Create app.py

app.py

@@ -0,0 +1,39 @@
+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")