Update app.py

app.py

@@ -5,15 +5,14 @@ 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
 
 # Set up Groq API key
 GROQ_API_KEY = os.getenv("GROQ_Api_Key")
 
 # List of GitHub PDF URLs
 PDF_URLS = [
-    "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",
+    "https://github.com/TahirSher/GenAI_Lawyers_Guide/blob/main/bi-partite.pdf",
     # Add more document links as needed
 ]
 
@@ -46,13 +45,23 @@ def load_or_create_vector_store(text_chunks):
 
 # 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=[
+    url = "https://api.groq.com/v1/chat/completions"  # Update with actual Groq API endpoint
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "messages": [
             {"role": "user", "content": f"{query}\n\nRelated information:\n{retrieved_text}"}
         ],
-        model="llama3-8b-8192",
-    )
-    return chat_completion.choices[0].message.content
+        "model": "llama3-8b-8192",
+    }
+    response = requests.post(url, headers=headers, json=payload)
+    if response.status_code == 200:
+        return response.json()["choices"][0]["message"]["content"]
+    else:
+        st.error("Failed to generate summary with Groq API")
+        return "Error in Groq API response"
 
 def user_input(user_question, vector_store):
     docs = vector_store.similarity_search(user_question)