Update app.py

app.py

@@ -1,581 +1,44 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-import openai
-import anthropic
-from typing import Optional
+from flask import Flask, render_template, request
 
-#############################
-# [기본코드] - 수정/삭제 불가
-#############################
+app = Flask(__name__)
 
-# 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"}
+# 각 모델에 대한 처리 함수 (가상)
+def 일반모델_처리(text):
+    return f"일반모델 처리 결과: {text}"
 
-# Cohere Command R+ 모델 ID 정의
-COHERE_MODEL = "CohereForAI/c4ai-command-r-plus-08-2024"
+def 코히어모델_처리(text):
+    return f"코히어모델 처리 결과: {text}"
 
-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.")
+def 지피티모델_처리(text):
+    return f"지피티모델 처리 결과: {text}"
 
-    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 클로드모델_처리(text):
+    return f"클로드모델 처리 결과: {text}"
 
+def 딥시크모델_처리(text):
+    return f"딥시크모델 처리 결과: {text}"
 
-def respond_hf_qna(
-    question: str,
-    model_name: str,
-    max_tokens: int,
-    temperature: float,
-    top_p: float,
-    system_message: str,
-    hf_token: str
-):
-    """
-    HuggingFace 모델(Zephyr 등)에 대해 한 번의 질문(question)에 대한 답변을 반환하는 함수.
-    """
-    try:
-        client = get_client(model_name, hf_token)
-    except ValueError as e:
-        return f"오류: {str(e)}"
-
-    messages = [
-        {"role": "system", "content": system_message},
-        {"role": "user", "content": question}
-    ]
-
-    try:
-        response = client.chat_completion(
-            messages,
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            stream=False,
-        )
-        assistant_message = response.choices[0].message.content
-        return assistant_message
-
-    except Exception as e:
-        return f"오류가 발생했습니다: {str(e)}"
-
-
-def respond_cohere_qna(
-    question: str,
-    system_message: str,
-    max_tokens: int,
-    temperature: float,
-    top_p: float,
-    hf_token: str
-):
-    """
-    Cohere Command R+ 모델을 이용해 한 번의 질문(question)에 대한 답변을 반환하는 함수.
-    """
-    model_name = "Cohere Command R+"
-    try:
-        client = get_client(model_name, hf_token)
-    except ValueError as e:
-        return f"오류: {str(e)}"
-
-    messages = [
-        {"role": "system", "content": system_message},
-        {"role": "user", "content": question}
-    ]
-
-    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
-        return assistant_message
-    except Exception as e:
-        return f"오류가 발생했습니다: {str(e)}"
-
-
-def respond_chatgpt_qna(
-    question: str,
-    system_message: str,
-    max_tokens: int,
-    temperature: float,
-    top_p: float,
-    openai_token: str
-):
-    """
-    ChatGPT(OpenAI) 모델을 이용해 한 번의 질문(question)에 대한 답변을 반환하는 함수.
-    """
-    if not openai_token:
-        return "OpenAI API 토큰이 필요합니다."
-
-    openai.api_key = openai_token
-
-    messages = [
-        {"role": "system", "content": system_message},
-        {"role": "user", "content": question}
-    ]
-
-    try:
-        response = openai.ChatCompletion.create(
-            model="gpt-4o-mini",  # 필요한 경우 변경
-            messages=messages,
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=top_p,
-        )
-        assistant_message = response.choices[0].message['content']
-        return assistant_message
-    except Exception as e:
-        return f"오류가 발생했습니다: {str(e)}"
-
-
-def respond_deepseek_qna(
-    question: str,
-    system_message: str,
-    max_tokens: int,
-    temperature: float,
-    top_p: float,
-    deepseek_token: str
-):
-    """
-    DeepSeek 모델을 이용해 한 번의 질문(question)에 대한 답변을 반환하는 함수.
-    """
-    if not deepseek_token:
-        return "DeepSeek API 토큰이 필요합니다."
-
-    openai.api_key = deepseek_token
-    openai.api_base = "https://api.deepseek.com/v1"
-
-    messages = [
-        {"role": "system", "content": system_message},
-        {"role": "user", "content": question}
-    ]
-
-    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']
-        return assistant_message
-    except Exception as e:
-        return f"오류가 발생했습니다: {str(e)}"
-
-
-def respond_claude_qna(
-    question: str,
-    system_message: str,
-    max_tokens: int,
-    temperature: float,
-    top_p: float,
-    claude_api_key: str
-) -> str:
-    """
-    Claude API를 사용한 개선된 응답 생성 함수
-    """
-    if not claude_api_key:
-        return "Claude API 토큰이 필요합니다."
-
-    try:
-        client = anthropic.Anthropic(api_key=claude_api_key)
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    if request.method == 'POST':
+        input_text = request.form['reference_text']
         
