Update app.py

app.py

@@ -40,468 +40,58 @@ def get_client(model_name):
     return InferenceClient(model_id, token=hf_token)
 
 
-def respond_hf_qna(
-    question: str,
-    model_name: str,
-    max_tokens: int,
-    temperature: float,
-    top_p: float,
-    system_message: str
-):
+def translate_text(text, source_lang, target_lang):
     """
-    HuggingFace 모델(Zephyr 등)에 대해 한 번의 질문(question)에 대한 답변을 반환하는 함수.
+    텍스트를 번역하는 함수.
     """
     try:
-        client = get_client(model_name)
-    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
-
+        client = get_client("Zephyr 7B Beta")  # 번역에 사용할 모델 선택
+        prompt = f"Translate the following text from {source_lang} to {target_lang}: {text}"
+        response = client.text_generation(prompt, max_new_tokens=500)
+        return response
     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
-):
-    """
-    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)
-    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
-):
-    """
-    ChatGPT(OpenAI) 모델을 이용해 한 번의 질문(question)에 대한 답변을 반환하는 함수.
-    """
-    openai_token = os.getenv("OPENAI_TOKEN")
-    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 모델을 이용해 한 번의 질문(question)에 대한 답변을 반환하는 함수.
-    """
-    deepseek_token = os.getenv("DEEPSEEK_TOKEN")
-    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
-) -> str:
-    """
-    Claude API를 사용한 개선된 응답 생성 함수
-    """
-    claude_api_key = os.getenv("CLAUDE_TOKEN")
-    if not claude_api_key:
-        return "Claude API 토큰이 필요합니다."
-
-    try:
-        client = anthropic.Anthropic(api_key=claude_api_key)
-        
-        # 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
-                }
-            ]
-        )
-        
-        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")
+    gr.Markdown("# 번역기")
 
     #################
-    # 일반 모델 탭
+    # 번역기 탭
     #################
-    with gr.Tab("일반 모델"):
-        # 모델명 선택
-        model_name = gr.Radio(
-            choices=list(MODELS.keys()),
-            label="Language Model (HuggingFace)",
-            value="Zephyr 7B Beta"
+    with gr.Tab("번역기"):
+        # 언어 선택
+        source_lang = gr.Radio(
+            choices=["한국어", "영어", "일본어", "중국어"],
+            label="원본 언어",
+            value="한국어"
         )
-
-        # 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):
-            # 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
-            )
-
-        submit_button.click(
-            fn=merge_and_call_hf,
-            inputs=[
-                input1, input2, input3, input4, input5,
-                model_name,
-                max_tokens,
-                temperature,
-                top_p,
-                system_message
-            ],
-            outputs=answer_output
+        target_lang = gr.Radio(
+            choices=["한국어", "영어", "일본어", "중국어"],
+            label="목표 언어",
+            value="영어"
         )
 
-    #################
-    # 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_):
-            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_
-            )
-
-        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
-            ],
-            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_):
-            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_
-            )
-
-        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
-            ],
-            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_):
-            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_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
-            ],
-            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)
+        # 입력 텍스트
+        input_text = gr.Textbox(label="번역할 텍스트", lines=5)
 
-        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")
+        # 번역 결과
+        translation_output = gr.Textbox(label="번역 결과", lines=5, interactive=False)
 
-        deepseek_submit_button = gr.Button("전송")
+        # 번역 버튼
+        translate_button = gr.Button("번역")
 
-        def merge_and_call_deepseek(i1, i2, i3, i4, i5, sys_msg, mt, temp, top_p_):
-            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_
-            )
+        def translate(input_text, source_lang, target_lang):
+            return translate_text(input_text, source_lang, target_lang)
 
-        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
-            ],
-            outputs=deepseek_answer_output
+        translate_button.click(
+            fn=translate,
+            inputs=[input_text, source_lang, target_lang],
+            outputs=translation_output
         )
 
 #############################