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)