app.py

import gradio as gr
import requests
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
import os 

##Bloom
API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
HF_API_TOKEN = os.getenv["HF_API_TOKEN"]
headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}

def _add_markup(table):
    parts = [p.strip() for p in table.splitlines(keepends=False)]
    if parts[0].startswith('TITLE'):
        result = f"Title: {parts[0].split(' | ')[1].strip()}\n"
        rows = parts[1:]
    else:
        result = ''
        rows = parts
    prefixes = ['Header: '] + [f'Row {i+1}: ' for i in range(len(rows) - 1)]
    return result + '\n'.join(prefix + row for prefix, row in zip(prefixes, rows))


_TABLE = """Year | Democrats | Republicans | Independents
2004 | 68.1% | 45.0% | 53.0%
2006 | 58.0% | 42.0% | 53.0%
2007 | 59.0% | 38.0% | 45.0%
2009 | 72.0% | 49.0% | 60.0%
2011 | 71.0% | 51.2% | 58.0%
2012 | 70.0% | 48.0% | 53.0%
2013 | 72.0% | 41.0% | 60.0%"""

_INSTRUCTION = 'Read the table below to answer the following questions.'


_TEMPLATE = f"""{_INSTRUCTION}
{_add_markup(_TABLE)}
Q: In which year republicans have the lowest favor rate?
A: Let's find the column of republicans. Then let's extract the favor rates, they [45.0, 42.0, 38.0, 49.0, 51.2, 48.0, 41.0]. The smallest number is 38.0, that's Row 3.  Row 3 is year 2007. The answer is 2007.
Q: What is the sum of Democrats' favor rates of 2004, 2012, and 2013?
A: Let's find the rows of years 2004, 2012, and 2013. We find Row 1, 6, 7. The favor dates of Demoncrats on that 3 rows are 68.1, 70.0, and 72.0. 68.1+70.0+72=210.1. The answer is 210.1.
Q: By how many points do Independents surpass Republicans in the year of 2011?
A: Let's find the row with year = 2011. We find Row 5. We extract Independents and Republicans' numbers. They are 58.0 and 51.2. 58.0-51.2=6.8. The answer is 6.8.
Q: Which group has the overall worst performance?
A: Let's sample a couple of years. In Row 1, year 2004, we find Republicans having the lowest favor rate 45.0 (since 45.0<68.1, 45.0<53.0). In year 2006, Row 2, we find Republicans having the lowest favor rate 42.0 (42.0<58.0, 42.0<53.0). The trend continues to other years. The answer is Republicans.
Q: Which party has the second highest favor rates in 2007?
A: Let's find the row of year 2007, that's Row 3. Let's extract the numbers on Row 3: [59.0, 38.0, 45.0]. 45.0 is the second highest. 45.0 is the number of Independents. The answer is Independents.
{_INSTRUCTION}"""

def text_generate(prompt, table, problem):
    p = prompt  + "\n" + table + "\n" + "Q: " + problem
    # print(f"Final prompt is : {p}")
    json_ = {"inputs": p,
            "parameters":
            {
            "top_p": 0.9,
          "temperature": 1.1,
          "max_new_tokens": 64,
          "return_full_text": True
          }, "options": 
              {
              "use_cache": True,
              "wait_for_model":True
              },}
    response = requests.post(API_URL, headers=headers, json=json_)
    print(f"Response  is : {response}")
    output = response.json()
    print(f"output is : {output}") #{output}")
    output_tmp = output[0]['generated_text']
    print(f"output_tmp is: {output_tmp}")
    #solution = output_tmp.split("\nQ:")[0]   #output[0]['generated_text'].split("Q:")[0] # +"."
    #print(f"Final response after splits is: {solution}")

    #return solution 
    return output_tmp




model_deplot = Pix2StructForConditionalGeneration.from_pretrained("belkada/deplot")
processor_deplot = Pix2StructProcessor.from_pretrained("belkada/deplot")

def process_document(image, question):
    # image = Image.open(image)
    inputs = processor_deplot(images=image, text="Generate the underlying data table for the figure below:", return_tensors="pt")
    predictions = model_deplot.generate(**inputs)
    table = processor_deplot.decode(predictions[0], skip_special_tokens=True)

    # send prompt+table to LLM
    res = text_generate(_TEMPLATE, table, question)
    print (res)
 
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: deplot+llm test",
    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.launch()