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, system_message=None): """Updates the chat history with the user message.""" print(f"User message: {message}") history = history or [] if system_message: history.append({"role": "system", "content": system_message}) history.append({"role": "user", "content": message}) return history def call_nvidia_api(history, max_tokens, temperature, top_p): """Calls the NVIDIA API to generate a response.""" payload = { "messages": history, "temperature": temperature, "top_p": top_p, "max_tokens": max_tokens, "stream": False } print(f"Payload enviado: {payload}") 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: {response_body}") if response_body["choices"]: assistant_message = response_body["choices"][0]["message"]["content"] history.append({"role": "assistant", "content": assistant_message}) 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.""" print("Updating chatbot...") # Atualiza o histórico do chat com a mensagem do usuário if not chat_history or (chat_history and chat_history[-1]["role"] != "user"): chat_history = user(message, chat_history, system_message) else: chat_history = user(message, chat_history) # Chama a API da NVIDIA para gerar uma resposta chat_history = call_nvidia_api(chat_history, max_tokens_val, temperature_val, top_p_val) # Extrai apenas a mensagem do assistente da resposta if chat_history and chat_history[-1]["role"] == "assistant": assistant_message = chat_history[-1]["content"] else: assistant_message = "Desculpe, ocorreu um erro ao gerar a resposta." return assistant_message 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="""
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)

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