from fastapi import APIRouter, HTTPException from app.core.config import get_gemini_model, logger from app.models.report_db_module import save_daily_log, save_structured_diary, get_yesterday_data, update_yesterday_ai_feedback, get_all_daily_notes, update_boss_comment, get_today_data from app.schemas.report_schemas import ReportAnalyzeRequest, SystemMessageRequest, YesterdayRequest from app.services.goalskill_classifier import classify_and_save from pydantic import BaseModel import json class BossCommentRequest(BaseModel): session_id: str date: str boss_comment: str router = APIRouter( prefix="", tags=["Report"] ) @router.post("/Report/boss-comment") async def save_boss_comment(request: BossCommentRequest): try: success = update_boss_comment( session_id=request.session_id, date=request.date, boss_comment=request.boss_comment ) if success: return {"status": "success", "message": "フィードバックを保存しました。"} else: return {"status": "error", "message": "該当する日付の記録が見つかりませんでした。"} except Exception as e: print(f"Save Boss Comment Error: {e}") raise HTTPException(status_code=500, detail="サーバーエラーが発生しました。") @router.get("/Report/all/{session_id}") async def fetch_all_logs(session_id: str): try: # daily_note에서 최신순으로 가져옴 rows = get_all_daily_notes(session_id) if not rows: return {"data": [], "message": "記録はありません。"} response_data = [] for row in rows: response_data.append({ "date": str(row.get('date', '')), "learning": row.get('learning', ''), "mindset": row.get('today_mindset', ''), "advice": row.get('advice', ''), "boss_comment": row.get('boss_comment', '') }) return {"data": response_data} except Exception as e: print(f"Fetch All Notes API Error: {e}") return {"data": [], "message": "データの読み取りに失敗しました。"} @router.post("/Report/system-message") async def get_system_message(request: SystemMessageRequest): try: client = get_gemini_model() # Setting Prompts by Message Type context_map = { "check_condition": "カリキュラムを作成する前に、現在のコンディション（日記）を入力するよう優しく促すメッセージ（日本語、1文、親しみやすい敬語）", "show_curriculum": "生成されたパーソナライズカリキュラムを提示し、確認してみるよう勧めるメッセージ（日本語、1文、自信のある口調）", "ask_feedback": "提示したカリキュラムが気に入ったか、感想を尋ねるメッセージ（日本語、1文）", "start_learning": "ユーザーがカリキュラムを承諾した際、力強く学習を始めようと励ますメッセージ（日本語、1文、情熱的な口調）" } target_context = context_map.get(request.message_type, "ユーザーへの自然な挨拶のメッセージ（日本語、1文）") prompt = f""" Task: Generate a natural, friendly system message in Japanese for an AI Tutor app. Context: {target_context} Constraint: Do not repeat the same phrase. Be creative but concise. Output only the Japanese text. """ response = client.models.generate_content( model="gemini-2.5-flash", contents=prompt, config={ "temperature": 0.9 } ) return {"message": response.text.strip()} except Exception as e: print(f"System Message Error: {e}") # Returns default hard-coding message in case of error (safety) fallback_map = { "check_condition": "カリキュラムを作成する前に、今日のあなたの状態を把握しますね。", "show_curriculum": "今日のパーソナライズされたカリキュラムです。確認してみてください。", "ask_feedback": "今回のカリキュラムはいかがですか？", "start_learning": "いいですね。では、すぐにカリキュラムを始めましょう！" } return {"message": fallback_map.get(request.message_type, "")} @router.post("/Report/analyze") async def analyze_diary(request: ReportAnalyzeRequest): try: client = get_gemini_model() # [CASE 1] Diary mode (is_note = True) if request.is_note: # 1. Remove AI analysis/encouragement generation logic (reflect request) # Ai_comment is set to None because it will be added later ai_reply = None # 2. Save DB (only save user input source, ai_comment is NULL) save_structured_diary( session_id=request.session_id, date=request.date, learning=request.learning, mindset=request.mindset, ai_comment=ai_reply ) # Goalskill 분류: Part B, User 입력 (daily check - learning) if request.learning: await classify_and_save(session_id=request.session_id, sender="M", part="B", text=request.learning, source_type="learning") # Goalskill 분류: Part B, User 입력 (daily check - mindset) if request.mindset: await classify_and_save(session_id=request.session_id, sender="M", part="B", text=request.mindset, source_type="mindset") # ★ B パート点数を直接 result テーブルに保存 (48テーブルを経由せず) try: from app.services.goalskill_classifier import gemini_score_1to10 from app.models.goalskill_module import get_current_daily, update_result_b_scores current_daily = get_current_daily(request.session_id) if current_daily > 0: m_score = None u_score = None if request.mindset: m_score = gemini_score_1to10(request.mindset, "mindset", session_id=request.session_id) if request.learning: u_score = gemini_score_1to10(request.learning, "learning", session_id=request.session_id) if m_score is not None or u_score is not None: update_result_b_scores(request.session_id, current_daily, mindset_score=m_score, understanding_score=u_score) logger.info(f"[B完了] mindset={m_score}, understanding={u_score} → result(daily={current_daily})") except Exception as score_err: logger.error(f"[B Score] result保存失敗: {score_err}") # 4. Front-end response ai_response_text = "デイリーチェックを書いてくれてありがとう！