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import gradio as gr
import PyPDF2
import io
import requests
import torch
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
# Download and load pre-trained model and tokenizer
model_name = "distilbert-base-cased-distilled-squad"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
def answer_questions(pdf_file, questions):
# Load PDF file and extract text
pdf_reader = PyPDF2.PdfFileReader(io.BytesIO(pdf_file.read()))
text = ""
for i in range(pdf_reader.getNumPages()):
page = pdf_reader.getPage(i)
text += page.extractText()
text = text.strip()
answers = []
for question in questions:
# Tokenize question and text
input_ids = tokenizer.encode(question, text)
# Perform question answering
outputs = model(torch.tensor([input_ids]), return_dict=True)
answer_start = outputs.start_logits.argmax().item()
answer_end = outputs.end_logits.argmax().item()
answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end+1]))
answers.append(answer)
return answers
inputs = [
gr.inputs.File(label="PDF document"),
gr.inputs.Textbox(label="Questions (one per line)", type="textarea")
]
outputs = gr.outputs.Textarea(label="Answers")
gr.Interface(fn=answer_questions, inputs=inputs, outputs=outputs, title="PDF Question Answering Tool",
description="Upload a PDF document and ask multiple questions. The app will use a pre-trained model to find the answers.").launch()
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