app.py

# Importing necessary modules and setting up environment variables
from langchain_openai import OpenAI
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import (ConversationBufferMemory,
                                                  ConversationSummaryMemory,
                                                  ConversationBufferWindowMemory
                                                  )
import tiktoken
from langchain.memory import ConversationTokenBufferMemory
import streamlit as st
from streamlit_chat import message
import os



# Initializing session state variables
if 'conversation' not in st.session_state:
    st.session_state['conversation'] =None
if 'messages' not in st.session_state:
    st.session_state['messages'] =[]
#if 'API_Key' not in st.session_state:
    #st.session_state['API_Key'] =''

# Setting up the page title and header
st.set_page_config(page_title="Chat GPT Clone", page_icon=":robot_face:")
st.markdown("<h1 style='text-align: center;'>How can I assist you? </h1>", unsafe_allow_html=True)
st.sidebar.title("😎")
#st.session_state['API_Key']= st.sidebar.text_input("What's your API key?",type="password")
summarise_button = st.sidebar.button("Summarise the conversation", key="summarise")
if summarise_button:
    summarise_placeholder = st.sidebar.write("Nice chatting with you my friend ❤️:\n\n"+st.session_state['conversation'].memory.buffer)

# Function to get response from the model
def getresponse(userInput):
    """
    This function takes user input and api key as arguments, and returns the model's response.
    """
    if st.session_state['conversation'] is None:
        llm = OpenAI(
            temperature=0,
            #openai_api_key from spaces secret
            #openai_api_key=api_key,
            model_name='gpt-3.5-turbo-instruct'
        )
        #Creating a new conversation chain
        #ConversationChain is a class that handles the conversation between the user and the model
        st.session_state['conversation'] = ConversationChain(
            llm=llm,
            verbose=True,
            memory=ConversationSummaryMemory(llm=llm)
        )
    #
    response=st.session_state['conversation'].predict(input=userInput)
    print(st.session_state['conversation'].memory.buffer)
    return response

# Function to clear the conversation
def clear():
    """
    This function clears the conversation and messages from the session state.
    """
    if st.session_state['conversation']:
        st.session_state['conversation'].memory.clear()
        st.session_state['messages'] =[]


# -- GUI --

# Setting up containers for user input and model response
response_container = st.container()
container = st.container()



# Setting up the user input form
with container:
    with st.form(key='my_form', clear_on_submit=True):
        user_input = st.text_area("Your question goes here:", key='input', height=100)
        col1, col2 = st.columns(2)
        with col1:
            submit_button = st.form_submit_button(label='Send')
        with col2:
            clear_button = st.form_submit_button(label="Clear")

        # Handling form submission
        if submit_button:
            st.session_state['messages'].append(user_input)
            model_response=getresponse(user_input)
            st.session_state['messages'].append(model_response)

            # Displaying the conversation
            with response_container:
                for i in range(len(st.session_state['messages'])):
                        if (i % 2) == 0:
                            message(st.session_state['messages'][i], is_user=True, key=str(i) + '_user')
                        else:
                            message(st.session_state['messages'][i], key=str(i) + '_AI')

        # Handling clear button click
        if clear_button:
            clear()