import gradio as gr from huggingface_hub import InferenceClient import os MODELS = { "Zephyr 7B Beta": "HuggingFaceH4/zephyr-7b-beta", "DeepSeek Coder V2": "deepseek-ai/DeepSeek-Coder-V2-Instruct", "Meta Llama 3.1 8B": "meta-llama/Meta-Llama-3.1-8B-Instruct", "Meta-Llama 3.1 70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct", "Microsoft Phi-3-mini-4k": "microsoft/Phi-3-mini-4k-instruct", "Mixtral 8x7B": "mistralai/Mistral-7B-Instruct-v0.3", "Mixtral Nous-Hermes": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO", "Cohere Command R+": "CohereForAI/c4ai-command-r-plus", "Cohere Aya-23-35B": "CohereForAI/aya-23-35B" } def get_client(model_name): model_id = MODELS[model_name] hf_token = os.getenv("HF_TOKEN") if not hf_token: raise ValueError("HF_TOKEN environment variable is required") return InferenceClient(model_id, token=hf_token) def respond( message, chat_history, model_name, max_tokens, temperature, top_p, system_message, ): try: client = get_client(model_name) except ValueError as e: chat_history.append((message, str(e))) return chat_history messages = [{"role": "system", "content": system_message}] for human, assistant in chat_history: messages.append({"role": "user", "content": human}) messages.append({"role": "assistant", "content": assistant}) messages.append({"role": "user", "content": message}) try: if "Cohere" in model_name: # Cohere 모델을 위한 비스트리밍 처리 response = client.chat_completion( messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p, ) assistant_message = response.choices[0].message.content chat_history.append((message, assistant_message)) yield chat_history else: # 다른 모델들을 위한 스트리밍 처리 stream = client.chat_completion( messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p, stream=True, ) partial_message = "" for response in stream: if response.choices[0].delta.content is not None: partial_message += response.choices[0].delta.content if len(chat_history) > 0 and chat_history[-1][0] == message: chat_history[-1] = (message, partial_message) else: chat_history.append((message, partial_message)) yield chat_history except Exception as e: error_message = f"An error occurred: {str(e)}" chat_history.append((message, error_message)) yield chat_history def clear_conversation(): return [] with gr.Blocks() as demo: gr.Markdown("# Prompting AI Chatbot") gr.Markdown("언어모델별 프롬프트 테스트 챗봇입니다.") with gr.Row(): with gr.Column(scale=1): model_name = gr.Radio( choices=list(MODELS.keys()), label="Language Model", value="Zephyr 7B Beta" ) max_tokens = gr.Slider(minimum=0, maximum=2000, value=500, step=100, label="Max Tokens") temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.05, label="Temperature") top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p") system_message = gr.Textbox( value="""반드시 한글로 답변할 것. 너는 최고의 비서이다. 내가 요구하는것들을 최대한 자세하고 정확하게 답변하라. """, label="System Message", lines=3 ) with gr.Column(scale=2): chatbot = gr.Chatbot() msg = gr.Textbox(label="메세지를 입력하세요") with gr.Row(): submit_button = gr.Button("전송") clear_button = gr.Button("대화 내역 지우기") msg.submit(respond, [msg, chatbot, model_name, max_tokens, temperature, top_p, system_message], chatbot) submit_button.click(respond, [msg, chatbot, model_name, max_tokens, temperature, top_p, system_message], chatbot) clear_button.click(clear_conversation, outputs=chatbot, queue=False) if __name__ == "__main__": demo.launch()