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import gradio as gr
import pdfplumber
from transformers import pipeline
from io import BytesIO
import re
# Initialize the question-answering pipeline with a specific pre-trained model
qa_pipeline = pipeline("question-answering", model="deepset/gelectra-large-germanquad")
def extract_text_from_pdf(file_obj):
"""Extracts text from a PDF file."""
text = []
with pdfplumber.open(file_obj) as pdf:
for page in pdf.pages:
page_text = page.extract_text()
if page_text: # Make sure there's text on the page
text.append(page_text)
return " ".join(text)
def answer_questions(context):
"""Generates answers to predefined questions based on the provided context."""
questions = [
"Welches ist das Titel des Moduls?",
"Welches ist das Sektor oder das Kernthema?",
"Welches ist das Land?",
"Zu welchem Program oder EZ-Programm gehört das Projekt?"
]
answers = {q: qa_pipeline(question=q, context=context)['answer'] for q in questions}
return answers
def process_pdf(file):
"""Process a PDF file to extract text and then use the text to answer questions."""
# Read the PDF file from Gradio's file input, which is a temporary file path
with file as file_path:
text = extract_text_from_pdf(BytesIO(file_path.read()))
results = answer_questions(text)
return "\n".join(f"{q}: {a}" for q, a in results.items())
# Define the Gradio interface
iface = gr.Interface(
fn=process_pdf,
inputs=gr.inputs.File(type="pdf", label="Upload your PDF file"),
outputs=gr.outputs.Textbox(label="Extracted Information and Answers"),
title="PDF Text Extractor and Question Answerer",
description="Upload a PDF file to extract text and answer predefined questions based on the content."
)
if __name__ == "__main__":
iface.launch() |