app.py

import streamlit as st
from streamlit_chat import message
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import ConversationSummaryMemory

# Step 1: Set up Google API key
google_api_key = st.secrets["google_api_key"]

# Step 2: Initialize 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'] = google_api_key  # Use the Google API key from secrets

# Step 3: Build the Streamlit UI
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)

# Sidebar for API key input and model selection
st.sidebar.title("To start chatting,")
st.session_state['API_Key'] = st.sidebar.text_input("Enter your Google API key below", type="password", key="google_api_key_input")

# Support multiple models
st.sidebar.markdown("### Select Model:")
model_name = st.sidebar.selectbox(
    "Choose a model:",
    ["gemini-1.5-flash", "gemini-1.5-pro"],
    index=0
)

# Add instructions for users
if 'welcome' not in st.session_state:
    st.session_state['welcome'] = True

if st.session_state['welcome']:
    st.sidebar.info(
        "### Instructions:\n"
        "1. Enter your Google API key (optional if pre-configured).\n"
        "2. Choose a model from the dropdown menu.\n"
        "3. Type your question in the text area and click 'Send'.\n"
        "4. Click 'Summarise the conversation' to view a summary of your chat."
    )
    st.session_state['welcome'] = False

# Summarization button
summarise_button = st.sidebar.button("Summarise the conversation", key="summarise")

if summarise_button:
    if st.session_state['conversation'] is not None:  # Check if conversation is initialized
        # Generate summary from conversation buffer
        summary = str(st.session_state['conversation'].memory.buffer)

        # Split summary into sentences
        summary_sentences = summary.strip().split(". ")

        # Exclude the first two sentences
        filtered_summary = summary_sentences[2:]

        # Display the summary at the center
        st.markdown("---")  # Separator line
        st.markdown("<h3 style='text-align: center;'>Summary of Conversation</h3>", unsafe_allow_html=True)

        summary_container = st.container()
        with summary_container:
            for i, line in enumerate(filtered_summary):
                if line:  # Avoid blank lines
                    message(line, is_user=False, key=f"summary_{i}")
    else:
        st.sidebar.write("No conversation history to summarize.")

# Step 4: Define the getresponse function using Google's Gemini
def getresponse(userInput, api_key, model_name):
    try:
        if st.session_state['conversation'] is None:
            # Initialize the Google generative model
            with st.spinner("Setting up the conversation..."):
                chat = ChatGoogleGenerativeAI(
                    model=model_name,
                    google_api_key=api_key
                )
                st.session_state['conversation'] = ConversationChain(
                    llm=chat,
                    verbose=True,
                    memory=ConversationSummaryMemory(llm=chat)
                )

        # Get response with loading indicator
        with st.spinner("Generating response..."):
            response = st.session_state['conversation'].predict(input=userInput)
        return response

    except Exception as e:  # Generic exception handler
        st.error(f"Error: {str(e)}")
        return "Sorry, there was an issue processing your request."

# Step 5: Creating the Chat UI
response_container = st.container()
container = st.container()

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)
        submit_button = st.form_submit_button(label='Send')
        if submit_button:
            if user_input.strip():  # Check for empty input
                st.session_state['messages'].append(user_input)
                model_response = getresponse(user_input, st.session_state['API_Key'], model_name)
                st.session_state['messages'].append(model_response)
            else:
                st.warning("Please enter a message before sending.")

# Display chat messages
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')