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

# Load environment variables
load_dotenv()

# Initialize the OpenAI client for OpenAI models
openai.api_key = os.getenv("OPENAI_API_KEY")

# Hugging Face API client setup (if needed)
HF_API_KEY = os.getenv("HF_API_KEY")
huggingface_url = "https://api-inference.huggingface.co/models/"

# Create supported models dictionary
model_links = {
    "ChatGPT": "openai/gpt-4",
    "Meta-Llama-3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct",
    "Mistral-7B-Instruct-v0.1": "mistralai/Mistral-7B-Instruct-v0.1",
    # Add more models as needed
}

# Define functions to interact with OpenAI and Hugging Face
def query_openai(prompt, temperature):
    """Query OpenAI's GPT model."""
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        temperature=temperature,
    )
    return response.choices[0].message['content']

def query_huggingface(prompt, model, temperature):
    """Query Hugging Face's API."""
    headers = {"Authorization": f"Bearer {HF_API_KEY}"}
    payload = {
        "inputs": prompt,
        "parameters": {"temperature": temperature, "return_full_text": False},
    }
    response = requests.post(f"{huggingface_url}{model}", headers=headers, json=payload)
    return response.json()[0]['generated_text']

# Function to reset conversation
def reset_conversation():
    st.session_state.messages = []
    st.session_state.responses = []
    st.session_state.current_model = None

# Sidebar setup
st.sidebar.title("ChatBot Configuration")
selected_model = st.sidebar.selectbox("Select Model", list(model_links.keys()))
temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.5)

# Reset chat button
st.sidebar.button('Reset Chat', on_click=reset_conversation)

# Initialize session state variables
if 'messages' not in st.session_state:
    st.session_state.messages = []
if 'responses' not in st.session_state:
    st.session_state.responses = []
if 'current_model' not in st.session_state:
    st.session_state.current_model = selected_model

# Check if the model was changed
if st.session_state.current_model != selected_model:
    reset_conversation()
    st.session_state.current_model = selected_model

# Chat Interface
st.title(f"Chat with {selected_model}")

# Display previous chat messages
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("Ask me anything..."):
    st.session_state.messages.append({"role": "user", "content": prompt})

    with st.chat_message("assistant"):
        if selected_model == "ChatGPT":
            response = query_openai(prompt, temperature)
        else:
            response = query_huggingface(prompt, model_links[selected_model], temperature)
        st.markdown(response)

    st.session_state.responses.append(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}")