import argparse
import gradio as gr
import torch
from PIL import Image

from donut import DonutModel


def demo_process_vqa(input_img, question):
    global pretrained_model, task_prompt, task_name
#    input_img = Image.fromarray(input_img)
    user_prompt = task_prompt.replace("{user_input}", question)
    output = pretrained_model.inference(input_img, prompt=user_prompt)["predictions"][0]
    return output


def demo_process(input_img):
    global pretrained_model, task_prompt, task_name,security_layer
    input_img = Image.fromarray(input_img)
    sec = security_layer.inference(image=input_img,prompt="<s_rvlcdip>")['predictions'][0]
    print(sec)
    if sec['class']=="invoice":
      output = pretrained_model.inference(image=input_img, prompt="<s_cord-v2>")["predictions"][0]
      return output
    return sec

task_name="cord-v2"
if "docvqa" == task_name:
    task_prompt = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
else:  # rvlcdip, cord, ...
    task_prompt = f"<s_{task_name}>"

security_layer = DonutModel.from_pretrained("naver-clova-ix/donut-base-finetuned-rvlcdip")

pretrained_model = DonutModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")


if torch.cuda.is_available():
    pretrained_model.half()
    security_layer.half()
    device = torch.device("cuda")
    pretrained_model.to(device)
    security_layer.to(device)
else:
  pretrained_model.encoder.to(torch.bfloat16)
  security_layer.encoder.to(torch.bfloat16)

pretrained_model.eval()
security_layer.eval()
    

demo = gr.Interface(
    fn=demo_process_vqa if task_name == "docvqa" else demo_process,
    inputs=["image", "text"] if task_name == "docvqa" else "image",
    outputs="json",
    title=f"Donut 🍩 demonstration for `{task_name}` task",
    concurrency_limit=10,
    description="Get invoice details if invoice"
)

demo.queue(default_concurrency_limit=2,max_size=5)
demo.launch(debug=True,share=True, inline=False)