app.py

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()