| import streamlit as st | |
| from transformers import pipeline | |
| # Load the conversational model from Hugging Face | |
| chatbot = pipeline('conversational', model='microsoft/DialoGPT-medium') | |
| # Initialize session state for storing the conversation | |
| if 'conversation' not in st.session_state: | |
| st.session_state.conversation = [] | |
| # Streamlit app layout | |
| st.title("Chatbot Application") | |
| user_input = st.text_input("You:", key="input") | |
| # if st.button("Send"): | |
| # if user_input: | |
| # # Append user input to the conversation | |
| # st.session_state.conversation.append({"role": "user", "content": user_input}) | |
| # # Generate response | |
| # response = chatbot(user_input) | |
| # bot_response = response[0]['generated_text'] | |
| # # Append bot response to the conversation | |
| # st.session_state.conversation.append({"role": "bot", "content": bot_response}) | |
| # # Display the conversation | |
| # for message in st.session_state.conversation: | |
| # if message["role"] == "user": | |
| # st.text_area("You:", value=message["content"], key=f"user_{message['content']}", height=50) | |
| # else: | |
| # st.text_area("Bot:", value=message["content"], key=f"bot_{message['content']}", height=50) | |