app.py

import streamlit as st
from streamlit_chat import message  # Ensure you have streamlit_chat installed
from langchain_openai import ChatOpenAI
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import ConversationSummaryMemory

# Initialize session state variables if they don't exist
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'] = ''

# Set page configuration
st.set_page_config(page_title="ChatMate: Your AI Conversation Partner", page_icon=":robot_face:")
st.markdown("<h1 style='text-align: center; color: navy;'>ChatMate</h1>", unsafe_allow_html=True)
st.markdown("<h4 style='text-align: center;'>Engage with a cutting-edge language model.</h4>", unsafe_allow_html=True)

# Sidebar for API Key Input
st.sidebar.title("API Key 🔑")
st.session_state['API_Key'] = st.sidebar.text_input(
    "Enter your OpenAI API Key:",
    type="password",
    help="Your API Key is safe with us and only used to power this conversation."
)

# Function to get a response from the model
def get_response(user_input, api_key):
    if st.session_state['conversation'] is None:
        llm = ChatOpenAI(
            temperature=0,
            openai_api_key=api_key,
            model_name='gpt-3.5-turbo'
        )
        st.session_state['conversation'] = ConversationChain(
            llm=llm,
            verbose=True,
            memory=ConversationSummaryMemory(llm=llm)
        )

    response = st.session_state['conversation'].predict(input=user_input)
    return response

# Main container for user input and displaying the conversation
response_container = st.container()
container = st.container()

with container:
    with st.form(key='my_form', clear_on_submit=True):
        user_input = st.text_area("Ask me anything.", height=100)
        submit_button = st.form_submit_button(label='Send')

        if submit_button:
            st.session_state['messages'].append(user_input)
            model_response = get_response(user_input, st.session_state['API_Key'])
            st.session_state['messages'].append(model_response)

            with response_container:
                for i, msg in enumerate(st.session_state['messages']):
                    # Alternate messages between user and AI
                    is_user = (i % 2) == 0
                    message(msg, is_user=is_user, key=f"msg_{i}")

# Optionally, you can add a feature to clear the conversation or summarize it using sidebar options