import streamlit as st
from transformers import pipeline

# Initialize the text generation pipeline with the conversational model
pipe = pipeline("text2text-generation", model="facebook/blenderbot-400M-distill")

# Initialize conversation history
if 'conversation_history' not in st.session_state:
    st.session_state.conversation_history = ""

def converse(user_message):
    # Access and update conversation history
    st.session_state.conversation_history += f"User: {user_message}\n"
    
    # Generate response
    response = pipe(st.session_state.conversation_history)[0]['generated_text']
    
    # Append the model's response to the conversation history
    st.session_state.conversation_history += f"Bot: {response}\n"
    
    return response

# Streamlit app
st.title("Conversational AI with Streamlit")

user_message = st.text_input("You:", "")

if st.button("Send"):
    if user_message:
        bot_response = converse(user_message)
        st.text_area("Bot Response", value=bot_response, height=100, max_chars=None, key="response")
    else:
        st.error("Please enter a message before sending.")

if st.button("Show Conversation History"):
        st.text_area("Conversation History", value=st.session_state.conversation_history, height=300, max_chars=None, key="history")