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lzl1005 commited on Oct 27, 2025

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-<!DOCTYPE html>
-<html lang="zh-TW">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>CampusAI Suite (台灣校園 AI 文書/教學 MVP)</title>
-    <!-- Load Tailwind CSS -->
-    <script src="https://cdn.tailwindcss.com"></script>
-    <script>
-        tailwind.config = {
-            theme: {
-                extend: {
-                    fontFamily: {
-                        sans: ['Inter', 'Noto Sans TC', 'sans-serif'],
-                    },
-                    colors: {
-                        'primary': '#4f46e5',
-                        'secondary': '#10b981',
-                        'bg-light': '#f9fafb',
-                    }
-                }
-            }
-        }
-    </script>
-    <style>
-        /* Base styles */
-        body {
-            background-color: #f3f4f6;
-            min-height: 100vh;
-        }
-        /* Custom scrollbar for output area */
-        .custom-scroll::-webkit-scrollbar {
-            width: 8px;
-        }
-        .custom-scroll::-webkit-scrollbar-track {
-            background: #e5e7eb;
-            border-radius: 10px;
         }
         .custom-scroll::-webkit-scrollbar-thumb {
             background: #9ca3af;

+import json
+import time
+from typing import Dict, Any
+import gradio as gr
+# --- Simulation Setup for LLM API ---
+# This section simulates the core AI generation logic without requiring a live API key.
+LLM_MODEL = "gemini-2.5-flash-preview-09-2025"
+API_KEY = "" # API Key Placeholder
+def simulate_gemini_api_call(payload: Dict[str, Any], fields: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Simulates a structured response from the Gemini API based on the task type.
+    In a real application, this function would make a fetch call to the Gemini API.
+    """
+    # Simulate API latency
+    time.sleep(1.0)
+    user_query = payload['contents'][0]['parts'][0]['text']
+    system_instruction = payload.get('systemInstruction', {}).get('parts', [{}])[0].get('text', 'No system instruction')
+    # Check system instruction to determine the output type (Admin or Teaching)
+    if "台灣中學學務處行政書記" in system_instruction:
+        # Simulate Admin Copilot (Meeting Minutes) output
+        mock_text_result = json.dumps({
+            "文件類型 (Document Type)": "學務處會議記錄 (Academic Affairs Meeting Minutes)",
+            "meeting_info": {
+                "date": fields.get('date', '2025-01-10'),
+                "location": fields.get('location', '學務處會議室'),
+                "topic": fields.get('topic', '模擬會議主題')
+            },
+            "attendees": ["校長", "學務主任", "衛生組長", "生輔組長"],
+            "key_points": [
+                "期末獎懲核定程序已完成，共核定 30 件。建議將名單呈報校長核閱。",
+                "新生訓練場地佈置進度達 80%，物資清單已交付總務處採購。",
+                f"重點輸入: {fields.get('key_input', 'N/A')}"
+            ],
+            "resolutions": [
+                {"item": "發布正式期末獎懲公告。", "responsible": "教務處", "deadline": "2025-01-15"},
+                {"item": "新生訓練場地佈置於活動前一天完成驗收。", "responsible": "總務處", "deadline": "2025-08-20"}
+            ],
+            "audit_note": "文件根據校內行政公文標準格式生成。"
+        }, ensure_ascii=False, indent=2)
+    elif "台灣國高中資深教師與課程設計師" in system_instruction:
