import gradio as gr from huggingface_hub import InferenceClient import openai import anthropic import os # 제거할 모델들을 MODELS 사전에서 제외 MODELS = { "Zephyr 7B Beta": "HuggingFaceH4/zephyr-7b-beta", "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": "microsoft/Phi-3-mini-4k-instruct", "Mixtral 8x7B": "mistralai/Mistral-7B-Instruct-v0.3", "Mixtral Nous-Hermes": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO", "Aya-23-35B": "CohereForAI/aya-23-35B", "DeepSeek-V3": "deepseek/deepseek-chat" } # Cohere Command R+ 모델 ID 정의 COHERE_MODEL = "CohereForAI/c4ai-command-r-plus-08-2024" def get_client(model_name, hf_token): """ 모델 이름에 맞춰 InferenceClient 생성. hf_token을 UI에서 입력받은 값으로 사용하도록 변경. """ if not hf_token: raise ValueError("HuggingFace API 토큰이 필요합니다.") if model_name in MODELS: model_id = MODELS[model_name] elif model_name == "Cohere Command R+": model_id = COHERE_MODEL else: raise ValueError("유효하지 않은 모델 이름입니다.") return InferenceClient(model_id, token=hf_token) def respond( message, chat_history, model_name, max_tokens, temperature, top_p, system_message, hf_token, # HF 토큰을 추가로 받음 ): try: client = get_client(model_name, hf_token) 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 model_name == "Cohere Command R+": # Cohere Command R+ 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)) return 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"오류가 발생했습니다: {str(e)}" chat_history.append((message, error_message)) yield chat_history def cohere_respond( message, chat_history, system_message, max_tokens, temperature, top_p, hf_token, # HF 토큰 추가 ): model_name = "Cohere Command R+" try: client = get_client(model_name, hf_token) 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: if human: messages.append({"role": "user", "content": human}) if assistant: messages.append({"role": "assistant", "content": assistant}) messages.append({"role": "user", "content": message}) try: # Cohere Command R+ 모델을 위한 비스트리밍 처리 response_full = client.chat_completion( messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p, ) assistant_message = response_full.choices[0].message.content chat_history.append((message, assistant_message)) return chat_history except Exception as e: error_message = f"오류가 발생했습니다: {str(e)}" chat_history.append((message, error_message)) return chat_history def chatgpt_respond( message, chat_history, system_message, max_tokens, temperature, top_p, openai_token, # openai 토큰 추가 ): """ chatgpt용 응답. openai_token을 UI에서 입력받아 사용하도록 변경. """ if not openai_token: chat_history.append((message, "OpenAI API 토큰이 필요합니다.")) return chat_history openai.api_key = openai_token # UI에서 받은 토큰 사용 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: response = openai.ChatCompletion.create( model="gpt-4o-mini", # 또는 다른 모델 ID 사용 messages=messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p, ) assistant_message = response.choices[0].message['content'] chat_history.append((message, assistant_message)) return chat_history except Exception as e: error_message = f"오류가 발생했습니다: {str(e)}" chat_history.append((message, error_message)) return chat_history def claude_respond( message, chat_history, system_message, max_tokens, temperature, top_p, claude_token, # Claude 토큰 추가 ): """ Claude용 응답. claude_token을 UI에서 입력받아 사용하도록 변경. """ if not claude_token: chat_history.append((message, "Claude API 토큰이 필요합니다.")) return chat_history try: client = anthropic.Anthropic(api_key=claude_token) # 메시지 생성 response = client.messages.create( model="claude-3-haiku-20240307", max_tokens=max_tokens, temperature=temperature, system=system_message, messages=[ { "role": "user", "content": message } ] ) assistant_message = response.content[0].text chat_history.append((message, assistant_message)) return chat_history except Exception as e: error_message = f"오류가 발생했습니다: {str(e)}" chat_history.append((message, error_message)) return chat_history def deepseek_respond( message, chat_history, system_message, max_tokens, temperature, top_p, deepseek_token, # DeepSeek 토큰 추가 ): """ DeepSeek용 응답. deepseek_token을 UI에서 입력받아 사용하도록 변경. """ if not deepseek_token: chat_history.append((message, "DeepSeek API 토큰이 필요합니다.")) return chat_history openai.api_key = deepseek_token openai.api_base = "https://api.deepseek.com/v1" 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: response = openai.ChatCompletion.create( model="deepseek-chat", messages=messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p, ) assistant_message = response.choices[0].message['content'] chat_history.append((message, assistant_message)) return chat_history except Exception as e: error_message = f"오류가 발생했습니다: {str(e)}" chat_history.append((message, error_message)) return chat_history def clear_conversation(): return [] with gr.Blocks() as demo: gr.Markdown("# Prompting AI Chatbot") gr.Markdown("언어모델별 프롬프트 테스트 챗봇입니다.") # --- 토큰 입력 UI 추가 --- with gr.Row(): hf_token_box = gr.Textbox( label="HuggingFace 토큰", type="password", placeholder="HuggingFace API 토큰을 입력하세요..." ) openai_token_box = gr.Textbox( label="OpenAI 토큰", type="password", placeholder="OpenAI API 토큰을 입력하세요..." ) claude_token_box = gr.Textbox( label="Claude 토큰", type="password", placeholder="Claude API 토큰을 입력하세요..." ) deepseek_token_box = gr.Textbox( label="DeepSeek 토큰", type="password", placeholder="DeepSeek API 토큰을 입력하세요..." ) with gr.Tab("일반 모델"): 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=100, maximum=8000, value=2000, 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") # respond 함수에 hf_token 인자를 전달하도록 수정 inputs_for_normal = [ msg, chatbot, model_name, max_tokens, temperature, top_p, system_message, hf_token_box ] msg.submit(respond, inputs_for_normal, chatbot) submit_button.click(respond, inputs_for_normal, chatbot) clear_button.click(clear_conversation, outputs=chatbot, queue=False) with gr.Tab("Cohere Command R+"): with gr.Row(): cohere_system_message = gr.Textbox( value="""반드시 한글로 답변할 것. 너는 최고의 비서이다. 내가 요구하는것들을 최대한 자세하고 정확하게 답변하라. """, label="System Message", lines=3 ) cohere_max_tokens = gr.Slider(minimum=100, maximum=8000, value=2000, step=100, label="Max new tokens") cohere_temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature") cohere_top_p = gr.Slider( minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-P", ) cohere_chatbot = gr.Chatbot(height=600) cohere_msg = gr.Textbox(label="메세지를 입력하세요") with gr.Row(): cohere_submit_button = gr.Button("전송") cohere_clear_button = gr.Button("대화 내역 지우기") # cohere_respond 함수에 hf_token 인자를 전달하도록 수정 inputs_for_cohere = [ cohere_msg, cohere_chatbot, cohere_system_message, cohere_max_tokens, cohere_temperature, cohere_top_p, hf_token_box ] cohere_msg.submit(cohere_respond, inputs_for_cohere, cohere_chatbot) cohere_submit_button.click(cohere_respond, inputs_for_cohere, cohere_chatbot) cohere_clear_button.click(clear_conversation, outputs=cohere_chatbot, queue=False) with gr.Tab("ChatGPT"): with gr.Row(): chatgpt_system_message = gr.Textbox( value="""반드시 한글로 답변할 것. 너는 ChatGPT, OpenAI에서 개발한 언어 모델이다. 내가 요구하는 것을 최대한 자세하고 정확하게 답변하라. """, label="System Message", lines=3 ) chatgpt_max_tokens = gr.Slider(minimum=100, maximum=5000, value=2000, step=100, label="Max Tokens") chatgpt_temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.05, label="Temperature") chatgpt_top_p = gr.Slider( minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-P", ) chatgpt_chatbot = gr.Chatbot(height=600) chatgpt_msg = gr.Textbox(label="메세지를 입력하세요") with gr.Row(): chatgpt_submit_button = gr.Button("전송") chatgpt_clear_button = gr.Button("대화 내역 지우기") # chatgpt_respond 함수에 openai_token 인자를 전달하도록 수정 inputs_for_chatgpt = [ chatgpt_msg, chatgpt_chatbot, chatgpt_system_message, chatgpt_max_tokens, chatgpt_temperature, chatgpt_top_p, openai_token_box ] chatgpt_msg.submit(chatgpt_respond, inputs_for_chatgpt, chatgpt_chatbot) chatgpt_submit_button.click(chatgpt_respond, inputs_for_chatgpt, chatgpt_chatbot) chatgpt_clear_button.click(clear_conversation, outputs=chatgpt_chatbot, queue=False) with gr.Tab("Claude"): with gr.Row(): claude_system_message = gr.Textbox( value="""반드시 한글로 답변할 것. 너는 Anthropic에서 개발한 클로드이다. 최대한 정확하고 친절하게 답변하라. """, label="System Message", lines=3 ) claude_max_tokens = gr.Slider(minimum=100, maximum=8000, value=2000, step=100, label="Max Tokens") claude_temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.05, label="Temperature") claude_top_p = gr.Slider( minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-P", ) claude_chatbot = gr.Chatbot(height=600) claude_msg = gr.Textbox(label="메세지를 입력하세요") with gr.Row(): claude_submit_button = gr.Button("전송") claude_clear_button = gr.Button("대화 내역 지우기") # claude_respond 함수에 claude_token 인자를 전달하도록 수정 inputs_for_claude = [ claude_msg, claude_chatbot, claude_system_message, claude_max_tokens, claude_temperature, claude_top_p, claude_token_box ] claude_msg.submit(claude_respond, inputs_for_claude, claude_chatbot) claude_submit_button.click(claude_respond, inputs_for_claude, claude_chatbot) claude_clear_button.click(clear_conversation, outputs=claude_chatbot, queue=False) with gr.Tab("DeepSeek"): with gr.Row(): deepseek_system_message = gr.Textbox( value="""반드시 한글로 답변할 것. 너는 DeepSeek-V3, 최고의 언어 모델이다. 내가 요구하는 것을 최대한 자세하고 정확하게 답변하라. """, label="System Message", lines=3 ) deepseek_max_tokens = gr.Slider(minimum=100, maximum=8000, value=2000, step=100, label="Max Tokens") deepseek_temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.05, label="Temperature") deepseek_top_p = gr.Slider( minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-P", ) deepseek_chatbot = gr.Chatbot(height=600) deepseek_msg = gr.Textbox(label="메세지를 입력하세요") with gr.Row(): deepseek_submit_button = gr.Button("전송") deepseek_clear_button = gr.Button("대화 내역 지우기") # deepseek_respond 함수에 deepseek_token 인자를 전달하도록 수정 inputs_for_deepseek = [ deepseek_msg, deepseek_chatbot, deepseek_system_message, deepseek_max_tokens, deepseek_temperature, deepseek_top_p, deepseek_token_box ] deepseek_msg.submit(deepseek_respond, inputs_for_deepseek, deepseek_chatbot) deepseek_submit_button.click(deepseek_respond, inputs_for_deepseek, deepseek_chatbot) deepseek_clear_button.click(clear_conversation, outputs=deepseek_chatbot, queue=False) if __name__ == "__main__": demo.launch()