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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/df2bee43-fb69-42b9-9ee5-f4eabbeaf3a8"
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):
messages = []
if system_message:
messages.append({"role": "system", "content": system_message})
messages.extend([{"role": "user", "content": msg[0]} for msg in history])
payload = {
"messages": messages,
"temperature": temperature,
"top_p": top_p,
"max_tokens": max_tokens,
"stream": False
}
print("Payload sendo enviado:", json.dumps(payload, indent=4)) # Debug: Imprime a payload
session = requests.Session()
response = session.post(INVOKE_URL, headers=headers, json=payload)
if 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() # Isso lançará uma exceção se o status não for 200
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
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)
# Gradio interface setup
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",
)
demo.launch()
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