Update app.py

app.py

@@ -3,10 +3,9 @@ import gradio as gr
 from dotenv import load_dotenv
 from openai import OpenAI
 from prompts.initial_prompt import INITIAL_PROMPT
-from prompts.main_prompt import TASK_PROMPT
+from prompts.main_prompt import MAIN_PROMPT
 
-
-# Load the OpenAI API key from .env file
+# .env 파일에서 OPENAI_API_KEY 로드
 if os.path.exists(".env"):
     load_dotenv(".env")
 
@@ -15,105 +14,96 @@ OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 client = OpenAI(api_key=OPENAI_API_KEY)
 
 
+
 def gpt_call(history, user_message,
              model="gpt-4o-mini",
              max_tokens=512,
              temperature=0.7,
              top_p=0.95):
     """
-    Calls OpenAI's ChatCompletion API to generate responses.
+    OpenAI ChatCompletion API를 통해 답변을 생성하는 함수.
     - history: [(user_text, assistant_text), ...]
-    - user_message: User's latest input
+    - user_message: 사용자가 방금 입력한 메시지
     """
-    # System message (TASK_PROMPT) at the beginning
-    messages = [{"role": "system", "content": TASK_PROMPT}]
+    # 1) 시스템 메시지(=MAIN_PROMPT)를 가장 앞에 추가
+    messages = [{"role": "system", "content": MAIN_PROMPT}]
     
-    # Convert history into OpenAI format
+    # 2) 기존 대화 기록(history)을 OpenAI 형식으로 변환
+    #    user_text -> 'user' / assistant_text -> 'assistant'
     for user_text, assistant_text in history:
         if user_text:
             messages.append({"role": "user", "content": user_text})
         if assistant_text:
             messages.append({"role": "assistant", "content": assistant_text})
 
-    # Add the latest user input
+    # 3) 마지막에 이번 사용자의 입력을 추가
     messages.append({"role": "user", "content": user_message})
 
-    # AI-controlled gradual guidance
-    if "bar model" in user_message.lower():
-        return "Great! You've started using a bar model. Can you explain how you divided it? What does each section represent?"
-    
-    elif "double number line" in user_message.lower():
-        return "Nice! How does your number line show the relationship between time and distance? Did you mark the correct intervals?"
-    
-    elif "ratio table" in user_message.lower():
-        return "Good choice! Before I check, how did you determine the ratio for 1 hour?"
-    
-    elif "graph" in user_message.lower():
-        return "Graphs are powerful! What key points did you plot, and why?"
-    
-    else:
-        # OpenAI API call (fallback response)
-        completion = client.chat.completions.create(
-            model=model,
-            messages=messages,
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=top_p
-        )
-        return completion.choices[0].message.content
-
+    # 4) OpenAI API 호출
+    completion = client.chat.completions.create(
+        model=model,
+        messages=messages,
+        max_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p
+    )
+    return completion.choices[0].message.content
 
 def respond(user_message, history):
     """
-    Handles user input and chatbot response in Gradio.
-    - user_message: The latest input from the user.
-    - history: A list of (user, assistant) message pairs.
+    Gradio 상에서 submit할 때 호출되는 함수
+    - user_message: 사용자가 방금 친 메시지
+    - history: 기존 (user, assistant) 튜플 리스트
     """
+    # 사용자가 빈 문자열을 보냈다면 아무 일도 하지 않음
     if not user_message:
         return "", history
 
-    # Generate AI response
+    # GPT 모델로부터 응답을 받음
     assistant_reply = gpt_call(history, user_message)
 
-    # Append to history
+    # history에 (user, assistant) 쌍 추가
     history.append((user_message, assistant_reply))
 
-    # Return the updated history and clear the input box
+    # Gradio에서는 (새로 비워질 입력창, 갱신된 history)를 반환
     return "", history
 
-
 ##############################
-#  Gradio Chatbot UI
+#  Gradio Blocks UI
 ##############################
 with gr.Blocks() as demo:
-    gr.Markdown("## AI-Guided Teacher PD Chatbot")
+    gr.Markdown("## Simple Chat Interface")
 
-    # Initial chatbot message (starts with the task)
+    # Chatbot 초기 상태를 설정
+    # 첫 번째 메시지는 (user="", assistant=INITIAL_PROMPT) 형태로 넣어
+    # 화면상에서 'assistant'가 INITIAL_PROMPT를 말한 것처럼 보이게 함
     chatbot = gr.Chatbot(
-        value=[("", INITIAL_PROMPT)],
+        value=[("", INITIAL_PROMPT)],  # (user, assistant)
         height=500
     )
 
-    # Chat history state
+    # (user, assistant) 쌍을 저장할 히스토리 상태
+    # 여기서도 동일한 초기 상태를 넣어줌
     state_history = gr.State([("", INITIAL_PROMPT)])
 
-    # User input box
+    # 사용자 입력
     user_input = gr.Textbox(
-        placeholder="Type your response here...",
+        placeholder="Type your message here...",
         label="Your Input"
     )
 
-    # When user submits input → respond() updates chatbot
+    # 입력이 submit되면 respond() 호출 → 출력은 (새 입력창, 갱신된 chatbot)
     user_input.submit(
         respond,
         inputs=[user_input, state_history],
         outputs=[user_input, chatbot]
     ).then(
+        # respond 끝난 뒤, 최신 history를 state_history에 반영
         fn=lambda _, h: h,
         inputs=[user_input, chatbot],
         outputs=[state_history]
     )
 
-# Launch the chatbot
+# 메인 실행
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860, share=True)