Update app.py

app.py

@@ -1,12 +1,11 @@
 import os
 import torch
 import streamlit as st
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.memory import ConversationBufferMemory
-from langchain import PromptTemplate, LLMChain
-from langchain.llms import HuggingFacePipeline
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_core.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain_community.llms import HuggingFacePipeline
 from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 from dotenv import load_dotenv
 from htmlTemplates import css
@@ -74,6 +73,7 @@ def get_text_files_content(folder):
 
 # Converting text to chunks
 def get_chunks(raw_text):
+    from langchain.text_splitter import CharacterTextSplitter
     text_splitter = CharacterTextSplitter(
         separator="\n",
         chunk_size=2000,
@@ -98,9 +98,9 @@ def handle_question(question, vectorstore=None):
         documents = vectorstore.similarity_search(question, k=3)
         context = "\n".join([doc.page_content for doc in documents])
         if context:
-            result_with_context = llm_context_chain.run(instruction=question, context=context)
+            result_with_context = llm_context_chain.invoke({"instruction": question, "context": context})
             return result_with_context
-    return llm_chain.run(instruction=question)
+    return llm_chain.invoke({"instruction": question})
 
 def main():
     st.write(css, unsafe_allow_html=True)
@@ -126,7 +126,7 @@ def main():
 
     st.sidebar.info(f"You have selected: {selected_subject}")
 
-    # Process data folder for question answering
+    # Process data folder for notes preview and question answering
     subject_folder_path = subject_folders[selected_subject]
     if os.path.exists(subject_folder_path):
         raw_text = get_text_files_content(subject_folder_path)
@@ -134,12 +134,17 @@ def main():
             text_chunks = get_chunks(raw_text)
             vectorstore = get_vectorstore(text_chunks)
             st.session_state.vectorstore = vectorstore
+
+            # Display preview of notes
+            st.subheader("Preview of Notes")
+            st.text_area("Preview Content:", value=raw_text[:2000], height=300, disabled=True)  # Show a snippet of the text
         else:
             st.error("No content found for the selected subject.")
     else:
         st.error(f"Folder not found for {selected_subject}.")
 
     # Chat interface
+    st.subheader("Ask Your Question")
     question = st.text_input("Ask a question about your selected subject:")
     if question:
         if st.session_state.vectorstore: