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 call_nvidia_api(history, system_message, max_tokens, temperature, top_p): """Calls the NVIDIA API to generate a response.""" messages = [{"role": "system", "content": system_message}] messages.extend([{"role": "user", "content": h[0]} for h in history]) payload = { "messages": messages, "temperature": temperature, "top_p": top_p, "max_tokens": max_tokens, "stream": False } print(f"Payload enviado: {json.dumps(payload, indent=2)}") 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() print(f"Payload recebido: {json.dumps(response_body, indent=2)}") 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 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="""
Explore the Capabilities of LLAMA 2 70B

Llama 2 is a large language AI model capable of generating text and code in response to prompts.

How to Use:

  1. Enter your message in the textbox to start a conversation or ask a question.
  2. Adjust the parameters in the "Additional Inputs" accordion to control the model's behavior.
  3. Use the buttons below the chatbot to submit your query, clear the chat history, or perform other actions.

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.

HF Created by: @artificialguybr (Twitter)

Discover more: artificialguy.com

""", submit_btn="Submit", clear_btn="🗑️ Clear", ) def clear_chat(): chat_history_state.value = [] chatbot.textbox.value = "" chatbot.clear() demo.launch()