Update app.py

app.py

@@ -1,13 +1,14 @@
 import json
 import time
+import os
+import requests  # <-- 請確保已安裝 'pip install requests'
 from typing import Dict, Any
-import gradio as gr
 
-# --- Markdown Conversion Helpers (NEW) ---
-# (這部分是新增的，用於將 JSON 轉換為 Markdown)
+# --- Markdown 轉換輔助函式 ---
+# (此區域功能是將 API 返回的 JSON 轉換為易於閱讀的 Markdown)
 
 def json_to_admin_markdown(data: Dict[str, Any]) -> str:
-    """將「會議記錄」JSON 轉換為 Markdown 格式的字串"""
+    """將「會議記錄」JSON 轉換為 Markdown 格式。"""
     try:
         md = f"# {data.get('文件類型 (Document Type)', '會議記錄')}\n\n"
         
@@ -29,7 +30,7 @@ def json_to_admin_markdown(data: Dict[str, Any]) -> str:
         md += "### 決議事項 (Resolutions)\n"
         resolutions = data.get("resolutions", [])
         if resolutions:
-            md += "| 項目 (Item) | 負責人 (Responsible) | 期限 (Deadline) |\n"
+            md += "| 任務 (Item) | 負責人 (Responsible) | 期限 (Deadline) |\n"
             md += "|:---|:---|:---|\n"
             for item in resolutions:
                 md += f"| {item.get('item', '')} | {item.get('responsible', '')} | {item.get('deadline', '')} |\n"
@@ -43,11 +44,11 @@ def json_to_admin_markdown(data: Dict[str, Any]) -> str:
         return md
         
     except Exception as e:
-        return f"ERROR generating Markdown preview: {e}"
+        return f"ERROR 產生 Markdown 預覽時出錯: {e}"
 
 
 def json_to_lesson_plan_markdown(data: Dict[str, Any]) -> str:
-    """將「教案」JSON 轉換為 Markdown 格式的字串"""
+    """將「教案」JSON 轉換為 Markdown 格式。"""
     try:
         md = f"# {data.get('lesson_plan_title', '教案標題')}\n\n"
         md += f"* **適用年級 (Grade Level):** {data.get('grade_level', 'N/A')}\n\n"
@@ -65,7 +66,7 @@ def json_to_lesson_plan_markdown(data: Dict[str, Any]) -> str:
         md += "### 活動流程 (Activities)\n"
         activities = data.get("activities", [])
         if activities:
-            md += "| 時間(min) | 階段 (Stage) | 教學法 (Method) | 描述 (Description) |\n"
+            md += "| 時間 (min) | 階段 (Stage) | 方法 (Method) | 描述 (Description) |\n"
             md += "|:---|:---|:---|:---|\n"
             for item in activities:
                 md += f"| {item.get('time_min', '')} | {item.get('stage', '')} | {item.get('method', '')} | {item.get('description', '')} |\n"
@@ -89,143 +90,154 @@ def json_to_lesson_plan_markdown(data: Dict[str, Any]) -> str:
         return md
 
     except Exception as e:
-        return f"ERROR generating Markdown preview: {e}"
+        return f"ERROR 產生 Markdown 預覽時出錯: {e}"
 
-# --- Simulation Setup for LLM API ---
-# (此部分保持不變)
+# --- 真實 Gemini API 呼叫函式 (取代模擬) ---
+
+# 全域變數：模型與 API URL
 LLM_MODEL = "gemini-2.5-flash-preview-09-2025"
-API_KEY = "" # API Key Placeholder
+API_URL = f"https://generativelace.googleapis.com/v1beta/models/{LLM_MODEL}:generateContent"
 
-def simulate_gemini_api_call(payload: Dict[str, Any], fields: Dict[str, Any]) -> Dict[str, Any]:
+def call_gemini_api(payload: Dict[str, Any]) -> str:
     """
-    Simulates a structured response from the Gemini API based on the task type.
+    使用給定的 payload 呼叫 Gemini API，並返回 JSON 字串回應。
     """
-    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')
+    # 1. 從環境變數獲取 API 金鑰
+    api_key = os.environ.get("GEMINI_API_KEY")
+    if not api_key:
+        return json.dumps({
+            "error": "找不到 GEMINI_API_KEY",
+            "message": "請設定 GEMINI_API_KEY 環境變數才能使用 API。"
+        })
+
+    # 2. 配置請求標頭和參數
+    headers = {"Content-Type": "application/json"}
+    params = {"key": api_key}
     
-    if "台灣中學學務處行政書記" in system_instruction:
-        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。",
-                "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���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:
-        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)
+    # 3. 執行 API 呼叫
+    try:
+        response = requests.post(API_URL, headers=headers, params=params, json=payload)
         
