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import streamlit as st
from hugchat import hugchat
from hugchat.login import Login
from huggingface_hub import InferenceClient
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# App title
st.set_page_config(page_title="🤗💬 HugChat")
# Hugging Face Credentials
with st.sidebar:
st.title('🤗💬 HugChat')
if ('EMAIL' in st.secrets) and ('PASS' in st.secrets):
st.success('HuggingFace Login credentials already provided!', icon='✅')
hf_email = st.secrets['EMAIL']
hf_pass = st.secrets['PASS']
else:
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='⚠️')
else:
st.success('Proceed to entering your prompt message!', icon='👉')
st.markdown('📖 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.keys():
st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]
# Display 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(messages, email, passwd):
for message in client.chat_completion(
messages,
max_tokens=500,
stream=True,
temperature=0.7,
top_p=0.9,
):
token = message.choices[0].delta.content
response += token
yield response
# def generate_response(prompt_input, email, passwd):
# # Hugging Face Login
# cookie_path_dir = "./cookies/"
# sign = Login(email, passwd)
# cookies = sign.login(cookie_dir_path=cookie_path_dir, save_cookies=True)
# # Create ChatBot
# chatbot = hugchat.ChatBot(cookies=cookies.get_dict())
# return chatbot.chat(prompt_input)
# # Function for generating LLM response based on "HuggingFaceH4/zephyr-7b-beta"
# def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p,):
# messages = [{"role": "system", "content": system_message}]
# for val in history:
# if val[0]:
# messages.append({"role": "user", "content": val[0]})
# if val[1]:
# messages.append({"role": "assistant", "content": val[1]})
# messages.append({"role": "user", "content": message})
# response = ""
# for message in client.chat_completion(
# messages,
# max_tokens=max_tokens,
# stream=True,
# temperature=temperature,
# top_p=top_p,
# ):
# token = message.choices[0].delta.content
# response += token
# yield response
# 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) |