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import gradio as gr
from transformers import pipeline
import PyPDF2
# Carregar o modelo de linguagem gratuito da Hugging Face
nlp = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
def extract_text_from_pdf(pdf_file):
with open(pdf_file.name, "rb") as file:
reader = PyPDF2.PdfFileReader(file)
text = ""
for page_num in range(reader.numPages):
page = reader.getPage(page_num)
text += page.extract_text()
return text
def answer_question(pdf_file, question):
# Extrair texto do PDF
context = extract_text_from_pdf(pdf_file)
# Usar o modelo para responder a pergunta
result = nlp(question=question, context=context)
return result['answer']
# Interface Gradio
iface = gr.Interface(
fn=answer_question,
inputs=[
gr.inputs.File(label="Carregar PDF"),
gr.inputs.Textbox(label="Pergunta")
],
outputs=gr.outputs.Textbox(label="Resposta"),
title="QA sobre PDF",
description="Carregue um PDF e faça perguntas sobre o conteúdo."
)
# Iniciar a interface
iface.launch() |