import gradio as gr import aiohttp import os import json from collections import deque TOKEN = os.getenv("HUGGINGFACE_API_TOKEN") if not TOKEN: raise ValueError("API token is not set. Please set the HUGGINGFACE_API_TOKEN environment variable.") memory = deque(maxlen=10) async def respond( message, history: list[tuple[str, str]], system_message="AI Assistant Role", max_tokens=512, temperature=0.7, top_p=0.95, ): system_prefix = "System: 입력어의 언어(영어, 한국어, 중국어, 일본어 등)에 따라 동일한 언어로 답변하라." full_system_message = f"{system_prefix}{system_message}" memory.append((message, None)) messages = [{"role": "system", "content": full_system_message}] for val in memory: if val[0]: messages.append({"role": "user", "content": val[0]}) if val[1]: messages.append({"role": "assistant", "content": val[1]}) headers = { "Authorization": f"Bearer {TOKEN}", "Content-Type": "application/json" } payload = { "model": "mistralai/Mistral-Nemo-Instruct-2407", "max_tokens": max_tokens, "temperature": temperature, "top_p": top_p, "messages": messages, "stream": True } try: async with aiohttp.ClientSession() as session: async with session.post("https://api-inference.huggingface.co/v1/chat/completions", headers=headers, json=payload) as response: response_text = "" async for chunk in response.content: if chunk: try: chunk_data = chunk.decode('utf-8') response_json = json.loads(chunk_data) if "choices" in response_json: content = response_json["choices"][0]["message"]["content"] response_text += content yield response_text except json.JSONDecodeError: continue if not response_text: yield "I apologize, but I couldn't generate a response. Please try again." except Exception as e: yield f"An error occurred: {str(e)}" memory[-1] = (message, response_text) async def chat(message, history, system_message, max_tokens, temperature, top_p): response = "" async for chunk in respond(message, history, system_message, max_tokens, temperature, top_p): response = chunk yield response theme = "Nymbo/Nymbo_Theme" css = """ footer { visibility: hidden; } """ demo = gr.ChatInterface( css=css, fn=chat, theme=theme, additional_inputs=[ gr.Textbox(value="AI Assistant Role", label="System message"), gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), ] ) if __name__ == "__main__": demo.queue().launch(max_threads=20)