from transformers import DonutProcessor, VisionEncoderDecoderModel
import gradio as gr
from PIL import Image
import torch
import re
from gradio.themes.utils import sizes


theme = gr.themes.Default(radius_size=sizes.radius_none).set(
    block_label_text_color = '#4D63FF',
    block_title_text_color = '#4D63FF',
    button_primary_text_color = '#4D63FF',
    button_primary_background_fill='#FFFFFF',
    button_primary_border_color='#4D63FF',
    button_primary_background_fill_hover='#EDEFFF',
)


processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")
model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")


def vqa(image, question):
    inp = Image.fromarray(image.astype('uint8'), 'RGB')
    pixel_values = processor(inp, return_tensors="pt").pixel_values
    task_prompt = "{user_input}"
    prompt = task_prompt.replace("{user_input}", question)
    decoder_input_ids = processor.tokenizer(prompt, add_special_tokens=False, return_tensors="pt")["input_ids"]
    
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model.to(device)

    outputs = model.generate(pixel_values.to(device),
                               decoder_input_ids=decoder_input_ids.to(device),
                               max_length=model.decoder.config.max_position_embeddings,
                               early_stopping=True,
                               pad_token_id=processor.tokenizer.pad_token_id,
                               eos_token_id=processor.tokenizer.eos_token_id,
                               use_cache=True,
                               num_beams=1,
                               bad_words_ids=[[processor.tokenizer.unk_token_id]],
                               return_dict_in_generate=True,
                               output_scores=True)


    seq = processor.batch_decode(outputs.sequences)[0]
    seq = seq.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
    seq = re.sub(r"<.*?>", "", seq, count=1).strip()

    return processor.token2json(seq)

with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
    gr.Markdown("""
    <div align='center' ><font size='60'>图片问答</font></div>
    """)
    with gr.Row():
        with gr.Column():
            image = gr.Image(label="图片")
            question = gr.Textbox(label="问题")
            with gr.Row():
                button = gr.Button("提交", variant="primary")
        box2 = gr.Textbox(label="文本")

    button.click(fn=vqa, inputs=[image, question], outputs=box2)
    examples = gr.Examples(examples=[['income.png', 'What are the 2020 net sales?'], ['invoice.png','What is the invoice number?']],  inputs=[image, question], label="例子")


if __name__ == "__main__":
    demo.queue(concurrency_count=3).launch(server_name = "0.0.0.0", server_port = 7026)