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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 stream."""
text = ""
with fitz.open(stream=pdf_file.read(), filetype="pdf") as doc:
for page in doc:
text += page.get_text()
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.text_area("Simplified Output", simplified_text, height=150)
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)
st.subheader("Extracted Text from PDF:")
st.text_area("Extracted Text", extracted_text, height=200)
if st.button("Simplify Extracted Text"):
simplified_text = simplify_text(extracted_text[:1000]) # Limit length for model input
st.subheader("Simplified Text:")
st.text_area("Simplified Output", simplified_text, height=150)
st.write("\nMade by Harshitha")
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