import gradio as gr
import requests
import json
import os

# API and environment variables
API_KEY = os.getenv('API_KEY')
INVOKE_URL = "https://api.nvcf.nvidia.com/v2/nvcf/pexec/functions/0e349b44-440a-44e1-93e9-abe8dcb27158"
FETCH_URL_FORMAT = "https://api.nvcf.nvidia.com/v2/nvcf/pexec/status/"
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Accept": "application/json",
    "Content-Type": "application/json",
}

# Base system message
BASE_SYSTEM_MESSAGE = "I carefully provide accurate, factual, thoughtful, nuanced answers and am brilliant at reasoning."

def clear_chat():
    """Clears the chat history and message state."""
    print("Clearing chat...")
    chat_history_state.value = []
    chatbot.textbox.value = ""

def user(message, history):
    """Updates the chat history with the user message."""
    print(f"User message: {message}")
    history = history or []
    history.append({"role": "user", "content": message})
    return history

def call_nvidia_api(history, system_message, max_tokens, temperature, top_p):
    """Calls the NVIDIA API to generate a response."""
    # Transforma o histórico de listas de listas para o formato esperado pela API
    messages = [{"role": "system", "content": system_message}]
    for msg in history:
        messages.append({"role": "user", "content": msg[0]})
        if msg[1]:  # Garante que não adicionamos mensagens vazias do assistente
            messages.append({"role": "assistant", "content": msg[1]})

    payload = {
        "messages": messages,
        "temperature": temperature,
        "top_p": top_p,
        "max_tokens": max_tokens,
        "stream": False
    }
    session = requests.Session()
    response = session.post(INVOKE_URL, headers=headers, json=payload)
    while response.status_code == 202:
        request_id = response.headers.get("NVCF-REQID")
        fetch_url = FETCH_URL_FORMAT + request_id
        response = session.get(fetch_url, headers=headers)
        response.raise_for_status()
    response_body = response.json()
    if response_body.get("choices"):
        assistant_message = response_body["choices"][0]["message"]["content"]
        history.append(["", assistant_message])  # Adiciona a nova mensagem do assistente ao histórico
    return history

def chatbot_submit(message, chat_history, system_message, max_tokens_val, temperature_val, top_p_val):
    """Submits the user message to the chatbot and updates the chat history."""
    # Adiciona a mensagem do usuário ao histórico
    updated_history = chat_history + [[message, ""]]

    # Chama a API da NVIDIA para gerar uma resposta
    updated_history = call_nvidia_api(updated_history, system_message, max_tokens_val, temperature_val, top_p_val)

    # A última entrada no histórico atualizado contém a última mensagem do usuário e a resposta do assistente
    if updated_history and updated_history[-1][1]:
        assistant_message = updated_history[-1][1]
    else:
        assistant_message = "Desculpe, ocorreu um erro ao gerar a resposta."

    return assistant_message, updated_history

chat_history_state = gr.State([])
system_msg = gr.Textbox(BASE_SYSTEM_MESSAGE, label="System Message", placeholder="System prompt.", lines=5)
max_tokens = gr.Slider(20, 1024, label="Max Tokens", step=20, value=1024)
temperature = gr.Slider(0.0, 1.0, label="Temperature", step=0.1, value=0.2)
top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.7)
with gr.Blocks() as demo:
    chat_history_state = gr.State([])
    chatbot = gr.ChatInterface(
        fn=chatbot_submit,
        additional_inputs=[system_msg, max_tokens, temperature, top_p],
        title="LLAMA 70B Free Demo",
        description="""
            <div style="text-align: center; font-size: 1.5em; margin-bottom: 20px;">
                <strong>Explore the Capabilities of LLAMA 2 70B</strong>
            </div>
            <p>Llama 2 is a large language AI model capable of generating text and code in response to prompts.</p>
            <p><strong>How to Use:</strong></p>
            <ol>
                <li>Enter your <strong>message</strong> in the textbox to start a conversation or ask a question.</li>
                <li>Adjust the parameters in the "Additional Inputs" accordion to control the model's behavior.</li>
                <li>Use the buttons below the chatbot to submit your query, clear the chat history, or perform other actions.</li>
            </ol>
            <p><strong>Powered by NVIDIA's cutting-edge AI API, LLAMA 2 70B offers an unparalleled opportunity to interact with an AI model of exceptional conversational ability, accessible to everyone at no cost.</strong></p>
            <p><strong>HF Created by:</strong> @artificialguybr (<a href="https://twitter.com/artificialguybr">Twitter</a>)</p>
            <p><strong>Discover more:</strong> <a href="https://artificialguy.com">artificialguy.com</a></p>
        """,
        submit_btn="Submit",
        clear_btn="🗑️ Clear",
    )

    def clear_chat():
        chat_history_state.value = []
        chatbot.textbox.value = ""

    chatbot.clear()
demo.launch()