Update app.py

app.py

@@ -4,11 +4,11 @@ from dotenv import load_dotenv
 import streamlit as st
 from PyPDF2 import PdfReader
 from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import FAISS
-from langchain.embeddings import SentenceTransformerEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import SentenceTransformerEmbeddings
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
-from groq import Groq
+from langchain.llms import OpenAI
 
 # Load environment variables
 load_dotenv()
@@ -46,24 +46,24 @@ def get_vectorstore(text_chunks):
         logging.info("Vectorstore created successfully.")
         return vectorstore
     except Exception as e:
-        logging.error(f"Error creating vectorstore: {e}")
-        st.error("An error occurred while creating the vectorstore.")
+        logging.error(f"Error creating vectorstore: {e}", exc_info=True)
+        st.error(f"An error occurred while creating the vectorstore: {e}")
         return None
 
 def get_conversation_chain(vectorstore):
     """Set up the conversational retrieval chain."""
     try:
-        client = Groq(api_key=os.getenv("GROQ_API_KEY"))
+        llm = OpenAI(model_name="text-davinci-003", temperature=0.5)
         conversation_chain = ConversationalRetrievalChain.from_llm(
-            llm=client.chat.completions.create(model="llama-3.3-70b-versatile", temperature=0.5),
+            llm=llm,
             retriever=vectorstore.as_retriever(),
             memory=ConversationBufferMemory(memory_key='chat_history', return_messages=True)
         )
         logging.info("Conversation chain created successfully.")
         return conversation_chain
     except Exception as e:
-        logging.error(f"Error creating conversation chain: {e}")
-        st.error("An error occurred while setting up the conversation chain.")
+        logging.error(f"Error creating conversation chain: {e}", exc_info=True)
+        st.error(f"An error occurred while setting up the conversation chain: {e}")
         return None
 
 def handle_userinput(user_question):