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import streamlit as st | |
from hugchat import hugchat | |
from hugchat.login import Login | |
import os | |
# App title | |
st.set_page_config(page_title="π€π¬ HugChat") | |
# Hugging Face Credentials | |
with st.sidebar: | |
st.title('π€π¬ HugChat') | |
hf_email = st.text_input('Enter E-mail:', type='password') | |
hf_pass = st.text_input('Enter password:', type='password') | |
if not (hf_email and hf_pass): | |
st.warning('Please enter your credentials!', icon='β οΈ') | |
else: | |
st.success('Proceed to entering your prompt message!', icon='π') | |
st.markdown('π Learn how to build this app in this [blog](https://blog.streamlit.io/how-to-build-an-llm-powered-chatbot-with-streamlit/)!') | |
# Store LLM generated responses | |
if "messages" not in st.session_state: | |
st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}] | |
# Display or clear chat messages | |
for message in st.session_state.messages: | |
with st.chat_message(message["role"]): | |
st.write(message["content"]) | |
def clear_chat_history(): | |
st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}] | |
st.sidebar.button('Clear Chat History', on_click=clear_chat_history) | |
# Function for generating LLM response | |
def generate_response(prompt_input, email, passwd): | |
# Hugging Face Login | |
sign = Login(email, passwd) | |
cookies = sign.login() | |
# Create ChatBot | |
chatbot = hugchat.ChatBot(cookies=cookies.get_dict()) | |
for dict_message in st.session_state.messages: | |
string_dialogue = "You are a helpful assistant." | |
if dict_message["role"] == "user": | |
string_dialogue += "User: " + dict_message["content"] + "\n\n" | |
else: | |
string_dialogue += "Assistant: " + dict_message["content"] + "\n\n" | |
prompt = f"{string_dialogue} {prompt_input} Assistant: " | |
return chatbot.chat(prompt) | |
# User-provided prompt | |
if prompt := st.chat_input(disabled=not (hf_email and hf_pass)): | |
st.session_state.messages.append({"role": "user", "content": prompt}) | |
with st.chat_message("user"): | |
st.write(prompt) | |
# Generate a new response if last message is not from assistant | |
if st.session_state.messages[-1]["role"] != "assistant": | |
with st.chat_message("assistant"): | |
with st.spinner("Thinking..."): | |
response = generate_response(prompt, hf_email, hf_pass) | |
st.write(response) | |
message = {"role": "assistant", "content": response} | |
st.session_state.messages.append(message) |