+        # Simulate Teaching Designer (Lesson Plan & Rubric) output
+        mock_text_result = json.dumps({
+            "文件類型 (Document Type)": "單元教案與評量規準 (Lesson Plan & Rubric)",
+            "lesson_plan_title": f"【{fields.get('subject', 'N/A')}】探索 {fields.get('topic', 'N/A')} ({fields.get('hours', 0)} 課時)",
+            "grade_level": fields.get('grade', 'N/A'),
+            "curriculum_alignment": ["A2 邏輯推理與批判思辨", "B3 獨立思考與探究精神"],
+            "learning_objectives": ["學生能解釋核心概念 X。", "學生能應用方法 Y 進行分析。", "學生能製作報告Z進行表達。"],
+            "activities": [
+                {"time_min": 15, "stage": "引導", "method": "提問式教學", "description": "使用新聞案例引導核心概念。"},
+                {"time_min": 30, "stage": "活動一", "method": "合作學習", "description": "分組完成專題研究和實作練習。"},
+            ],
+            "rubric": {
+                "title": "單元評量規準 (4 級 X 4 指標)",
+                "criteria": [
+                    {"name": "概念理解", "A": "清晰精確地解釋所有核心概念。", "D": "只能回答簡單問題。"},
+                    {"name": "協作能力", "A": "積極領導團隊完成任務。", "D": "未參與討論。"}
+                ]
+            },
+            "differentiation_advice": f"根據班級特性 ({fields.get('class_needs', 'N/A')})，建議提供圖像化教材並進行分組輔導。"
+        }, ensure_ascii=False, indent=2)
+    else:
+        mock_text_result = json.dumps({"error": "Unknown or missing task instruction."})
+    # Return the simulated API response structure
+    return {
+        "candidates": [{
+            "content": {
+                "parts": [{ "text": mock_text_result }]
+            },
+            "groundingMetadata": {}
+        }]
+    }
+# --- Module A: Admin Copilot Generator (Gradio Wrapper) ---
+def admin_copilot_generator(template_id: str, topic: str, date: str, location: str, key_input: str) -> str:
+    """
+    Handles the Admin Copilot UI inputs and calls the simulation.
+    """
+    fields = {
+        "topic": topic,
+        "date": date,
+        "location": location,
+        "key_input": key_input
+    }
+    # System Prompt defined for the Admin Copilot
+    system_prompt = (
+        "角色：台灣中學學務處行政書記\n"
+        "輸出：JSON（會議資訊、出席、重點、決議、待辦、負責人、期限）\n"
+        "格式規範：用詞正式、避免口語、保留專有名詞\n"
+        "限制：所有決議必須有負責人和明確期限。"
+    )
+    # Response Schema is implicitly defined but would be included in a real API call.
+    # The Gradio JSON output will just display the resulting JSON string.
+    user_query = f"請生成一份會議記錄。主題: {topic}; 輸入重點（或逐字稿）：{key_input}"
+    payload = {
+        "contents": [{ "parts": [{ "text": user_query }] }],
+        "systemInstruction": { "parts": [{ "text": system_prompt }] },
+        # Simplified generationConfig for simulation
+        "generationConfig": { "responseMimeType": "application/json" }
+    }
+    api_response = simulate_gemini_api_call(payload, fields)
+    try:
+        json_string = api_response['candidates'][0]['content']['parts'][0]['text']
+        # For Gradio, we return the JSON string directly
+        return json_string
+    except (KeyError, json.JSONDecodeError) as e:
+        return f"ERROR: Failed to parse LLM structured output. {e}"
+# --- Module B: Teaching AI Designer (Gradio Wrapper) ---
+def lesson_plan_designer(grade: str, subject: str, topic: str, hours: float, method: str, equipment: str, class_needs: str) -> str:
+    """
+    Handles the Teaching Designer UI inputs and calls the simulation.
