Update app.py

app.py

@@ -11,6 +11,7 @@ from langchain.document_transformers import Html2TextTransformer
 from langchain.callbacks import get_openai_callback
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.memory import ConversationBufferWindowMemory
+from streamlit_chat import message
 
 import asyncio
 from langchain.docstore.document import Document
@@ -19,13 +20,24 @@ import os
 from dotenv import load_dotenv
 
 
+
+if 'conversation' not in st.session_state:
+    st.session_state['conversation'] = None
+if 'messages' not in st.session_state:
+    st.session_state['messages'] = []
+
+
+
+st.sidebar.title("URL")
+input_url = st.sidebar.text_input("Inserisci url:")
+
 #loading openai api keys
 load_dotenv()
 
 
 st.title("🤖 Chat with your website 🤖")
 
-input_url = st.text_input("Inserisci url:")
+
 
 question = st.text_area("Chiedi pure:")
 
@@ -57,6 +69,10 @@ memory = ConversationBufferWindowMemory(
 )
 
 
+    
+
+
+
 if st.button("Invia", type="primary"):
     
     loader = AsyncHtmlLoader(input_url)
@@ -79,18 +95,20 @@ if st.button("Invia", type="primary"):
         embedding=openai_embeddings)
 
     retriever = vectordb.as_retriever(search_kwargs={"k": 3})
-    llm = ChatOpenAI(model_name = "gpt-3.5-turbo", temperature=0)
+
     relevant_docs = retriever.get_relevant_documents(question)
 
+    if st.session_state['conversation'] is None:
+        llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
 
-    chain = ConversationalRetrievalChain.from_llm(
-        llm,
-        chain_type='stuff',
-        retriever=retriever,
-        memory=memory,
-        combine_docs_chain_kwargs={"prompt": prompt},
-        verbose=True
-    )
+        st.session_state['conversation'] = ConversationalRetrievalChain.from_llm(
+            llm,
+            chain_type='stuff',
+            retriever=retriever,
+            memory=memory,
+            combine_docs_chain_kwargs={"prompt": prompt},
+            verbose=True
+        )
 
     #qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, chain_type_kwargs={"prompt": prompt})
 
@@ -99,8 +117,9 @@ if st.button("Invia", type="primary"):
 
     with get_openai_callback() as cb:
         #run the chain and generate response
-        response = chain(question)
+        response = st.session_state['conversation'](question)
         print(cb)
     
+    
     answer.write(response["answer"])
     st.write(relevant_docs)