app.py

import os
import streamlit as st
from langchain_community.llms import Replicate
from langchain.chains import ConversationChain
from langchain.memory import ConversationSummaryMemory

# Setting page title and header
st.set_page_config(page_title="Chat GPT Clone", page_icon="🤖")
st.markdown("<h1 style='text-align: center;'>How can I assist you? </h1>", unsafe_allow_html=True)

# Initialize session state if not already done
if "messages" not in st.session_state:
    st.session_state.messages = [{"role": "assistant", "content": "Hey there! Feel free to ask me anything. What's on your mind?"}]
if "conversation" not in st.session_state:
    st.session_state["conversation"] = None
if "REPLICATE_API_TOKEN" not in st.session_state:
    st.session_state["REPLICATE_API_TOKEN"] = ""

# Sidebar for API key input and summarisation
with st.sidebar:
    # Get Replicate API Token
    replicate_api = st.secrets.get('REPLICATE_API_TOKEN', '')
    input_token = st.text_input('Enter your Replicate API token:', type='password')
    
    if input_token:
        replicate_api = input_token

# Validate the token
    if not replicate_api:
        st.warning('No API Key provided! You may get one by signing up at Replicate.')
    elif not (replicate_api.startswith('r8_') and len(replicate_api) == 40):
        st.error('Invalid API Key! Please double-check your token.')
    else:
        st.success('Replicate API token is valid!')

    # Set environment variable for Replicate
    os.environ['REPLICATE_API_TOKEN'] = replicate_api
    st.session_state['REPLICATE_API_TOKEN'] = replicate_api

    # Summarise button logic
    summarise_button = st.button("Summarise the conversation", key="summarise_button")
    if summarise_button:
        if st.session_state.get('conversation') and st.session_state['conversation'].memory:
            summary = st.session_state['conversation'].memory.buffer
            st.session_state['summary'] = summary
            st.write("Summary:\n\n" + summary)
        else:
            st.write("No conversation to summarise.")

    # Add download button for summary
    if 'summary' in st.session_state:
        st.download_button(
            label="Download Summary",
            data=st.session_state['summary'],
            file_name="conversation_summary.txt",
            mime="text/plain",
        )

# Ensure the API token is valid before proceeding
if st.session_state['REPLICATE_API_TOKEN']:
    try:
        # Initialize conversation state if not done already
        if 'conversation' not in st.session_state or st.session_state['conversation'] is None:
            # Ensure the API token is passed explicitly to Replicate
            llm = Replicate(
                model="meta/meta-llama-3-8b-instruct",
                model_kwargs={
                    "temperature": 0.01,
                    "top_p": 0.9,
                    "max_length": 128,
                },
                replicate_api_token=st.session_state['REPLICATE_API_TOKEN']  # Explicitly passing the token
            )
            st.session_state['conversation'] = ConversationChain(
                llm=llm,
                verbose=True,
                memory=ConversationSummaryMemory(llm=llm),
            )
    except Exception as e:
        st.error(f"Failed to initialize the model: {e}")
else:
    st.warning("Please provide a valid Replicate API token to start the conversation.")

# Function to get the response from the LLM
def get_response(user_input):
    response_dict = st.session_state['conversation'].invoke(input=user_input)
    return response_dict.get("response", "No response generated.")

# Function to get user input
def get_text():
    return st.chat_input("Say something!")

# Display the chat history (including the initial message)
if st.session_state.messages:
    for msg in st.session_state.messages:
        with st.chat_message(msg["role"]):
            st.markdown(msg["content"])

# Handle user input and conversation flow
if st.session_state['REPLICATE_API_TOKEN']:
    user_input = get_text()
    if user_input:
        try:
            # Append user input to messages
            st.session_state.messages.append({"role": "user", "content": user_input})
            with st.chat_message("user"):
                st.markdown(user_input)

            # Get response from LLM
            response = get_response(user_input)

            # Append LLM response to messages
            st.session_state.messages.append({"role": "LLM", "content": response})
            with st.chat_message("LLM"):
                st.markdown(response)

        except Exception as e:
            st.error(f"An error occurred: {e}")