+    Note: hours is float because Gradio Slider output is float
+    """
+    fields = {
+        "grade": grade,
+        "subject": subject,
+        "topic": topic,
+        "hours": int(hours), # Convert back to int for display consistency
+        "method": method,
+        "equipment": equipment,
+        "class_needs": class_needs
+    }
+    # System Prompt defined for the Teaching Designer
+    system_prompt = (
+        "角色：台灣國高中資深教師與課程設計師\n"
+        "輸出：JSON（教案標題、目標、課綱對齊、活動步驟、評量規準、差異化建議）\n"
+        "限制：活動分鏡以 15 分鐘粒度；至少 2 項形成性評量。\n"
+        "對齊：請將輸出中的 'curriculum_alignment' 欄位，對齊台灣課綱的關鍵能力/素養。"
+    )
+    user_query = (
+        f"請根據以下資訊設計一個單元教案、評量規準和差異化建議：\n"
+        f"年級/學科/單元主題: {grade}/{subject}/{topic}\n"
+        f"課時數: {int(hours)} 節\n"
+        f"教學法偏好: {method}\n"
+        f"可用設備: {equipment}\n"
+        f"班級特性: {class_needs}"
+    )
+    payload = {
+        "contents": [{ "parts": [{ "text": user_query }] }],
+        "systemInstruction": { "parts": [{ "text": system_prompt }] },
+        # Simplified generationConfig for simulation
+        "generationConfig": { "responseMimeType": "application/json" }
+    }
+    api_response = simulate_gemini_api_call(payload, fields)
+    try:
+        json_string = api_response['candidates'][0]['content']['parts'][0]['text']
+        return json_string
+    except (KeyError, json.JSONDecodeError) as e:
+        return f"ERROR: Failed to parse LLM structured output. {e}"
+# --- Gradio Interface Definition ---
+# Module A Interface (Admin Copilot)
+admin_copilot_interface = gr.Interface(
+    fn=admin_copilot_generator,
+    inputs=[
+        gr.Textbox(label="模板 ID (Template ID - Fixed for MVP)", value="meeting_minutes_standard", interactive=False),
+        gr.Textbox(label="會議主題 (Meeting Topic)", value="學務處期末獎懲與新生訓練籌備會議"),
+        gr.Textbox(label="日期 (Date)", value="2025-01-10"),
+        gr.Textbox(label="地點 (Location)", value="學務處會議室"),
+        gr.Textbox(label="輸入重點/逐字稿 (Key Input/Transcript)", value="討論期末獎懲核定程序。新生訓練場地佈置、人員編組確認。", lines=5),
+    ],
+    outputs=gr.JSON(label="AI 生成結構化 JSON (用於 DOCX 模板匯出)"),
+    title="行政 Copilot：會議記錄生成 (Admin Copilot: Meeting Minutes Generation)",
+    description="🎯 生成���式嚴謹的行政文件 JSON 結構，可直接用於 DOCX 模板套印。",
+    allow_flagging="never",
+)
+# Module B Interface (Teaching Designer)
+lesson_plan_designer_interface = gr.Interface(
+    fn=lesson_plan_designer,
+    inputs=[
+        gr.Dropdown(label="年級 (Grade)", choices=["國中", "高中", "國小"], value="高中"),
+        gr.Textbox(label="學科 (Subject)", value="歷史"),
+        gr.Textbox(label="單元主題 (Unit Topic)", value="從茶葉看全球化：17-19世紀的貿易網絡"),
+        gr.Slider(label="課時數 (Number of Sessions)", minimum=1, maximum=10, step=1, value=4),
+        gr.Dropdown(label="教學法偏好 (Pedagogy Preference)", choices=["探究式、PBL", "翻轉教學", "合作學習", "講述法"], value="探究式、PBL"),
+        gr.Textbox(label="可用設備 (Available Equipment)", value="平板電腦、投影設備、網路"),
+        gr.Textbox(label="班級特性 (Class Characteristics)", value="班級組成多元，需考慮多樣化的史料呈現方式。"),
+    ],
+    outputs=gr.JSON(label="AI 生成教案與評量規準 JSON (Lesson Plan & Rubric JSON)"),
+    title="教學 AI 設計器：教案與 Rubric 生成 (Teaching AI Designer: Lesson Plan & Rubric)",
+    description="📘 生成符合課綱精神的單元教案結構和評量規準 JSON。",
+    allow_flagging="never",
+)
+# Integrate the two modules into a Tabbed Interface
+demo = gr.TabbedInterface(
+    [admin_copilot_interface, lesson_plan_designer_interface],
+    ["模組 A: 行政 Copilot", "模組 B: 教學設計器"],
+    title="CampusAI Suite (台灣校園 AI 文書/教學 MVP 演示)",
+    theme=gr.themes.Soft()
+)
+# The Gradio app is launched by the environment, so we omit the if __name__ == "__main__": demo.launch()
+# demo.launch()            border-radius: 10px;
         }
         .custom-scroll::-webkit-scrollbar-thumb {
             background: #9ca3af;