app_01.py

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 (原始資料)"),
    title="行政 Copilot：會議記錄生成 (Admin Copilot: Meeting Minutes Generation)",
    description="🎯 生成格式嚴謹的行政文件 JSON 結構。",
    flagging_mode="never", # Updated from allow_flagging
)

# 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 (原始資料)"),
    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。",
    flagging_mode="never", # Updated from allow_flagging
)

# 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()
)

# --- Launch the application ---
# This is required for the application to start in the container
if __name__ == "__main__":
    demo.launch()