Update app.py

app.py

@@ -4,18 +4,6 @@ This script uses the LangChain Language Model API to answer questions using Retr
 and FAISS vector stores. It also uses the Mistral huggingface inference endpoint to 
 generate responses.
 """
-import os
-import streamlit as st
-from dotenv import load_dotenv
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import HuggingFaceBgeEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.chat_models import ChatOpenAI
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from htmlTemplates import css, bot_template, user_template
-from langchain.llms import HuggingFaceHub
 
 import os
 import streamlit as st
@@ -106,6 +94,18 @@ def main():
     if user_question:
         handle_userinput(user_question)
 
+    # Add Hugging Face token input
+    huggingface_token = st.text_input("Enter your Hugging Face API token:", type="password")
+    if huggingface_token:
+        os.environ["HUGGINGFACEHUB_API_TOKEN"] = huggingface_token
+
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        if not huggingface_token:
+            st.error("Please enter your Hugging Face API token to proceed.")
+        else:
+            handle_userinput(user_question)
+
     with st.sidebar:
         st.subheader("Your documents")
         pdf_docs = st.file_uploader(