import streamlit as st
from openai import OpenAI
import os
import sys
from dotenv import load_dotenv, dotenv_values

load_dotenv()

# initialize the client
client = OpenAI(
  base_url="https://api-inference.huggingface.co/v1",
  api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN')#"hf_xxx" # Replace with your token
) 

#Create supported models
model_links ={
    "Meta-Llama-3.1-8B": "meta-llama/Meta-Llama-3.1-8B-Instruct",
    "Mistral-7B-Instruct-v0.3": "mistralai/Mistral-7B-Instruct-v0.3",
    "Gemma-7b-it": "google/gemma-7b-it",
}

#Pull info about the model to display
model_info ={
    "Meta-Llama-3.1-8B":
    {'description':"""The Llama (3.1) model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
        \nIt was created by the [**Meta's AI**](https://llama.meta.com/) team and has over  **8 billion parameters.** \n""",
    'logo':'llama_logo.gif'},
    "Mistral-7B-Instruct-v0.3":
        {'description':"""The Mistral-7B-Instruct-v0.3 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.3.\n \
            \nIt was created by the [**Mistral AI**](https://mistral.ai/news/announcing-mistral-7b/) team as has over  **7 billion parameters.** \n""",
        'logo':'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'},
    "Gemma-7b-it":
    {'description':"""Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models.\n \
    \nThey are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. \n"""},
}


#Random dog images for error message
random_dog = ["BlueLogoBox.jpg"]

# Define the available models
models =[key for key in model_links.keys()]

# Create the sidebar with the dropdown for model selection
selected_model = st.sidebar.selectbox("Select Model", models)

#Create a temperature slider
temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))

# Create model description
st.sidebar.write(f"You're now chatting with **{selected_model}**")
st.sidebar.markdown(model_info[selected_model]['description'])
st.sidebar.image(model_info[selected_model]['logo'])
st.sidebar.markdown("*Generated content may be inaccurate or false.*")

if "prev_option" not in st.session_state:
    st.session_state.prev_option = selected_model

if st.session_state.prev_option != selected_model:
    st.session_state.messages = []
    st.write(f"Changed to {selected_model}")
    st.session_state.prev_option = selected_model

#Pull in the model we want to use
repo_id = model_links[selected_model]

st.header(f'Liahona.AI')
st.markdown(f'_powered_ by ***:violet[{selected_model}]***')
# st.title(f'ChatBot Using {selected_model}')

# Set a default model
if selected_model not in st.session_state:
    st.session_state[selected_model] = model_links[selected_model] 

# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []


# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# Accept user input
if prompt := st.chat_input(f"Type message here..."):

    # Display user message in chat message container
    with st.chat_message("user"):
        st.markdown(prompt)
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})


    # Display assistant response in chat message container
    with st.chat_message("assistant"):

        try:
            stream = client.chat.completions.create(
                model=model_links[selected_model],
                messages=[
                    {"role": m["role"], "content": m["content"]}
                    for m in st.session_state.messages
                ],
                temperature=temp_values,#0.5,
                stream=True,
                max_tokens=4000,
            )
    
            response = st.write_stream(stream)

        except Exception as e:
            # st.empty()
            response = "😵‍💫 Looks like someone unplugged something!\
                    \n Either the model space is being updated or something is down.\
                    \n\
                    \n Try again later. \
                    \n\
                    \n Here's a random pic of a 🐶:"
            st.write(response)
            random_dog_pick = 'https://random.dog/'+ random_dog[np.random.randint(len(random_dog))]
            st.image(random_dog_pick)
            st.write("This was the error message:")
            st.write(e)
        
    st.session_state.messages.append({"role": "assistant", "content": response})