app.py

from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from langchain.prompts import (
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
    ChatPromptTemplate,
    MessagesPlaceholder
)
import streamlit as st
from streamlit_chat import message

import os
from dotenv import find_dotenv, load_dotenv
load_dotenv(find_dotenv())

# Get from utils.py
from utils import *

st.subheader("Chatbot with Langchain, ChatGPT, Pinecone, and Streamlit")

# Make sure to create a session -> responses and request

if 'responses' not in st.session_state:
    st.session_state['responses'] = ["How can I assist you?"]

if 'requests' not in st.session_state:
    st.session_state['requests'] = []

llm = ChatOpenAI(model_name="gpt-3.5-turbo", openai_api_key=os.environ["OPENAI_API_KEY"])

if 'buffer_memory' not in st.session_state:
            #save the memory/chat history
            # sometimes it can exceed the token limit -> can use conversation summary memory ->
            # Conversation Buffer Window Memory: Maintain the conversation within a window (3 last interactions)
            st.session_state.buffer_memory=ConversationBufferWindowMemory(k=3,return_messages=True)

# What the system should do
system_msg_template = SystemMessagePromptTemplate.from_template(template="""Answer the question as truthfully as possible using the provided context, 
and if the answer is not contained within the text below, say 'I don't know'""")

# What to do with input variable
human_msg_template = HumanMessagePromptTemplate.from_template(template="{input}")

# We have to pass a variable name "history" -> 
prompt_template = ChatPromptTemplate.from_messages([system_msg_template, MessagesPlaceholder(variable_name="history"), human_msg_template])

# Chain: Putting multiple components together
conversation = ConversationChain(memory=st.session_state.buffer_memory, prompt=prompt_template, llm=llm, verbose=True)


# STREAMLIT

# Container for chat history
response_container = st.container()
# Container for text box
textcontainer = st.container()


with textcontainer:
    query = st.text_input("Query: ", key="input")
    if query:
        with st.spinner("typing..."):
            conversation_string = get_conversation_string()
            # st.code(conversation_string)
            refined_query = query_refiner(conversation_string, query)
            st.subheader("Refined Query:")
            st.write(refined_query)
            context = find_match(refined_query)
            # print(context)  
            response = conversation.predict(input=f"Context:\n {context} \n\n Query:\n{query}")
        
        # Adds response and requests to history (session)
        st.session_state.requests.append(query)
        st.session_state.responses.append(response) 
with response_container:
    if st.session_state['responses']:

        for i in range(len(st.session_state['responses'])):
            message(st.session_state['responses'][i],key=str(i))
            if i < len(st.session_state['requests']):
                message(st.session_state["requests"][i], is_user=True,key=str(i)+ '_user')