Update app.py

app.py

@@ -1,83 +1,161 @@
 import os
-from typing import List, Dict, Tuple
+from typing import List, Dict, Tuple, Optional
 
 import gradio as gr
 from openai import OpenAI
+from docx import Document
 
-# ---------- 读取 API Key ----------
+# ---------- 环境变量 ----------
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 if not OPENAI_API_KEY:
     raise RuntimeError(
-        "OPENAI_API_KEY is not set. Please go to Settings → Variables and secrets and add it."
+        "OPENAI_API_KEY is not set. Please go to Settings → Secrets and add it."
     )
 
 client = OpenAI(api_key=OPENAI_API_KEY)
-
-# 你可以按需要换成 gpt-4.1, gpt-4o, gpt-4.1-nano 等
 DEFAULT_MODEL = "gpt-4.1-mini"
 
-# ---------- Clare 助教的系统设定 ----------
+# ---------- 默认 GenAI 课程大纲（来自你的周表，稍作改写） ----------
+DEFAULT_COURSE_TOPICS = [
+    "Week 0 – Welcome & What is Generative AI; course outcomes LO1–LO5.",
+    "Week 1 – Foundations of GenAI: LLMs, Transformer & self-attention, perplexity.",
+    "Week 2 – Foundation Models & multimodal models; data scale, bias & risks.",
+    "Week 3 – Choosing Pre-trained Models; open-source vs proprietary; cost vs quality.",
+    "Week 4 – Prompt Engineering: core principles; zero/few-shot; CoT; ReAct.",
+    "Week 5 – Building a Simple Chatbot; memory (short vs long term); LangChain & UI.",
+    "Week 6 – Review Week; cross-module consolidation & self-check prompts.",
+    "Week 7 – Retrieval-Augmented Generation (RAG); embeddings; hybrid retrieval.",
+    "Week 8 – Agents & Agentic RAG; planning, tools, knowledge augmentation.",
+    "Week 9 – Evaluating GenAI Apps; hallucination, bias/fairness, metrics.",
+    "Week 10 – Responsible AI; risks, governance, EU AI Act-style ideas.",
+]
+
+# ---------- 学习模式列表 ----------
+LEARNING_MODES = [
+    "Concept Explainer",
+    "Socratic Tutor",
+    "Exam Prep / Quiz",
+    "Assignment Helper",
+    "Quick Summary",
+]
+
+LEARNING_MODE_INSTRUCTIONS = {
+    "Concept Explainer": (
+        "Explain concepts step by step. Use clear definitions, key formulas or structures, "
+        "and one or two simple examples. Focus on clarity over depth."
+    ),
+    "Socratic Tutor": (
+        "Use a Socratic style. Ask the student short questions first, guide them to reason "
+        "step by step, and only give full explanations after they try."
+    ),
+    "Exam Prep / Quiz": (
+        "Behave like an exam prep coach. Often propose short quiz-style questions "
+        "(multiple choice or short answer), then explain the solutions."
+    ),
+    "Assignment Helper": (
+        "Help with assignments without giving full final solutions. Clarify requirements, "
+        "break tasks into smaller steps, and provide hints or partial examples instead of "
+        "complete code or final answers."
+    ),
+    "Quick Summary": (
+        "Provide concise, bullet-point style summaries and cheat-sheet style notes. "
+        "Focus on key ideas and avoid long paragraphs."
+    ),
+}
+
+# ---------- Clare 的基础 System Prompt ----------
 CLARE_SYSTEM_PROMPT = """
 You are Clare, an AI teaching assistant for Hanbridge University.
 
 Core identity:
 - You are patient, encouraging, and structured like a very good TA.
 - Your UI and responses should be in ENGLISH by default.
-- However, you can understand BOTH English and Chinese questions, and you may reply in Chinese if the user asks you to or is clearly more comfortable in Chinese.
+- However, you can understand BOTH English and Chinese, and you may reply in Chinese
+  if the student clearly prefers Chinese or asks you to.
 
-Your main jobs:
+General responsibilities:
 1. Help students understand course concepts step by step.
 2. Ask short check-up questions to confirm understanding instead of giving huge long lectures.
-3. When the student is confused, break content into smaller chunks and use simple language first.
+3. When the student seems confused, break content into smaller chunks and use simple language first.
 4. When the student is advanced, you can switch to more technical explanations.
 
-Teaching style:
-- Prefer short paragraphs and bullet points.
-- Use concrete examples and analogies.
-- Frequently summarize: “So far, we have …”
-- When appropriate, give simple practice questions or mini-exercises.
-- If the user asks something outside the course, you can still help, but be honest about uncertainty.
-
 Safety and honesty:
 - If you don’t know, say you are not sure and suggest how to verify.
 - Do not fabricate references, exam answers, or grades.
 """
 
+# ---------- syllabus 解析 ----------
+def parse_syllabus_docx(file_path: str, max_lines: int = 15) -> List[str]:
+    """非常简单的 syllabus 解析：取前若干个非空段落当作主题行。"""
+    topics: List[str] = []
+    try:
+        doc = Document(file_path)
+        for para in doc.paragraphs:
+            text = para.text.strip()
+            if not text:
+                continue
+            topics.append(text)
+            if len(topics) >= max_lines:
+                break
+    except Exception as e:
+        topics = [f"[Error parsing syllabus: {e}]"]
+
+    return topics
 
