""" TypeGPT
@author: NiansuhAI
@email: niansuhtech@gmail.com

"""
import numpy as np
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('API_KEY')  # Replace with your token
)

# Create supported models
model_links = {
    "Meta-Llama-3-70B-Instruct": "meta-llama/Meta-Llama-3-70B-Instruct",
    "C4ai-command-r-plus": "CohereForAI/c4ai-command-r-plus",
    "Zephyr-orpo-141b-A35b-v0.1": "HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",
    "Mixtral-8x7B-Instruct-v0.1": "mistralai/Mixtral-8x7B-Instruct-v0.1",
    "Nous-Hermes-2-Mixtral-8x7B-DPO": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
    "Meta-Llama-3-8B-Instruct": "meta-llama/Meta-Llama-3-8B-Instruct",
    "Yi-1.5-34B-Chat": "01-ai/Yi-1.5-34B-Chat",
    "Meta-Llama-2-70B-Chat-HF": "meta-llama/Llama-2-70b-chat-hf",
    "Meta-Llama-2-7B-Chat-HF": "meta-llama/Llama-2-7b-chat-hf",
    "Meta-Llama-2-13B-Chat-HF": "meta-llama/Llama-2-13b-chat-hf",
    "Mistral-7B-Instruct-v0.1": "mistralai/Mistral-7B-Instruct-v0.1",
    "Mistral-7B-Instruct-v0.2": "mistralai/Mistral-7B-Instruct-v0.2",
    "Mistral-7B-Instruct-v0.3": "mistralai/Mistral-7B-Instruct-v0.3",
    "Gemma-2-27b-it": "google/gemma-2-27b-it",
    "Gemma-2-9b-it": "google/gemma-2-9b-it",
    "Gemma-1.1-7b-it": "google/gemma-1.1-7b-it",
    "Gemma-1.1-2b-it": "google/gemma-1.1-2b-it",
    "Zephyr-7B-Beta": "HuggingFaceH4/zephyr-7b-beta",
    "Phi-3-mini-128k-instruct": "microsoft/Phi-3-mini-128k-instruct",
    "Phi-3-mini-4k-instruct": "microsoft/Phi-3-mini-4k-instruct",
}

#Random dog images for error message
random_dog = ["0f476473-2d8b-415e-b944-483768418a95.jpg",
              "1bd75c81-f1d7-4e55-9310-a27595fa8762.jpg",
              "526590d2-8817-4ff0-8c62-fdcba5306d02.jpg",
              "1326984c-39b0-492c-a773-f120d747a7e2.jpg",
              "42a98d03-5ed7-4b3b-af89-7c4876cb14c3.jpg",
              "8b3317ed-2083-42ac-a575-7ae45f9fdc0d.jpg",
              "ee17f54a-83ac-44a3-8a35-e89ff7153fb4.jpg",
              "027eef85-ccc1-4a66-8967-5d74f34c8bb4.jpg",
              "08f5398d-7f89-47da-a5cd-1ed74967dc1f.jpg",
              "0fd781ff-ec46-4bdc-a4e8-24f18bf07def.jpg",
              "0fb4aeee-f949-4c7b-a6d8-05bf0736bdd1.jpg",
              "6edac66e-c0de-4e69-a9d6-b2e6f6f9001b.jpg",
              "bfb9e165-c643-4993-9b3a-7e73571672a6.jpg"]



def reset_conversation():
    '''
    Resets Conversation
    '''
    st.session_state.conversation = []
    st.session_state.messages = []
    return None
    



# 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))


#Add reset button to clear conversation
st.sidebar.button('Reset Chat', on_click=reset_conversation) #Reset button


# Create model description
st.sidebar.write(f"You're now chatting with **{selected_model}**")
st.sidebar.markdown("*Generated content may be inaccurate or false.*")
st.sidebar.markdown("\n[TypeGPT](https://typegpt.net).")




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
    reset_conversation()



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


st.subheader(f'TypeGPT.net - {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"Hi I'm {selected_model}, ask me a question"):

    # 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=3000,
            )
    
            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})