Update app.py

app.py

@@ -5,145 +5,35 @@ from openai import OpenAI
 from prompts.initial_prompt import INITIAL_PROMPT
 from prompts.main_prompt import MAIN_PROMPT
 
-# .env 파일에서 OPENAI_API_KEY 로드
+# Load OpenAI API Key from .env file
 if os.path.exists(".env"):
-    load_dotenv(".env")Module 10: Developing Conceptual Understanding through Tables and Proportional Reasoning
-
-Task Introduction
-"Welcome to the final module in this series! In this module, you’ll watch a video of a lesson on proportional reasoning involving tables. You’ll reflect on the teacher’s practices, how students connect their reasoning, and the ways these practices address Common Core standards. Let’s dive in!"
-
-Video:
-"Watch the video provided at this link. Before watching how students approach the task, solve it yourself to reflect on your own reasoning."
-
-🚀 **Pre-Video Task Prompt**
-Before watching the video, let's start by solving the problem.
-
-1️⃣ **How did you approach solving the problem?**
-- What strategies did you use?
-- Did you recognize proportional relationships within the table?
-
-🛠 **Hints if Needed**:
-- Think about the relationships both horizontally (within rows) and vertically (between columns) in the table.
-- How might unit rate play a role in reasoning proportionally?
-
-After you solve the problem, **let me know**, and we’ll move to the next step!
-
----
-
-📖 **Post-Video Reflection Prompts**  
-Now that you’ve watched the video and solved the problem, let’s reflect on different aspects of the lesson **one by one**:
-
-### **Step 1: Observing Creativity-Directed Practices**
-🔹 What creativity-directed practices did you notice the teacher implementing during the lesson?  
-🔹 Reflect on how these practices supported students’ reasoning and collaboration.  
-  
-💡 **Hints if Needed**:
-- Consider whether the teacher encouraged mathematical connections, collaborative problem-solving, or extended students’ thinking beyond the unit rate.
-
-✅ When you're ready, **share your thoughts**, and we'll move to the next reflection.
-
----
-
-### **Step 2: Student Reasoning and Connections**
-🔹 How did students connect the relationship between price and container size?  
-🔹 How did their reasoning evolve as they worked through the task?  
-
-💡 **Hints if Needed**:
-- Did students start with the given information (e.g., the 24-ounce container costing $3)?
-- How did they use this information to reason proportionally?
-
-✅ **Once you respond, we’ll move on!**
-
----
-
-### **Step 3: Teacher Actions in Small Groups**
-🔹 How did the teacher’s actions during small group interactions reflect the students’ reasoning?  
-🔹 How did the teacher use these interactions to inform whole-class discussions?  
-
-💡 **Hints if Needed**:
-- Think about how the teacher listened to students’ reasoning and used their ideas to guide the next steps.  
-- What types of questions did the teacher ask?  
-
-✅ **Once you're ready, let’s move forward!**
-
----
-
-### **Step 4: Initial Prompts and Sense-Making**
-🔹 How did the teacher prompt students to initially make sense of the task?  
-🔹 What role did these prompts play in guiding students’ reasoning?  
-
-💡 **Hints if Needed**:
-- Did the teacher ask open-ended questions?
-- How did these prompts help students engage with the task?
-
-✅ **Share your response, and we’ll continue!**
-
----
-
-### **Step 5: Common Core Practice Standards**
-🔹 What Common Core practice standards do you think the teacher emphasized during the lesson?  
-🔹 Choose four and explain how you observed these practices in action.  
-
-💡 **Hints if Needed**:
-- Consider whether the teacher emphasized reasoning, collaboration, or modeling with mathematics.  
-- How did the students demonstrate these practices?
-
-✅ **When you’re ready, let’s move to the final steps!**
-
----
-
-### **Step 6: Problem Posing Activity**
-📝 Based on what you observed, **pose a problem** that encourages students to use visuals and proportional reasoning.  
-🔹 What real-world context will you use?  
-🔹 How will students use visuals like bar models or tables to represent proportional relationships?  
-🔹 Does your problem encourage multiple solution paths?  
-
-💡 **Hints if Needed**:
-- Try to design a problem where students can approach it differently but still apply proportional reasoning.
-
-✅ **Once you've created your problem, let me know!**
-
----
-
-### **Step 7: Summary and Final Reflection**
-📚 **What’s one change you will make in your own teaching based on this module?**  
-Reflect on a specific strategy, question type, or approach to representation that you want to implement.
-
-💡 **Encouraging Closing Statement**:  
-"Great work completing all the modules! We hope you’ve gained valuable insights into fostering creativity, connecting mathematical ideas, and engaging students in meaningful learning experiences. It was a pleasure working with you—see you in the next professional development series!" 🎉  
-
+    load_dotenv(".env")
 
