import numpy as np
import streamlit as st
from openai import OpenAI
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

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

# Define model links
model_links = {
    "Meta-Llama-3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct",
    "Meta-Llama-3.1-8B-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct",
    # Add more models as needed
}

# Function to reset conversation
def reset_conversation():
    st.session_state.conversation = []
    st.session_state.messages = []

# Sidebar setup
models = [key for key in model_links.keys()]
selected_model = st.sidebar.selectbox("Select Model", models)
temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, 0.5)
st.sidebar.button('Reset Chat', on_click=reset_conversation)

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).")

# Manage session state
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.session_state.prev_option = selected_model
    reset_conversation()

# Model repository id
repo_id = model_links[selected_model]

# Main chat interface
st.subheader(f'TypeGPT.net - {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"):
    with st.chat_message("user"):
        st.markdown(prompt)
    st.session_state.messages.append({"role": "user", "content": prompt})

    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,
                stream=True,
                max_tokens=3000,
            )
            response = st.write_stream(stream)
        except Exception as e:
            response = "😵‍💫 Looks like something went wrong! Please try again later."
            st.write(response)

    st.session_state.messages.append({"role": "assistant", "content": response})




# import gradio as gr
# from huggingface_hub import InferenceClient

# """
# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
# """
# client = InferenceClient("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

# """
# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
# """
# demo = gr.ChatInterface(
#     respond,
#     additional_inputs=[
#         gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
#         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
#         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
#         gr.Slider(
#             minimum=0.1,
#             maximum=1.0,
#             value=0.95,
#             step=0.05,
#             label="Top-p (nucleus sampling)",
#         ),
#     ],
# )


# if __name__ == "__main__":
#     demo.launch()
#####################################
# import gradio as gr

# gr.load("models/meta-llama/Meta-Llama-3.1-70B-Instruct").launch()
########################################
# import streamlit as st
# from transformers import AutoTokenizer, AutoModelForCausalLM

# # Load model directly
# tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
# model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")

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

# # Display chat history
# for chat in st.session_state.chat_history:
#     st.write(f"User: {chat['user']}")
#     st.write(f"Response: {chat['response']}")

# # Get user input
# user_input = st.text_input("Enter your message:")

# # Generate response
# if st.button("Send"):
#     inputs = tokenizer(user_input, return_tensors="pt")
#     outputs = model.generate(**inputs)
#     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
#     st.session_state.chat_history.append({"user": user_input, "response": response})
#     st.write(f"Response: {response}")