Update app.py

app.py

@@ -1,4 +1,3 @@
-import os
 import streamlit as st
 from dotenv import load_dotenv
 from PyPDF2 import PdfReader
@@ -33,14 +32,14 @@ def get_text_chunks(text):
 
 def get_vectorstore(text_chunks):
     # embeddings = OpenAIEmbeddings()
-    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl", token=os.getenv("HUGGING_FACE_TOKEN"))
+    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
     vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
     return vectorstore
 
 
 def get_conversation_chain(vectorstore):
     # llm = ChatOpenAI()
-    llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512}, token=os.getenv("HUGGING_FACE_TOKEN"))
+    llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
 
     memory = ConversationBufferMemory(
         memory_key='chat_history', return_messages=True)
@@ -53,13 +52,7 @@ def get_conversation_chain(vectorstore):
 
 
 def handle_userinput(user_question):
-    conversation = st.session_state.conversation
-    if conversation is None:
-        st.error("Conversation not initialized. Please upload PDFs and process them.")
-        return
-
-    print("Conversation:", conversation)  # Add this line for debugging
-    response = conversation({'question': user_question})
+    response = st.session_state.conversation({'question': user_question})
     st.session_state.chat_history = response['chat_history']
 
     for i, message in enumerate(st.session_state.chat_history):
@@ -71,8 +64,6 @@ def handle_userinput(user_question):
                 "{{MSG}}", message.content), unsafe_allow_html=True)
 
 
-
-
 def main():
     load_dotenv()
     st.set_page_config(page_title="Chat with multiple PDFs",
@@ -110,4 +101,4 @@ def main():
 
 
 if __name__ == '__main__':
-    main()
+    main()