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 call_nvidia_api(api_history, max_tokens, temperature, top_p): """Calls the NVIDIA API to generate a response.""" payload = { "messages": api_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.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, api_history, system_message, max_tokens_val, temperature_val, top_p_val): """Submits the user message and updates both histories.""" # Update Gradio history chat_history.append([message, ""]) # Update API history api_history.append({"role": "user", "content": message}) # Call NVIDIA API assistant_message = call_nvidia_api(api_history, max_tokens_val, temperature_val, top_p_val) # Update Gradio history with response chat_history[-1][1] = assistant_message # Update API history with response api_history.append({"role": "assistant", "content": assistant_message}) return assistant_message, chat_history, api_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="""
Llama 2 is a large language AI model capable of generating text and code in response to prompts.
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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.
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""", submit_btn="Submit", clear_btn="🗑️ Clear", ) def clear_chat(): chat_history_state.value = [] chatbot.textbox.value = "" chatbot.clear() demo.launch()