import streamlit as st from streamlit_chat import message from streamlit_extras.colored_header import colored_header from streamlit_extras.add_vertical_space import add_vertical_space from hugchat import hugchat st.set_page_config(page_title="HugChat - An LLM-powered Streamlit app") # image = "0_AI4xFlr8mYASsylX.png" # Replace with the actual path to your image # st.image(image, caption="Robotic Llama", use_column_width=1) with st.sidebar: st.title('🤗💬 HugChat App') st.markdown(''' ## About This app is an LLM-powered chatbot built using: - [Streamlit]() - [HugChat]() - [OpenAssistant/oasst-sft-6-llama-30b-xor]() LLM model 💡 Note: No API key required! ''') add_vertical_space(5) st.write('Thanks Meta for LLAMA and hugging face- hugchat') if 'generated' not in st.session_state: st.session_state['generated'] = ["I'm HugChat, How may I help you?"] if 'past' not in st.session_state: st.session_state['past'] = ['Hi!'] input_container = st.container() colored_header(label='', description='', color_name='blue-70') response_container = st.container() # User input ## Function for taking user provided prompt as input def get_text(): input_text = st.text_input("You: ", "", key="input") return input_text ## Applying the user input box with input_container: user_input = get_text() # Response output ## Function for taking user prompt as input followed by producing AI generated responses def generate_response(prompt): chatbot = hugchat.ChatBot() response = chatbot.chat(prompt) return response ## Conditional display of AI generated responses as a function of user provided prompts with response_container: if user_input: response = generate_response(user_input) st.session_state.past.append(user_input) st.session_state.generated.append(response) if st.session_state['generated']: for i in range(len(st.session_state['generated'])): message(st.session_state['past'][i], is_user=True, key=str(i) + '_user') message(st.session_state['generated'][i], key=str(i))