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import streamlit as st
from langchain.chains import ConversationChain
from hugchat import hugchat
from hugchat.login import Login
st.set_page_config(page_title="HugChat - An LLM-powered Streamlit app")
st.title('🤗💬 HugChat App')
# Hugging Face Credentials
with st.sidebar:
st.header('Hugging Face Login')
hf_email = st.text_input('Enter E-mail:', type='password')
hf_pass = st.text_input('Enter password:', type='password')
# Store AI generated responses
if "messages" not in st.session_state.keys():
st.session_state.messages = [{"role": "assistant", "content": "I'm HugChat, How may I help you?"}]
# Display existing chat messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
# Function for generating LLM response
def generate_response(prompt, email, passwd):
# Hugging Face Login
sign = Login(email, passwd)
cookies = sign.login()
sign.saveCookies()
# Create ChatBot
chatbot = hugchat.ChatBot(cookies=cookies.get_dict())
chain = ConversationChain(llm=chatbot)
response = chain.run(input=prompt)
return response
# Prompt for user input and save
if prompt := st.chat_input():
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.write(prompt)
# If last message is not from assistant, we need to generate a new response
if st.session_state.messages[-1]["role"] != "assistant":
# Call LLM
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) |