app.py

import gradio as gr
import requests
import os
import json

# Carrega a chave da API do ambiente ou define diretamente
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 = "I carefully provide accurate, factual, thoughtful, nuanced answers and am brilliant at reasoning."

def call_nvidia_api(history, system_message, max_tokens, temperature, top_p):
    """Chama a API da NVIDIA para gerar uma resposta."""
    # Prepara as mensagens, incluindo a mensagem do sistema se fornecida
    messages = []
    if system_message:
        messages.append({"role": "system", "content": system_message})
    messages.extend(history)

    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"]
        return assistant_message
    else:
        return "Desculpe, ocorreu um erro ao gerar a resposta."

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."""
    print("Updating chatbot...")

    # Adiciona a mensagem do usuário ao histórico para exibição
    chat_history.append([message, ""])

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

    # Atualiza o histórico com a resposta do assistente
    chat_history[-1][1] = assistant_message

    return assistant_message, chat_history

# Gradio interface setup
with gr.Blocks() as demo:
    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)
    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",
    )

demo.launch()