Update app.py

app.py

@@ -1,24 +1,20 @@
-
 import os
 from PyPDF2 import PdfReader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import FAISS
-from transformers import pipeline
+from langchain_community.vectorstores import FAISS
 import streamlit as st
 import requests
 from io import BytesIO
+import groq.client as client  # Ensure Groq client is properly installed
 
-# Use HF_TOKEN for Hugging Face model access
-#HF_TOKEN = os.getenv("HF_TOKEN")  # Ensure this environment variable is set
-
-# Replace `HF_TOKEN` in the pipeline instantiation directly with your token string (for testing purposes only).
-pipe = pipeline("text-generation", model="meta-llama/Llama-Guard-3-8B-INT8", use_auth_token="HF_TOKEN")
-
+# Set up Groq API key
+GROQ_API_KEY = os.getenv("Groq_Api_Key")
+client.configure(api_key=GROQ_API_KEY)
 
 # List of GitHub PDF URLs
 PDF_URLS = [
-    "https://github.com/TahirSher/GenAI_Lawyers_Guide/blob/main/bi%20pat%20graphs.pdf",
     "https://github.com/TahirSher/GenAI_Lawyers_Guide/blob/main/bi-partite.pdf",
+    "https://github.com/TahirSher/GenAI_Lawyers_Guide/blob/main/bi%20pat%20graphs.pdf",
     # Add more document links as needed
 ]
 
@@ -49,19 +45,20 @@ def load_or_create_vector_store(text_chunks):
     vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
     return vector_store
 
-def generate_answer(user_question, context_text):
-    # Format the input message for the pipeline
-    messages = [
-        {"role": "user", "content": f"Context: {context_text}\nQuestion: {user_question}"}
-    ]
-    # Generate response using the pipeline
-    response = pipe(messages, max_length=250, do_sample=True)
-    return response[0]['generated_text'][:250]  # Limit response to 250 characters
+# Call Groq API for generating summary based on the query and retrieved text
+def generate_summary_with_groq(query, retrieved_text):
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {"role": "user", "content": f"{query}\n\nRelated information:\n{retrieved_text}"}
+        ],
+        model="llama3-8b-8192",
+    )
+    return chat_completion.choices[0].message.content
 
 def user_input(user_question, vector_store):
     docs = vector_store.similarity_search(user_question)
     context_text = " ".join([doc.page_content for doc in docs])
-    return generate_answer(user_question, context_text)
+    return generate_summary_with_groq(user_question, context_text)
 
 def main():
     st.set_page_config(page_title="RAG-based PDF Chat", layout="centered", page_icon="📄")