Update app.py

app.py

@@ -1,4 +1,41 @@
+# app.py
+
 import streamlit as st
+import pdfplumber
+from transformers import pipeline
+
+# Load QA model pipeline
+qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")
+
+st.title("📄 PDF Question Answering using LLM")
+
+uploaded_pdf = st.file_uploader("Upload a PDF", type=["pdf"])
+
+if uploaded_pdf:
+    # Extract text from PDF
+    with pdfplumber.open(uploaded_pdf) as pdf:
+        extracted_text = ""
+        for page in pdf.pages:
+            extracted_text += page.extract_text() + "\n"
+
+    # Store extracted text in session
+    st.session_state['document_text'] = extracted_text
+
+    st.success("✅ PDF uploaded and text extracted successfully!")
+
+    # Display preview
+    with st.expander("📄 Preview Extracted Text"):
+        st.write(extracted_text[:1500] + "...")
+
+# Ask questions about the PDF
+if 'document_text' in st.session_state:
+    st.subheader("💬 Ask a question based on the uploaded PDF")
+    user_question = st.text_input("Enter your question")
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+    if user_question:
+        with st.spinner("Generating answer..."):
+            result = qa_pipeline({
+                "question": user_question,
+                "context": st.session_state['document_text']
+            })
+            st.markdown(f"**Answer:** {result['answer']}")