import gradio as gr from huggingface_hub import InferenceClient import openai import anthropic import os # Cohere Command R+ 모델 ID 정의 COHERE_MODEL = "CohereForAI/c4ai-command-r-plus-08-2024" def get_client(model_name: str): """ 모델 이름에 맞춰 InferenceClient 생성. HuggingFace 토큰은 os.environ.get("HF_TOKEN")을 통해 환경변수로 가져온다. """ hf_token = os.environ.get("HF_TOKEN") if not hf_token: raise ValueError("HuggingFace API 토큰이 필요합니다. (환경변수 HF_TOKEN 미설정)") if model_name == "Cohere Command R+": model_id = COHERE_MODEL else: raise ValueError("유효하지 않은 모델 이름입니다.") return InferenceClient(model_id, token=hf_token) def cohere_respond( message, chat_history, system_message, max_tokens, temperature, top_p, ): """ Cohere Command R+ 모델 응답 함수. HF 토큰은 함수 내부에서 os.environ을 통해 불러온다. """ model_name = "Cohere Command R+" 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: if human: messages.append({"role": "user", "content": human}) if assistant: messages.append({"role": "assistant", "content": assistant}) messages.append({"role": "user", "content": message}) try: 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, ): """ ChatGPT 모델 응답 함수. OpenAI 토큰은 함수 내부에서 os.environ을 통해 불러온다. """ openai_token = os.environ.get("OPENAI_TOKEN") if not openai_token: chat_history.append((message, "OpenAI API 토큰이 필요합니다. (환경변수 OPENAI_TOKEN 미설정)")) return chat_history openai.api_key = openai_token # 환경변수에서 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 모델 응답 함수. Claude 토큰은 함수 내부에서 os.environ을 통해 불러온다. """ claude_token = os.environ.get("CLAUDE_TOKEN") if not claude_token: chat_history.append((message, "Claude API 토큰이 필요합니다. (환경변수 CLAUDE_TOKEN 미설정)")) return chat_history try: client = anthropic.Anthropic(api_key=claude_token) 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, deepseek_model_choice, max_tokens, temperature, top_p, ): """ DeepSeek 모델 응답 함수. DeepSeek 토큰은 함수 내부에서 os.environ을 통해 불러온다. deepseek_model_choice에 따라 deepseek-chat 또는 deepseek-reasoner를 선택하며, 스트리밍 방식으로 응답을 받아옵니다. """ deepseek_token = os.environ.get("DEEPSEEK_TOKEN") if not deepseek_token: chat_history.append((message, "DeepSeek API 토큰이 필요합니다. (환경변수 DEEPSEEK_TOKEN 미설정)")) yield chat_history return 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}) # 모델 선택: 기본은 deepseek-chat if deepseek_model_choice == "R1(deepseek-reasoner)": model = "deepseek-reasoner" else: model = "deepseek-chat" try: response = openai.ChatCompletion.create( model=model, messages=messages, max_tokens=max_tokens, temperature=temperature, top_p=top_p, stream=True ) assistant_message = "" # 새로운 대화 항목을 추가하고 초기값을 스트리밍하면서 갱신 chat_history.append((message, assistant_message)) yield chat_history for chunk in response: # "content"가 None인 경우 빈 문자열로 처리하여 오류 방지 delta = chunk.choices[0].delta.get("content") or "" assistant_message += delta chat_history[-1] = (message, assistant_message) yield chat_history return except Exception as e: error_message = f"오류가 발생했습니다: {str(e)}" chat_history.append((message, error_message)) yield chat_history return def clear_conversation(): return [] # -------------------------------------------- # Gradio 앱 시작 # -------------------------------------------- with gr.Blocks() as demo: gr.Markdown("# Prompting AI Chatbot") gr.Markdown("언어모델별 프롬프트 테스트 챗봇입니다.") # -------------------------------------------------- # 일반 모델 관련 UI/기능 제거 (요청 사항에 따라 삭제) # -------------------------------------------------- # Cohere Command R+ 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") inputs_for_cohere = [ cohere_msg, cohere_chatbot, cohere_system_message, cohere_max_tokens, cohere_temperature, cohere_top_p ] 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) # ChatGPT 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") inputs_for_chatgpt = [ chatgpt_msg, chatgpt_chatbot, chatgpt_system_message, chatgpt_max_tokens, chatgpt_temperature, chatgpt_top_p ] 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) # Claude 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") inputs_for_claude = [ claude_msg, claude_chatbot, claude_system_message, claude_max_tokens, claude_temperature, claude_top_p ] 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) # DeepSeek 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_model_choice = gr.Radio( choices=["V3(deepseek-chat)", "R1(deepseek-reasoner)"], value="V3(deepseek-chat)", label="모델 선택" ) 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") inputs_for_deepseek = [ deepseek_msg, deepseek_chatbot, deepseek_system_message, deepseek_model_choice, deepseek_max_tokens, deepseek_temperature, deepseek_top_p ] # Textbox.submit에서는 stream 인자를 제거합니다. 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()