Update app.py

app.py

@@ -2,8 +2,11 @@ import streamlit as st
 from dotenv import load_dotenv
 from PyPDF2 import PdfReader
 from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import OpenAIEmbeddings
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
 from langchain.vectorstores import FAISS
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationRetrievalChain
+from langchain.llms import ChatOpenAI
 
 def get_html(html):
     text = ""
@@ -19,17 +22,36 @@ def get_chunk_text(raw_text):
     return chunks
 
 def get_vector_store(text_chunks):
-    embeddings = OpenAIEmbeddings
+    # embeddings = OpenAIEmbeddings()
+    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
     vector_store = FAISS.from_texts(texts=text_chunks,embedding = embeddings)
     return vector_store
+
+def get_conversation_chain(vector_store):
+    llm = ChatOpenAI()
+    memory = ConversationBufferMemory(memory_key="chat_history",return_messages=True)
+    conversation_chain = ConversationRetrievalChain.from_llm(
+        llm = llm,
+        retriever = vector_store.as_retriever(),
+        memory = memory
+    )
+    return conversation_chain
     
+def handle_input(user_input):
+    response = st.session_state.conversation({"question":user_input})
+    st.write(response)
             
     
 def main():
     load_dotenv()
     st.set_page_config(page_title="Reads your html",page_icon=":books:")
+
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = None
     st.header("Get your best Element")
-    st.text_input("Pass your Element with its information")
+    user_input = st.text_input("Pass your Element with its information")
+    if user_input:
+        handle_input(user_input)
 
     with st.sidebar:
         st.subheader("your html")
@@ -46,7 +68,12 @@ def main():
                 
 
                 #create vector store
-                vectors = get_vector_store(text_chunks)
+                vector_store = get_vector_store(text_chunks)
+
+                #create conversation chain
+                st.session_state.conversation = get_conversation_chain(vector_store)
+                
+
 
 if __name__ == '__main__':
     main()