Update app.py

app.py

@@ -5,14 +5,14 @@ import numpy as np
 from transformers import pipeline
 import requests
 
-# Initialize the summarization model from Hugging Face
+# Initialize the summarization and question-answering models from Hugging Face
 summarizer = pipeline("summarization")
+qa_pipeline = pipeline("question-answering")  # Adding the question-answering pipeline
 
 # Groq API Configuration
 API_KEY = "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"  # Replace with your actual Groq API key
 API_URL = "https://api.groq.com/openai/v1/chat/completions"  # Default endpoint for chat completions (check Groq docs)
 
-
 # PDF Processing Function
 def extract_text_from_pdf(pdf_file):
     # Read the file from the uploaded BytesIO object
@@ -60,7 +60,7 @@ def call_groq_api(input_text):
         return f"Error: {str(e)}"
 
 # Streamlit UI
-st.title("RAG-based PDF Summarizer")
+st.title("RAG-based PDF Summarizer and Q&A")
 
 # Upload PDF file
 pdf_file = st.file_uploader("Upload a PDF file", type="pdf")
@@ -82,7 +82,12 @@ if pdf_file:
         relevant_chunk = retrieve_relevant_chunk(query, index, sentences)
         st.write(f"Relevant Text: {relevant_chunk}")
         
-        # Summarize the relevant chunk
+        # Answer the question based on the relevant chunk
+        st.write("Answering the question...")
+        answer = qa_pipeline(question=query, context=relevant_chunk)  # Use question-answering pipeline
+        st.write(f"Answer: {answer['answer']}")
+
+        # Optionally, summarize the relevant chunk
         st.write("Summarizing...")
         summary = summarizer(relevant_chunk, max_length=150, min_length=50, do_sample=False)[0]['summary_text']
         st.write(f"Summary: {summary}")