-        # 메시지 생성
-        message = client.messages.create(
-            model="claude-3-haiku-20240307",
-            max_tokens=max_tokens,
-            temperature=temperature,
-            system=system_message,
-            messages=[
-                {
-                    "role": "user",
-                    "content": question
-                }
-            ]
-        )
+        # 모든 모델에 대해 처리
+        일반모델_결과 = 일반모델_처리(input_text)
+        코히어모델_결과 = 코히어모델_처리(input_text)
+        지피티모델_결과 = 지피티모델_처리(input_text)
+        클로드모델_결과 = 클로드모델_처리(input_text)
+        딥시크모델_결과 = 딥시크모델_처리(input_text)
         
-        return message.content[0].text
-            
-    except anthropic.APIError as ae:
-        return f"Claude API 오류: {str(ae)}"
-    except anthropic.RateLimitError:
-        return "요청 한도를 초과했습니다. 잠시 후 다시 시도해주세요."
-    except Exception as e:
-        return f"예상치 못한 오류가 발생했습니다: {str(e)}"
-
-
-#############################
-# [기본코드] UI 부분 - 수정/삭제 불가
-#############################
-
-with gr.Blocks() as demo:
-    gr.Markdown("# LLM 플레이그라운드")
-
-    # 토큰 비밀번호 입력창
-    with gr.Row():
-        token_password_box = gr.Textbox(
-            label="토큰 비밀번호",
-            type="password",
-            placeholder="비밀번호를 입력하세요...",
-            lines=1
-        )
-
-    # 토큰 입력창 (초기에는 숨김)
-    with gr.Row(visible=False) as token_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...",
-            show_copy_button=False
-        )
-        deepseek_token_box = gr.Textbox(
-            label="DeepSeek 토큰",
-            type="password",
-            placeholder="DeepSeek API 토큰을 입력하세요..."
-        )
-
-    # 말투 바꾸기 탭
-    with gr.Tab("말투 바꾸기"):
-        tone_radio = gr.Radio(
-            choices=["친근하게", "일반적인", "전문적인"],
-            label="말투 선택",
-            value="일반적인"
-        )
-
-    # 참조글 입력 탭
-    with gr.Tab("참조글 입력"):
-        ref_input1 = gr.Textbox(label="참조글 1", lines=5)
-        ref_input2 = gr.Textbox(label="참조글 2", lines=5)
-        ref_input3 = gr.Textbox(label="참조글 3", lines=5)
-
-    #################
-    # 일반 모델 탭
-    #################
-    with gr.Tab("일반 모델"):
-        # 모델명 선택
-        model_name = gr.Radio(
-            choices=list(MODELS.keys()),
-            label="Language Model (HuggingFace)",
-            value="Zephyr 7B Beta"
-        )
-
-        # 입력1 ~ 입력5 (세로로 하나씩)
-        input1 = gr.Textbox(label="입력1", lines=1)
-        input2 = gr.Textbox(label="입력2", lines=1)
-        input3 = gr.Textbox(label="입력3", lines=1)
-        input4 = gr.Textbox(label="입력4", lines=1)
-        input5 = gr.Textbox(label="입력5", lines=1)
-
-        # 결과
-        answer_output = gr.Textbox(label="결과", lines=5, interactive=False)
-
-        # 고급 설정 - System Message를 Max Tokens 위로 이동
-        with gr.Accordion("고급 설정 (일반 모델)", open=False):
-            system_message = gr.Textbox(
-                value="""반드시 한글로 답변할 것.
-너는 최고의 비서이다.
-내가 요구하는것들을 최대한 자세하고 정확하게 답변하라.
-""",
-                label="System Message",
-                lines=3
-            )
-            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")
-
-        submit_button = gr.Button("전송")
-
-        def merge_and_call_hf(i1, i2, i3, i4, i5, m_name, mt, temp, top_p_, sys_msg, hf_token):
-            # 입력1~5를 공백 기준으로 합쳐서 question 구성
-            question = " ".join([i1, i2, i3, i4, i5])
-            return respond_hf_qna(
-                question=question,
-                model_name=m_name,
-                max_tokens=mt,
-                temperature=temp,
-                top_p=top_p_,
-                system_message=sys_msg,
-                hf_token=hf_token
-            )
-
-        submit_button.click(
-            fn=merge_and_call_hf,
-            inputs=[
-                input1, input2, input3, input4, input5,
-                model_name,
-                max_tokens,
