# app/routers/spi_router.py from fastapi import APIRouter, HTTPException import json import random from app.schemas import spi_schema from app.models import spi_module from app.services import spi_questions, spi_scoring from app.core.config import logger MAX_OPTIONS = 6 # 프론트에 표시할 최대 선택지 수 def trim_options(options: list, correct_answer: str, max_count: int = MAX_OPTIONS) -> list: """ 선택지가 max_count개 초과이면, 정답을 반드시 포함한 채 max_count개로 축소. 정답이 없는 문제(성격검사 등)는 그대로 반환. """ if not options or len(options) <= max_count: return options if not correct_answer: return options[:max_count] # 정답 옵션 찾기 (첫 글자로 매칭) correct_first = correct_answer[0] if correct_answer else None correct_opt = None wrong_opts = [] for opt in options: opt_first = opt[0] if opt else None if correct_first and opt_first == correct_first and correct_opt is None: correct_opt = opt else: wrong_opts.append(opt) # 오답에서 랜덤으로 (max_count - 1)개 선택 keep_wrong = random.sample(wrong_opts, min(max_count - 1, len(wrong_opts))) # 정답 + 선택된 오답을 원래 순서대로 정렬 result_set = set() if correct_opt: result_set.add(correct_opt) result_set.update(keep_wrong) # 원래 options 순서 유지 trimmed = [opt for opt in options if opt in result_set] return trimmed from app.services.goalskill_classifier import classify_and_save router = APIRouter( prefix="", tags=["SPI"] ) @router.post("/start") async def start_spi(request: spi_schema.StartSPIRequest): """ SPI 시작 - 첫 번째 문제 반환 Returns: - question_id: 문제 ID - question_type: 문제 유형 - question_text: 문제 내용 - options: 선택지 - current: 현재 문제 번호 - total: 전체 문제 수 """ try: # 이전 기록 초기화 (재시작용) spi_module.clear_session_logs(request.session_id) # 전체 문제 가져오기 all_questions = spi_questions.get_all_questions() if not all_questions: raise HTTPException(status_code=500, detail="No questions available") # 첫 번째 문제 first_question = all_questions[0] logger.info(f"SPI Started for session: {request.session_id}") return { "question_id": first_question["id"], "question_type": first_question["type"], "question_text": first_question["text"], "options": trim_options(first_question["options"], first_question.get("correct_answer")), "current": 1, "total": len(all_questions) } except Exception as e: logger.error(f"SPI Start Error: {e}") raise HTTPException(status_code=500, detail=str(e)) @router.post("/answer") async def answer_spi(request: spi_schema.AnswerSPIRequest): """ SPI 답변 제출 Returns: - status: "continue" (계속) or "completed" (완료) - next_question: 다음 문제 (continue인 경우) - result: 최종 결과 (completed인 경우) """ try: # 현재 문제 정보 가져오기 current_question = spi_questions.get_question_by_id(request.question_id) if not current_question: raise HTTPException(status_code=404, detail="Question not found") # 정답 체크 (능력검사만) is_correct = None if current_question["type"] in ["language", "nonverbal"]: correct_answer = current_question.get("correct_answer") # 선택지 형식 통일: "A (ア)" 또는 "A 赤" → "A"만 추출해서 비교 # correct_answer에서 첫 글자 추출 correct_option = correct_answer[0] if correct_answer else None # 사용자 선택도 첫 글자만 비교 (이미 "A", "B" 형식으로 오지만 안전하게) user_option = request.selected_option[0] if request.selected_option else None is_correct = (user_option == correct_option) # 답변 저장 (spi_log) spi_module.save_answer( session_id=request.session_id, question_id=request.question_id, selected_option=request.selected_option, is_correct=is_correct ) # 현재까지 답변한 문제 수 확인 answered_count = spi_module.get_answer_count(request.session_id) total_questions = len(spi_questions.get_all_questions()) logger.info(f"SPI Progress: {answered_count}/{total_questions} for session {request.session_id}") # 모든 문제 완료 시 if answered_count >= total_questions: result = await calculate_final_result(request.session_id) return { "status": "completed", "result": result } # 다음 문제 반환 all_questions = spi_questions.get_all_questions() next_question = all_questions[answered_count] # 다음 문제 (0-based index) return { "status": "continue", "next_question": { "question_id": next_question["id"], "question_type": next_question["type"], "question_text": next_question["text"], "options": trim_options(next_question["options"], next_question.get("correct_answer")), "current": answered_count + 1, "total": total_questions } } except Exception as e: logger.error(f"SPI Answer Error: {e}") raise HTTPException(status_code=500, detail=str(e)) async def calculate_final_result(session_id: str): """SPI 최종 결과 계산""" try: # 1. 