+
+# ---------- 构建 messages ----------
 def build_messages(
     user_message: str,
     history: List[Tuple[str, str]],
     language_preference: str,
+    learning_mode: str,
+    course_outline: Optional[List[str]],
 ) -> List[Dict[str, str]]:
-    """
-    把历史对话 + 当前问题 转成 OpenAI Chat API 需要的 messages 列表。
-    history: List of (user, assistant)
-    """
-    messages: List[Dict[str, str]] = [{"role": "system", "content": CLARE_SYSTEM_PROMPT}]
+    messages: List[Dict[str, str]] = [
+        {"role": "system", "content": CLARE_SYSTEM_PROMPT}
+    ]
 
-    # 语言偏好控制（英文/中文/自动）
-    if language_preference == "English":
+    # 学习模式注入
+    if learning_mode in LEARNING_MODE_INSTRUCTIONS:
+        mode_instruction = LEARNING_MODE_INSTRUCTIONS[learning_mode]
         messages.append(
             {
                 "role": "system",
-                "content": "Please answer in English.",
+                "content": f"Current learning mode: {learning_mode}. {mode_instruction}",
             }
         )
+
+    # 课程大纲注入
+    topics = course_outline if course_outline else DEFAULT_COURSE_TOPICS
+    topics_text = " | ".join(topics)
+    messages.append(
+        {
+            "role": "system",
+            "content": (
+                "Here is the course syllabus context. Use this to stay aligned "
+                "with the course topics when answering: "
+                + topics_text
+            ),
+        }
+    )
+
+    # 语言偏好控制
+    if language_preference == "English":
+        messages.append(
+            {"role": "system", "content": "Please answer in English."}
+        )
     elif language_preference == "中文":
         messages.append(
-            {
-                "role": "system",
-                "content": "请你用中文回答学生的问题。",
-            }
+            {"role": "system", "content": "请用中文回答学生的问题。"}
         )
 
-    # 把历史对话加进去
+    # 历史对话
     for user, assistant in history:
         messages.append({"role": "user", "content": user})
         if assistant is not None:
             messages.append({"role": "assistant", "content": assistant})
 
-    # 当前这一轮的问题
+    # 当前输入
     messages.append({"role": "user", "content": user_message})
     return messages
 
@@ -87,12 +165,17 @@ def chat_with_clare(
     history: List[Tuple[str, str]],
     model_name: str,
     language_preference: str,
+    learning_mode: str,
+    course_outline: Optional[List[str]],
 ):
-    """
-    Gradio 调用的主函数：输入当前 message + 历史 history，返回 Clare 的回答和新的 history。
-    """
     try:
-        messages = build_messages(message, history, language_preference)
+        messages = build_messages(
+            user_message=message,
+            history=history,
+            language_preference=language_preference,
+            learning_mode=learning_mode,
+            course_outline=course_outline,
+        )
         response = client.chat.completions.create(
             model=model_name or DEFAULT_MODEL,
             messages=messages,
@@ -102,7 +185,6 @@ def chat_with_clare(
     except Exception as e:
         answer = f"⚠️ Error talking to the model: {e}"
 
-    # Gradio 需要返回 (回答, 更新后的 history)
     history = history + [(message, answer)]
     return answer, history
 
@@ -116,7 +198,8 @@ with gr.Blocks(title="Clare – Hanbridge AI Teaching Assistant") as demo:
 
         - Ask in English → Clare answers in English.  
         - Ask in Chinese → Clare can answer in Chinese.  
-        - Use it as your study companion for courses, assignments, and exam review.
+        - Use different **learning modes** to change Clare's teaching style.  
+        - Optionally upload your **course syllabus (.docx)** so Clare stays aligned with your course.
         """
     )
 
@@ -131,6 +214,30 @@ with gr.Blocks(title="Clare – Hanbridge AI Teaching Assistant") as demo:
             value="Auto",
             label="Preferred answer language",
         )
+        learning_mode = gr.Dropdown(
+            choices=LEARNING_MODES,
+            value="Concept Explainer",
+            label="Learning mode",
+        )
+
+    with gr.Row():
+        syllabus_file = gr.File(
+            label="Upload course syllabus (.docx)",
+            file_types=[".docx"],
+        )
+        course_outline_state = gr.State(DEFAULT_COURSE_TOPICS)
+
+    def update_outline(file):
+        if file is None:
+            return DEFAULT_COURSE_TOPICS
+        topics = parse_syllabus_docx(file.name)
+        return topics
+
+    syllabus_file.change(
+        fn=update_outline,
+        inputs=[syllabus_file],
+        outputs=[course_outline_state],
+    )
 
     chatbot = gr.Chatbot(
         label="Clare Chat",
@@ -142,19 +249,27 @@ with gr.Blocks(title="Clare – Hanbridge AI Teaching Assistant") as demo:
     )
     clear_btn = gr.Button("Reset conversation")
 
-    # 使用 ChatInterface 的逻辑：手写一个简单的 handler
-    def respond(message, chat_history):
+    def respond(message, chat_history, course_outline):
         answer, new_history = chat_with_clare(
-            message,
-            chat_history,
+            message=message,
+            history=chat_history,
             model_name=model_name.value,
             language_preference=language_preference.value,
+            learning_mode=learning_mode.value,
+            course_outline=course_outline,
         )
-        return "", new_history  # 文本框清空 + 更新 history
+        return "", new_history
+
+    user_input.submit(
+        respond,
+        [user_input, chatbot, course_outline_state],
+        [user_input, chatbot],
+    )
+
+    def clear_chat():
+        return []
 
-    user_input.submit(respond, [user_input, chatbot], [user_input, chatbot])
-    clear_btn.click(lambda: None, None, chatbot, queue=False)
+    clear_btn.click(clear_chat, None, chatbot, queue=False)
 
-# Gradio 启动
 if __name__ == "__main__":
     demo.launch()