor(images=image, text=question, return_tensors="pt")
    predictions = model.generate(**inputs)
    return processor.decode(predictions[0], skip_special_tokens=True)
 
description = "Demo for pix2struct fine-tuned on DocVQA (document visual question answering). To use it, simply upload your image and type a question and click 'submit', or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/pdf/2210.03347.pdf' target='_blank'>PIX2STRUCT: SCREENSHOT PARSING AS PRETRAINING FOR VISUAL LANGUAGE UNDERSTANDING</a></p>"

demo = gr.Interface(
    fn=process_document,
    inputs=["image", "text"],
    outputs="text",
    title="Demo: pix2struct for DocVQA",
    description=description,
    article=article,
    # enable_queue=True,
    examples=[["example_1.png", "When is the coffee break?"], ["example_2.jpeg", "What's the population of Stoddard?"]],
    cache_examples=False)

demo.queue()
demo.launch()