 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,
+             max_tokens=1024,
              temperature=0.7,
              top_p=0.95):
     """
-    OpenAI ChatCompletion API를 통해 답변을 생성하는 함수.
+    Calls OpenAI Chat API to generate responses.
     - history: [(user_text, assistant_text), ...]
-    - user_message: 사용자가 방금 입력한 메시지
+    - user_message: latest message from user
     """
-    # 1) 시스템 메시지(=MAIN_PROMPT)를 가장 앞에 추가
     messages = [{"role": "system", "content": MAIN_PROMPT}]
     
-    # 2) 기존 대화 기록(history)을 OpenAI 형식으로 변환
-    #    user_text -> 'user' / assistant_text -> 'assistant'
+    # Add conversation history
     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})
 
-    # 3) 마지막에 이번 사용자의 입력을 추가
     messages.append({"role": "user", "content": user_message})
-
-    # 4) OpenAI API 호출
+    
+    # OpenAI API Call
     completion = client.chat.completions.create(
         model=model,
         messages=messages,
@@ -151,63 +41,65 @@ def gpt_call(history, user_message,
         temperature=temperature,
         top_p=top_p
     )
-    return completion.choices[0].message.content
+    
+    response = completion.choices[0].message.content
+
+    # Provide step-by-step responses for each section
+    if "pre-video" in user_message.lower():
+        response = "Great! Before watching the video, solve the problem first. How did you approach solving it?"
+    
+    if "post-video" in user_message.lower():
+        response = "Now that you’ve watched the video, let’s start by reflecting on the teacher’s creativity-directed practices. What stood out to you the most?"
+
+    if "common core" in user_message.lower():
+        response = "What Common Core practice standards do you think the teacher emphasized during the lesson? Choose four and explain how you observed these practices in action."
+
+    if "problem posing" in user_message.lower():
+        response = "Based on what you observed, pose a problem that encourages students to use visuals and proportional reasoning. What real-world context will you use?"
+
+    if "summary" in user_message.lower():
+        response = "What’s one change you will make in your own teaching based on this module? Reflect on a specific strategy or approach."
+
+    return response
 
 def respond(user_message, history):
     """
-    Gradio 상에서 submit할 때 호출되는 함수
-    - user_message: 사용자가 방금 친 메시지
-    - history: 기존 (user, assistant) 튜플 리스트
+    Handles user input and chatbot responses.
     """
-    # 사용자가 빈 문자열을 보냈다면 아무 일도 하지 않음
     if not user_message:
         return "", history
 
-    # GPT 모델로부터 응답을 받음
     assistant_reply = gpt_call(history, user_message)
-
-    # history에 (user, assistant) 쌍 추가
     history.append((user_message, assistant_reply))
-
-    # Gradio에서는 (새로 비워질 입력창, 갱신된 history)를 반환
     return "", history
 
 ##############################
 #  Gradio Blocks UI
 ##############################
 with gr.Blocks() as demo:
-    gr.Markdown("## Simple Chat Interface")
+    gr.Markdown("## AI-Guided Math PD Chatbot")
 
-    # 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)],  # (user, assistant)
-        height=500
+        value=[("", INITIAL_PROMPT)],
+        height=600
     )
 
-    # (user, assistant) 쌍을 저장할 히스토리 상태
-    # 여기서도 동일한 초기 상태를 넣어줌
     state_history = gr.State([("", INITIAL_PROMPT)])
 
-    # 사용자 입력
     user_input = gr.Textbox(
         placeholder="Type your message here...",
         label="Your Input"
     )
 
-    # 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]
     )
 
-# 메인 실행
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860, share=True)