언어능력 채점 language_answers = spi_module.get_answers_by_type(session_id, "language") language_correct = sum(1 for a in language_answers if a.get("is_correct") == 1) language_score, language_grade = spi_scoring.calculate_ability_grade(language_correct, len(language_answers)) # 2. 비언어능력 채점 nonverbal_answers = spi_module.get_answers_by_type(session_id, "nonverbal") nonverbal_correct = sum(1 for a in nonverbal_answers if a.get("is_correct") == 1) nonverbal_score, nonverbal_grade = spi_scoring.calculate_ability_grade(nonverbal_correct, len(nonverbal_answers)) # 3. 성격검사 채점 personality_answers = spi_module.get_answers_by_type(session_id, "personality") personality_answers_with_trait = [] for answer in personality_answers: question = spi_questions.get_question_by_id(answer["question_id"]) if question: personality_answers_with_trait.append({ "question_id": answer["question_id"], "selected_option": answer["selected_option"], "trait": question.get("trait") }) # 결과 딕셔너리 생성 personality_result = spi_scoring.calculate_personality_scores(personality_answers_with_trait) # 日本語キーに変換してJSON文字列に整形 personality_for_db = spi_scoring.format_personality_for_db(personality_result) personality_score_json = json.dumps(personality_for_db, ensure_ascii=False) # 4. AI 코멘트 생성 (personality_type 컬럼에 저장하기 위해) try: ai_comment = spi_scoring.generate_ai_comment( personality_result, language_grade, nonverbal_grade ) except Exception as e: logger.error(f"SPI AI Comment Generation Error: {e}") ai_comment = "データを分析中です..." # 5. DB 저장 (personality_type에 AI 코멘트 저장) spi_module.save_or_update_output( session_id=session_id, language_score=language_score, language_grade=language_grade, nonverbal_score=nonverbal_score, nonverbal_grade=nonverbal_grade, personality_score=personality_score_json, # 個別特性JSONを保存 personality_type=ai_comment # AI コメント保存 ) await classify_and_save( session_id = session_id, part = "A", sender = "I", text = ai_comment ) # 6. ★ 중요: 여기서 우리가 만든 리포트 생성 함수 호출 ★ summary = spi_scoring.generate_professional_report( session_id=session_id, language_score=language_score, language_grade=language_grade, nonverbal_score=nonverbal_score, nonverbal_grade=nonverbal_grade, personality_scores=personality_result ) logger.info(f"SPI Completed for session: {session_id}") return { "session_id": session_id, "language_score": language_score, "language_grade": language_grade, "nonverbal_score": nonverbal_score, "nonverbal_grade": nonverbal_grade, "personality_scores": personality_result, "personality_type": ai_comment, # API 응답에 AI 분석 코멘트 (frontend 용) "summary": summary # 옛날 레거시 텍스트용 } except Exception as e: logger.error(f"SPI Result Calculation Error: {e}") raise e @router.get("/result/{session_id}") async def get_spi_result(session_id: str): """ SPI 결과 조회 Returns: SPI 최종 결과 """ try: output = spi_module.get_output(session_id) if not output: raise HTTPException(status_code=404, detail="No result found for this session") import json try: p_scores = json.loads(output.get("personality_score", "{}")) except: p_scores = {} result = { "session_id": session_id, "language_score": output.get("language_score", 0), "language_grade": output.get("language_grade", "E"), "nonverbal_score": output.get("nonverbal_score", 0), "nonverbal_grade": output.get("nonverbal_grade", "E"), "personality_scores": p_scores, "personality_type": output.get("personality_type", ""), "summary": "分析完了" } return { "status": "success", "result": result } except Exception as e: logger.error(f"SPI Result Get Error: {e}") raise HTTPException(status_code=500, detail=str(e))