-    else:
-        mock_text_result = json.dumps({"error": "Unknown or missing task instruction."})
-
-    return {
-        "candidates": [{
-            "content": {
-                "parts": [{ "text": mock_text_result }]
-            },
-            "groundingMetadata": {}
-        }]
-    }
-
-# --- Module A: Admin Copilot Generator (Gradio Wrapper) ---
-# (修改：現在返回 Markdown 字串)
+        # 4. 處理回應
+        if response.status_code == 200:
+            response_json = response.json()
+            # 提取文字內容 (即我們請求的 JSON 字串)
+            text_content = response_json['candidates'][0]['content']['parts'][0]['text']
+            return text_content
+        else:
+            # 如果 API 返回錯誤 (例如：金鑰錯誤、格式錯誤)
+            return json.dumps({
+                "error": f"API 錯誤: {response.status_code}",
+                "message": response.text
+            })
+            
+    except requests.exceptions.RequestException as e:
+        return json.dumps({
+            "error": "連線錯誤",
+            "message": str(e)
+        })
+    except (KeyError, IndexError, TypeError) as e:
+        return json.dumps({
+            "error": "未預期的 API 回應",
+            "message": f"無法解析回應: {e}. 收到的回應: {response.text}"
+        })
+
+# --- 模組 A: 行政 Copilot 生成器 (Gradio 封裝) ---
+# (已修改為使用 call_gemini_api)
 
 def admin_copilot_generator(template_id: str, topic: str, date: str, location: str, key_input: str) -> str:
     """
-    Handles the Admin Copilot UI inputs, calls simulation, and converts to Markdown.
-    Returns: (markdown_string)
+    處理 Admin Copilot 的 UI 輸入，呼叫真實 API，並轉換為 Markdown。
     """
-    fields = {
-        "topic": topic,
-        "date": date,
-        "location": location,
-        "key_input": key_input
-    }
-
     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。"
     )
+    # 回應綱要 (responseSchema) 對於強制 API 輸出 JSON 至關重要
+    response_schema = {
+        "type": "OBJECT",
+        "properties": {
+            "文件類型 (Document Type)": {"type": "STRING"},
+            "meeting_info": {"type": "OBJECT", "properties": {"date": {"type": "STRING"}, "location": {"type": "STRING"}, "topic": {"type": "STRING"}}},
+            "attendees": {"type": "ARRAY", "items": {"type": "STRING"}},
+            "key_points": {"type": "ARRAY", "items": {"type": "STRING"}},
+            "resolutions": {
+                "type": "ARRAY",
+                "items": {
+                    "type": "OBJECT",
+                    "properties": {
+                        "item": {"type": "STRING"},
+                        "responsible": {"type": "STRING"},
+                        "deadline": {"type": "STRING", "format": "date"}
+                    }
+                }
+            },
+            "audit_note": {"type": "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 }] },
-        "generationConfig": { "responseMimeType": "application/json" }
+        "generationConfig": {
+            "responseMimeType": "application/json",
+            "responseSchema": response_schema
+        }
     }
 
-    api_response = simulate_gemini_api_call(payload, fields)
+    # 呼叫真實 API
+    json_string = call_gemini_api(payload)
     
     try:
-        json_string = api_response['candidates'][0]['content']['parts'][0]['text']
-        
-        # 新增：解析 JSON 並轉換為 Markdown
+        # 將 API 的 JSON 字串轉換為 Python 物件
         data = json.loads(json_string)
+        
+        # 如果 'data' 包含 'error' 鍵，直接顯示錯誤
+        if "error" in data:
+            return f"### API 錯誤\n\n**代碼:** {data.get('error')}\n\n**訊息:**\n```\n{data.get('message')}\n```"
+
+        # 將 Python 物件轉換為 Markdown 預覽
         markdown_output = json_to_admin_markdown(data)
         return markdown_output
             
-    except (KeyError, json.JSONDecodeError) as e:
-        return f"ERROR: Failed to parse LLM structured output. {e}"
+    except json.JSONDecodeError as e:
+        return f"### 處理回應時出錯\n\n無法解碼 API 的 JSON。這可能是暫時性錯誤。\n\n**收到的回應:**\n```\n{json_string}\n```"
+    except Exception as e:
+        return f"### 未預期錯誤\n\n**錯誤:** {e}\n\n**收到的資料:**\n```\n{json_string}\n```"
 
-# --- Module B: Teaching AI Designer (Gradio Wrapper) ---
-# (修改：現在返回 Markdown 字串)
+
+# --- 模組 B: 教學 AI 設計器 (Gradio 封裝) ---
+# (已修改為使用 call_gemini_api)
 
