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

def infer_infographics(image, question):
    model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-ai2d-base").to("cuda")
    processor = Pix2StructProcessor.from_pretrained("google/pix2struct-ai2d-base")

    inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")

    predictions = model.generate(**inputs)
    return processor.decode(predictions[0], skip_special_tokens=True)

def infer_ui(image, question):
    model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-screen2words-base").to("cuda")
    processor = Pix2StructProcessor.from_pretrained("google/pix2struct-screen2words-base")

    inputs = processor(images=image,text=question, return_tensors="pt").to("cuda")

    predictions = model.generate(**inputs)
    return processor.decode(predictions[0], skip_special_tokens=True)

def infer_chart(image, question):
  model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-chartqa-base").to("cuda")
  processor = Pix2StructProcessor.from_pretrained("google/pix2struct-chartqa-base")

  inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")

  predictions = model.generate(**inputs)
  return processor.decode(predictions[0], skip_special_tokens=True)

def infer_doc(image, question):
  model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-docvqa-base").to("cuda")
  processor = Pix2StructProcessor.from_pretrained("google/pix2struct-docvqa-base")
  inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")
  predictions = model.generate(**inputs)
  return processor.decode(predictions[0], skip_special_tokens=True)

css = """
  #mkd {
    height: 500px; 
    overflow: auto; 
    border: 1px solid #ccc; 
  }
"""

with gr.Blocks(css=css) as demo:
  gr.HTML("<h1><center>Pix2Struct 📄<center><h1>")
  gr.HTML("<h3><center>Pix2Struct is a powerful backbone for visual question answering. ⚡</h3>")
  gr.HTML("<h3><center>Each tab in this app demonstrates Pix2Struct models fine-tuned on document question answering, infographics question answering, question answering on user interfaces, and charts. 📄📱📊<h3>")
  gr.HTML("<h3><center>This app has base versions of each model. For better performance, use large checkpoints.<h3>")

  with gr.Tab(label="Visual Question Answering over Documents"):
    with gr.Row():
      with gr.Column():
        input_img = gr.Image(label="Input Document")
        question = gr.Text(label="Question")
        submit_btn = gr.Button(label="Submit")
      output = gr.Text(label="Answer")
    gr.Examples(
    [["docvqa_example.png", "How many items are sold?"]],
    inputs = [input_img, question],
    outputs = [output],
    fn=infer_doc,
    cache_examples=True,
    label='Click on any Examples below to get Document Question Answering results quickly 👇'
    )

    submit_btn.click(infer_doc, [input_img, question], [output])

  with gr.Tab(label="Visual Question Answering over Infographics"):
    with gr.Row():
      with gr.Column():
        input_img = gr.Image(label="Input Image")
        question = gr.Text(label="Question")
        submit_btn = gr.Button(label="Submit")
      output = gr.Text(label="Answer")
    gr.Examples(
    [["infographics_example.jpeg", "What is this infographic about?"]],
    inputs = [input_img, question],
    outputs = [output],
    fn=infer_doc,
    cache_examples=True,
    label='Click on any Examples below to get Infographics QA results quickly 👇'
    )

    submit_btn.click(infer_infographics, [input_img, question], [output])
  with gr.Tab(label="Caption User Interfaces"):
    with gr.Row():
      with gr.Column():
        input_img = gr.Image(label="Input UI Image")
        question = gr.Text(label="Question")
        submit_btn = gr.Button(label="Submit")
      output = gr.Text(label="Caption")
    submit_btn.click(infer_chart, [input_img, question], [output])
    gr.Examples(
    [["screen2words_ui_example.png", "What is this UI about?"]],
    inputs = [input_img, question],
    outputs = [output],
    fn=infer_doc,
    cache_examples=True,
    label='Click on any Examples below to get UI question answering results quickly 👇'
    )

  with gr.Tab(label="Ask about Charts"):
    with gr.Row():
      with gr.Column():
        input_img = gr.Image(label="Input Chart")
        question = gr.Text(label="Question")
        submit_btn = gr.Button(label="Submit")
      output = gr.Text(label="Caption")

    submit_btn.click(infer_chart, [input_img, question], [output])
    gr.Examples(
    [["chartqa_example.png", "How much percent is bicycle?"]],
    inputs = [input_img, question],
    outputs = [output],
    fn=infer_doc,
    cache_examples=True,
    label='Click on any Examples below to get Chart question answering results quickly 👇'
    )

demo.launch(debug=True)