-                temperature,
-                top_p,
-                system_message,
-                hf_token_box
-            ],
-            outputs=answer_output
-        )
-
-    #################
-    # Cohere Command R+ 탭
-    #################
-    with gr.Tab("Cohere Command R+"):
-        cohere_input1 = gr.Textbox(label="입력1", lines=1)
-        cohere_input2 = gr.Textbox(label="입력2", lines=1)
-        cohere_input3 = gr.Textbox(label="입력3", lines=1)
-        cohere_input4 = gr.Textbox(label="입력4", lines=1)
-        cohere_input5 = gr.Textbox(label="입력5", lines=1)
-
-        cohere_answer_output = gr.Textbox(label="결과", lines=5, interactive=False)
-
-        with gr.Accordion("고급 설정 (Cohere)", open=False):
-            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=10000, value=4000, step=100, label="Max 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_submit_button = gr.Button("전송")
-
-        def merge_and_call_cohere(i1, i2, i3, i4, i5, sys_msg, mt, temp, top_p_, hf_token):
-            question = " ".join([i1, i2, i3, i4, i5])
-            return respond_cohere_qna(
-                question=question,
-                system_message=sys_msg,
-                max_tokens=mt,
-                temperature=temp,
-                top_p=top_p_,
-                hf_token=hf_token
-            )
-
-        cohere_submit_button.click(
-            fn=merge_and_call_cohere,
-            inputs=[
-                cohere_input1, cohere_input2, cohere_input3, cohere_input4, cohere_input5,
-                cohere_system_message,
-                cohere_max_tokens,
-                cohere_temperature,
-                cohere_top_p,
-                hf_token_box
-            ],
-            outputs=cohere_answer_output
-        )
-
-    #################
-    # ChatGPT 탭
-    #################
-    with gr.Tab("gpt-4o-mini"):
-        chatgpt_input1 = gr.Textbox(label="입력1", lines=1)
-        chatgpt_input2 = gr.Textbox(label="입력2", lines=1)
-        chatgpt_input3 = gr.Textbox(label="입력3", lines=1)
-        chatgpt_input4 = gr.Textbox(label="입력4", lines=1)
-        chatgpt_input5 = gr.Textbox(label="입력5", lines=1)
-
-        chatgpt_answer_output = gr.Textbox(label="결과", lines=5, interactive=False)
-
-        with gr.Accordion("고급 설정 (ChatGPT)", open=False):
-            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=4000, 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_submit_button = gr.Button("전송")
-
-        def merge_and_call_chatgpt(i1, i2, i3, i4, i5, sys_msg, mt, temp, top_p_, openai_token):
-            question = " ".join([i1, i2, i3, i4, i5])
-            return respond_chatgpt_qna(
-                question=question,
-                system_message=sys_msg,
-                max_tokens=mt,
-                temperature=temp,
-                top_p=top_p_,
-                openai_token=openai_token
-            )
-
-        chatgpt_submit_button.click(
-            fn=merge_and_call_chatgpt,
-            inputs=[
-                chatgpt_input1, chatgpt_input2, chatgpt_input3, chatgpt_input4, chatgpt_input5,
-                chatgpt_system_message,
-                chatgpt_max_tokens,
-                chatgpt_temperature,
-                chatgpt_top_p,
-                openai_token_box
-            ],
-            outputs=chatgpt_answer_output
-        )
-
-    #################
-    # Claude 탭
-    #################
-    with gr.Tab("claude-3-haiku"):
-        claude_input1 = gr.Textbox(label="입력1", lines=1)
-        claude_input2 = gr.Textbox(label="입력2", lines=1)
-        claude_input3 = gr.Textbox(label="입력3", lines=1)
-        claude_input4 = gr.Textbox(label="입력4", lines=1)
-        claude_input5 = gr.Textbox(label="입력5", lines=1)
-
-        claude_answer_output = gr.Textbox(label="결과", interactive=False, lines=5)
-