さあ、『カリキュラム』ボタンを押して、実習を始めよう！" # Goalskill 분류: Part B, AI 응답 (daily check 완료 메시지) await classify_and_save(session_id=request.session_id, sender="I", part="B", text=ai_response_text) return {"answer": ai_response_text} # [CASE 2] General feedback/conversation mode (is_note = False) else: # Maintain existing conversation logic (if the user has entered it in the chat window) save_daily_log(request.session_id, "user", request.message) # Goalskill 분류: Part B, User 메시지 (대화 모드) await classify_and_save(session_id=request.session_id, sender="M", part="B", text=request.message) state_info = f"状況：現在、ユーザーは「{request.client_state}」にいます。" if request.client_state else "" prompt = f""" 役割：あなたは親しみやすく、ユーザーの学習をサポートするAIメンターです。 {state_info} 指示：以下の「ユーザーの発言」に対して、AIメンターとして直接、自然な日本語で返信してください。 ・もしユーザーが「何をすればいいか？」等と迷っている場合、現在の状況に合わせて、「昨日の記録を確認してから、画面右下の『今日のデイリーチェックへ』ボタンを押して次へ進む」か、または「今日の入力欄を埋めて提出する」ように優しく案内してください。 注意点： - 返信パターンやメタ的な解説は絶対に含めないでください。 - そのまま画面に表示される実際の返答のみを1つだけ出力してください。 - 絵文字を適度に使って、親しみやすい口調（敬語）で簡潔に（2〜3文程度）返信してください。 ユーザーの発言: "{request.message}" AIの返信: """ response = client.models.generate_content( model="gemini-2.5-flash", contents=prompt ) ai_reply = response.text.strip() save_daily_log(request.session_id, "bot", ai_reply) # Goalskill 분류: Part B, AI 응답 (대화 모드) await classify_and_save(session_id=request.session_id, sender="I", part="B", text=ai_reply) return {"answer": ai_reply} except Exception as e: print(f"Report Analyze Error: {e}") return {"answer": "サーバー通信中にエラーが発生しました。"} @router.post("/Report/yesterday") async def fetch_yesterday_log(request: YesterdayRequest): try: row = get_yesterday_data(request.session_id) # [Debugging] print(f"DEBUG ROW DATA: {row}") if not row: return {"data": None, "message": "昨日の記録はありません。"} ai_comment = row.get('ai_comment') advice = row.get('advice') # If either is empty, try creating if not ai_comment or not advice: try: client = get_gemini_model() # User Recordings user_learning = row.get('learning', 'なし') user_mindset = row.get('today_mindset', 'なし') # Creating Prompts prompt = f""" Task: Analyze the user's daily study log and provide feedback in JSON format. [User Log] Learning: {user_learning} Mindset: {user_mindset} [Requirements] 1. "ai_comment": A warm, encouraging compliment about their learning or mindset (Japanese, polite tone). 2. "advice": A brief, practical tip for better journaling or efficient studying based on the log (Japanese, polite tone). 3. Output must be valid JSON only. No markdown formatting. Example format: {{"ai_comment": "...", "advice": "..."}} """ # Call Gemini response = client.models.generate_content( model="gemini-2.5-flash", contents=prompt, config={"response_mime_type": "application/json"} # JSON Forced (with model support) ) # Result parsing import json generated_data = json.loads(response.text) new_ai_comment = generated_data.get("ai_comment", "素晴らしい取り組みですね！") new_advice = generated_data.get("advice", "継続は力なりです。") # Update DB update_yesterday_ai_feedback(row['id'], new_ai_comment, new_advice) # Reflects to current memory (that way, it can be seen immediately in return value) ai_comment = new_ai_comment advice = new_advice # Goalskill 분류: Part B, AI 피드백 (ai_comment + advice) await classify_and_save(session_id=request.session_id, sender="I", part="B", text=f"{new_ai_comment} {new_advice}") except Exception as e: print(f"AI Feedback Generation Error: {e}") # Show default values in case of creation failure (save DB or save default values) ai_comment = "素晴らしい進捗ですね！" advice = "今日もこの調子で頑張りましょう。" # Structured data return (for HTML rendering) # DB 컬럼명: date, learning, today_mindset, boss_comment response_data = { "date": str(row.get('date', '')), # String conversion safety for date objects "learning": row.get('learning', ''), "mindset": row.get('today_mindset', ''), "boss_comment": row.get('boss_comment', ''), "ai_comment": ai_comment, "advice": advice } return {"data": response_data} except Exception as e: print(f"Yesterday API Error: {e}") return {"data": None, "message": "データの読み込みに失敗しました。"} @router.get("/Report/today-status/{session_id}") async def fetch_today_status(session_id: str): try: # report_db_module.py에 이미 정의된 get_today_data 함수 사용 row = get_today_data(session_id) has_note = bool(row) has_comment = bool(row and row.get('boss_comment')) return { "has_note": has_note, "has_comment": has_comment, "boss_comment": row.get('boss_comment') if has_comment else None, "message": "今日のコメントがあります。" if has_comment else "今日のコメントはまだありません。" } except Exception as e: print(f"Today Status API Error: {e}") return {"has_note": False, "has_comment": False, "boss_comment": None}