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| 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 langchain import PromptTemplate, HuggingFaceHub, LLMChain | |
| from hugchat import hugchat | |
| from hugchat.login import Login | |
| from time import sleep | |
| from hugchat_api import HuggingChat | |
| import os | |
| from dotenv import load_dotenv | |
| # load the Environment Variables. | |
| load_dotenv() | |
| st.set_page_config(page_title="OpenAssistant Powered Chat App") | |
| # Sidebar contents | |
| with st.sidebar: | |
| st.title('🤗💬 HuggingChat App') | |
| st.markdown(''' | |
| - [OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5](https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5) LLM model | |
| ''') | |
| add_vertical_space(3) | |
| #st.write('Made with ❤️ by [Prompt Engineer](https://youtube.com/@engineerprompt)') | |
| st.header("Your Personal Assistant 💬") | |
| def main(): | |
| # Generate empty lists for generated and user. | |
| ## Assistant Response | |
| if 'generated' not in st.session_state: | |
| st.session_state['generated'] = ["I'm Assistant, How may I help you?"] | |
| ## user question | |
| if 'user' not in st.session_state: | |
| st.session_state['user'] = ['Hi!'] | |
| # Layout of input/response containers | |
| response_container = st.container() | |
| colored_header(label='', description='', color_name='blue-30') | |
| input_container = st.container() | |
| # get user 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() | |
| def chain_setup(): | |
| template = """<|prompter|>{question}<|endoftext|> | |
| <|assistant|>""" | |
| prompt = PromptTemplate(template=template, input_variables=["question"]) | |
| llm=HuggingFaceHub(repo_id="OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5", model_kwargs={"max_new_tokens":1200}) | |
| llm_chain=LLMChain( | |
| llm=llm, | |
| prompt=prompt | |
| ) | |
| return llm_chain | |
| # generate response | |
| def generate_response(question, llm_chain): | |
| response = llm_chain.run(question) | |
| return response | |
| ## load LLM | |
| llm_chain = chain_setup() | |
| # main loop | |
| with response_container: | |
| if user_input: | |
| response = generate_response(user_input, llm_chain) | |
| st.session_state.user.append(user_input) | |
| st.session_state.generated.append(response) | |
| # # Initialize chat history | |
| # if "messages" not in st.session_state: | |
| # st.session_state.messages = [] | |
| if st.session_state['generated']: | |
| # for message in st.session_state.messages: | |
| # with st.chat_message(message["role"]): | |
| # st.markdown(message["content"]) | |
| for i in range(len(st.session_state['generated'])): | |
| message(st.session_state['user'][i], is_user=True, key=str(i) + '_user') | |
| message(st.session_state["generated"][i], key=str(i)) | |
| if __name__ == '__main__': | |
| main() | |