-        with gr.Accordion("고급 설정 (Claude)", open=False):
-            claude_system_message = gr.Textbox(
-                label="System Message",
-                value="""반드시 한글로 답변할 것.
-너는 Anthropic에서 개발한 클로드이다.
-최대한 정확하고 친절하게 답변하라.""",
-                lines=3
-            )
-            claude_max_tokens = gr.Slider(
-                minimum=100,
-                maximum=4000,
-                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_submit_button = gr.Button("전송")
-
-        def merge_and_call_claude(i1, i2, i3, i4, i5, sys_msg, mt, temp, top_p_, claude_key):
-            question = " ".join([i1, i2, i3, i4, i5])
-            return respond_claude_qna(
-                question=question,
-                system_message=sys_msg,
-                max_tokens=mt,
-                temperature=temp,
-                top_p=top_p_,
-                claude_api_key=claude_key
-            )
-
-        claude_submit_button.click(
-            fn=merge_and_call_claude,
-            inputs=[
-                claude_input1, claude_input2, claude_input3, claude_input4, claude_input5,
-                claude_system_message,
-                claude_max_tokens,
-                claude_temperature,
-                claude_top_p,
-                claude_token_box
-            ],
-            outputs=claude_answer_output
-        )
-
-    #################
-    # DeepSeek 탭
-    #################
-    with gr.Tab("DeepSeek-V3"):
-        deepseek_input1 = gr.Textbox(label="입력1", lines=1)
-        deepseek_input2 = gr.Textbox(label="입력2", lines=1)
-        deepseek_input3 = gr.Textbox(label="입력3", lines=1)
-        deepseek_input4 = gr.Textbox(label="입력4", lines=1)
-        deepseek_input5 = gr.Textbox(label="입력5", lines=1)
-
-        deepseek_answer_output = gr.Textbox(label="결과", lines=5, interactive=False)
-
-        with gr.Accordion("고급 설정 (DeepSeek)", open=False):
-            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=4000, 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_submit_button = gr.Button("전송")
-
-        def merge_and_call_deepseek(i1, i2, i3, i4, i5, sys_msg, mt, temp, top_p_, deepseek_token):
-            question = " ".join([i1, i2, i3, i4, i5])
-            return respond_deepseek_qna(
-                question=question,
-                system_message=sys_msg,
-                max_tokens=mt,
-                temperature=temp,
-                top_p=top_p_,
-                deepseek_token=deepseek_token
-            )
-
-        deepseek_submit_button.click(
-            fn=merge_and_call_deepseek,
-            inputs=[
-                deepseek_input1, deepseek_input2, deepseek_input3, deepseek_input4, deepseek_input5,
-                deepseek_system_message,
-                deepseek_max_tokens,
-                deepseek_temperature,
-                deepseek_top_p,
-                deepseek_token_box
-            ],
-            outputs=deepseek_answer_output
-        )
-
-    # 토큰 비밀번호 입력 후 토큰 입력창 표시
-    def toggle_token_row(password):
-        if password == "123":
-            return gr.Row.update(visible=True)
-        else:
-            return gr.Row.update(visible=False)
-
-    token_password_box.submit(
-        fn=toggle_token_row,
-        inputs=token_password_box,
-        outputs=token_row
-    )
-
-#############################
-# 메인 실행부
-#############################
-if __name__ == "__main__":
-    demo.launch()
+        # 결과를 result.html에 전달
+        return render_template('result.html', 
+                              일반모델_결과=일반모델_결과,
+                              코히어모델_결과=코히어모델_결과,
+                              지피티모델_결과=지피티모델_결과,
+                              클로드모델_결과=클로드모델_결과,
+                              딥시크모델_결과=딥시크모델_결과)
+    
+    return render_template('index.html')
+
+if __name__ == '__main__':
+    app.run(debug=True)