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import torch
from transformers import pipeline
import streamlit as st
import fitz # PyMuPDF for PDF text extraction
# Load pretrained model for simplification
simplifier = pipeline("summarization", model="facebook/bart-large-cnn")
def simplify_text(text):
"""Simplifies a given academic text using a pretrained model."""
simplified = simplifier(text, max_length=96, min_length=30, do_sample=False)
return simplified[0]['summary_text']
def extract_text_from_pdf(pdf_file):
"""Extracts text from an uploaded PDF file."""
doc = fitz.open(pdf_file)
text = "\n".join(page.get_text("text") for page in doc)
return text
# Streamlit UI
st.title("Text Simplification with Pretrained Model")
option = st.radio("Choose input type:", ("Text Input", "Upload PDF"))
if option == "Text Input":
user_text = st.text_area("Enter your text:")
if st.button("Simplify") and user_text:
simplified_text = simplify_text(user_text)
st.subheader("Simplified Text:")
st.write(simplified_text)
elif option == "Upload PDF":
uploaded_file = st.file_uploader("Upload a PDF", type=["pdf"])
if uploaded_file:
extracted_text = extract_text_from_pdf(uploaded_file)
simplified_text = simplify_text(extracted_text[:1000]) # Limit text length for processing
st.subheader("Simplified Text:")
st.write(simplified_text)
st.write("\nMade with ❤️ using Pretrained Models")
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