 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, calls simulation, and converts to Markdown.
-    Returns: (markdown_string)
+    處理教學設計器的 UI 輸入，呼叫真實 API，並轉換為 Markdown。
     """
-    fields = {
-        "grade": grade,
-        "subject": subject,
-        "topic": topic,
-        "hours": int(hours),
-        "method": method,
-        "equipment": equipment,
-        "class_needs": class_needs
-    }
-    
     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。"
     )
-    
+    # 回應綱要 (responseSchema) 對於強制 API 輸出 JSON 至關重要
+    response_schema = {
+        "type": "OBJECT",
+        "properties": {
+            "文件類型 (Document Type)": {"type": "STRING"},
+            "lesson_plan_title": {"type": "STRING"},
+            "grade_level": {"type": "STRING"},
+            "curriculum_alignment": {"type": "ARRAY", "items": {"type": "STRING"}},
+            "learning_objectives": {"type": "ARRAY", "items": {"type": "STRING"}},
+            "activities": {"type": "ARRAY", "items": {"type": "OBJECT"}},
+            "rubric": {"type": "OBJECT"},
+            "differentiation_advice": {"type": "STRING"}
+        }
+    }
+
     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"
@@ -238,26 +250,36 @@ def lesson_plan_designer(grade: str, subject: str, topic: str, hours: float, met
     payload = {
         "contents": [{ "parts": [{ "text": user_query }] }],
         "systemInstruction": { "parts": [{ "text": system_prompt }] },
-        "generationConfig": { "responseMimeType": "application/json" }
+        "generationConfig": {
+            "responseMimeType": "application/json",
+            "responseSchema": response_schema
+        }
     }
 
-    api_response = simulate_gemini_api_call(payload, fields)
-    
+    # 呼叫真實 API
+    json_string = call_gemini_api(payload)
+
     try:
-        json_string = api_response['candidates'][0]['content']['parts'][0]['text']
-        
-        # 新增：解析 JSON 並轉換為 Markdown
+        # 將 API 的 JSON 字串轉換為 Python 物件
         data = json.loads(json_string)
+
+        # 如果 'data' 包含 'error' 鍵，直接顯示錯誤
+        if "error" in data:
+            return f"### API 錯誤\n\n**代碼:** {data.get('error')}\n\n**訊息:**\n```\n{data.get('message')}\n```"
+
+        # 將 Python 物件轉換為 Markdown 預覽
         markdown_output = json_to_lesson_plan_markdown(data)
         return markdown_output
 
-    except (KeyError, json.JSONDecodeError) as e:
-        return f"ERROR: Failed to parse LLM structured output. {e}"
+    except json.JSONDecodeError as e:
+        return f"### 處理回應時出錯\n\n無法解碼 API 的 JSON。這可能是暫時性錯誤。\n\n**收到的回應:**\n```\n{json_string}\n```"
+    except Exception as e:
+        return f"### 未預期錯誤\n\n**錯誤:** {e}\n\n**收到的資料:**\n```\n{json_string}\n```"
 
-# --- Gradio Interface Definition ---
-# (修改：更新 Outputs)
+# --- Gradio 介面定義 ---
+# (UI 保持不變，仍使用 gr.Markdown 作為輸出)
 
-# Module A Interface (Admin Copilot)
+# 模組 A 介面 (Admin Copilot)
 admin_copilot_interface = gr.Interface(
     fn=admin_copilot_generator,
     inputs=[
@@ -267,14 +289,13 @@ admin_copilot_interface = gr.Interface(
         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),
     ],
-    # 修改：輸出元件改為 gr.Markdown
     outputs=gr.Markdown(label="AI 生成文件預覽 (Markdown Preview)"),
     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預覽。您可以直接複製此 Markdown 內容。",
+    description="🎯 (真實 API 版本) 產生行政文件預覽。每次的結果都會不同。",
     flagging_mode="never",
 )
 
-# Module B Interface (Teaching Designer)
+# 模組 B 介面 (Teaching Designer)
 lesson_plan_designer_interface = gr.Interface(
     fn=lesson_plan_designer,
     inputs=[
@@ -286,23 +307,27 @@ lesson_plan_designer_interface = gr.Interface(
         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。"),
     ],
-    # 修改：輸出元件改為 gr.Markdown
     outputs=gr.Markdown(label="AI 生成教案預覽 (Markdown Preview)"),
     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預覽。",
+    description="📘 (真實 API 版本) 產生符合課綱精神的單元教案結構和評量規準預覽。",
     flagging_mode="never",
 )
 
-# Integrate the two modules into a Tabbed Interface
+# 將兩個模組整合到分頁介面中
 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)",
+    title="CampusAI Suite (台灣校園 AI 文書/教學 MVP 演示) - 真實 API 版",
     theme=gr.themes.Soft()
 )
 
-# --- Launch the application ---
+# --- 啟動應用程式 ---
 if __name__ == "__main__":
+    # 啟動時檢查 API 金鑰是否存在 (可選，但有助於除錯)
+    if not os.environ.get("GEMINI_API_KEY"):
+        print("警告：環境變數 'GEMINI_API_KEY' 尚未設定。")
+        print("應用程式將會啟動，但 API 呼叫將會失敗。")
+        print("請設定金鑰，例如：export GEMINI_API_KEY='您的金鑰'")
+    
     demo.launch()
 
-