Merge pull request #36 from 9git9git/SCRUM-140-BE-분석-페이지-서비스-API-개발

Scrum 140 be 분석 페이지 서비스 api 개발

Author: GideokKim

app/api/v1/endpoints/analyze.py

@@ -0,0 +1,58 @@
+from fastapi import APIRouter, Depends, HTTPException, status
+from sqlalchemy.ext.asyncio import AsyncSession
+from uuid import UUID
+from app.schemas.base import ResponseBase
+from app.schemas.comprehensive_evaluation import ComprehensiveEvaluationResponse
+from app.schemas.recommended_challenge import RecommendedChallengeResponse
+from app.services.analyze import (
+    get_or_create_today_comprehensive_evaluation,
+    get_or_create_today_recommended_challenges,
+)
+from app.db.session import get_db
+from typing import List
+
+router = APIRouter()
+
+
+@router.get(
+    "/today",
+    response_model=ResponseBase[ComprehensiveEvaluationResponse],
+)
+async def get_today_analysis(
+    user_id: UUID,
+    db: AsyncSession = Depends(get_db),
+) -> ResponseBase[ComprehensiveEvaluationResponse]:
+    """
+    오늘의 종합 평가를 가져오거나 생성합니다.
+    """
+    try:
+        result = await get_or_create_today_comprehensive_evaluation(db, user_id)
+        return ResponseBase(status_code=status.HTTP_200_OK, data=result)
+    except HTTPException as e:
+        return ResponseBase(status_code=e.status_code, error=e.detail)
+    except Exception as e:
+        return ResponseBase(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, error=str(e)
+        )
+
+
+@router.get(
+    "/today/challenges",
+    response_model=ResponseBase[List[RecommendedChallengeResponse]],
+)
+async def get_today_challenges(
+    user_id: UUID,
+    db: AsyncSession = Depends(get_db),
+) -> ResponseBase[List[RecommendedChallengeResponse]]:
+    """
+    오늘의 추천 도전과제를 가져오거나 생성합니다.
+    """
+    try:
+        result = await get_or_create_today_recommended_challenges(db, user_id)
+        return ResponseBase(status_code=status.HTTP_200_OK, data=result)
+    except HTTPException as e:
+        return ResponseBase(status_code=e.status_code, error=e.detail)
+    except Exception as e:
+        return ResponseBase(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, error=str(e)
+        )

app/api/v1/endpoints/recommended_challenge.py

@@ -72,14 +72,13 @@ async def get_recommended_challenge(
 )
 async def post_recommended_challenge(
     user_id: UUID,
-    progress_id: UUID,
     category_id: UUID,
     challenge_data: RecommendedChallengeCreate,
     db: AsyncSession = Depends(get_db),
 ) -> ResponseBase[RecommendedChallengeResponse]:
     try:
         challenge = await add_recommended_challenge(
-            db, user_id, progress_id, category_id, challenge_data
+            db, user_id, category_id, challenge_data
         )
         return ResponseBase(status_code=status.HTTP_201_CREATED, data=challenge)
     except HTTPException as e:

app/api/v1/router.py

@@ -15,6 +15,7 @@
     progress,
     todo,
     main,
+    analyze,
 )
 
 router = APIRouter()
@@ -77,3 +78,8 @@
     prefix="/users/{user_id}",
     tags=["user_characters"],  # or "characters" if you want to group together
 )
+router.include_router(
+    analyze.router,
+    prefix="/users/{user_id}/analyze",
+    tags=["analyze"],
+)

app/core/config.py

@@ -14,7 +14,13 @@ class Settings(BaseSettings):
     # 쿠키 보안 설정
     IS_SECURE: bool
 
-    model_config = ConfigDict(env_file=".env", env_file_encoding='utf-8', extra="allow")
+    # Azure OpenAI API 설정
+    AZURE_OPENAI_API_KEY: str
+    AZURE_OPENAI_ENDPOINT: str
+    AZURE_OPENAI_DEPLOYMENT_NAME: str
+    AZURE_OPENAI_API_VERSION: str
+
+    model_config = ConfigDict(env_file=".env", env_file_encoding="utf-8", extra="allow")
 
 
 settings = Settings()

app/crud/recommended_challenge.py

@@ -17,13 +17,11 @@
 async def create_recommended_challenge(
     db: AsyncSession,
     user_id: UUID,
-    progress_id: UUID,
     category_id: UUID,
     challenge_data: RecommendedChallengeCreate,
 ) -> RecommendedChallengeResponse:
     db_challenge = RecommendedChallenge(
         user_id=user_id,
-        progress_id=progress_id,
         category_id=category_id,
         progress_rate=challenge_data.progressRate,
         challenge_task=challenge_data.challengeTask,

app/llm/agent_router.py

@@ -1,9 +1,16 @@
 from app.llm.tools import detect_intent, detect_goal_function
 from app.llm.goal_agents import (
-    get_english_response, get_coding_response, get_fitness_response,
-    get_english_question, get_coding_question,
-    get_english_info, get_coding_info, get_fitness_info,
-    get_english_mentalcare, get_coding_mentalcare, get_fitness_mentalcare,
+    get_english_response,
+    get_coding_response,
+    get_fitness_response,
+    get_english_question,
+    get_coding_question,
+    get_english_info,
+    get_coding_info,
+    get_fitness_info,
+    get_english_mentalcare,
+    get_coding_mentalcare,
+    get_fitness_mentalcare,
 )
 
 VALID_GOALS = ["영어", "코딩", "운동"]

app/llm/goal_agents.py

@@ -7,7 +7,8 @@
 # 통합 프롬프트 템플릿
 
 
-general_prompt = PromptTemplate.from_template("""
+general_prompt = PromptTemplate.from_template(
+    """
 당신은 {goal} 분야의 따뜻하고 실용적인 전문가입니다.
 
 [사용자 요청]
@@ -17,73 +18,88 @@
 요청 유형: {intent}
 
 {extra_instruction}
-""")
-
+"""
+)
 
 
 # 일반 응답 (피드백, 로드맵)
 def get_response(goal: str, user_input: str, intent: str) -> str:
-    instruction = "사용자의 질문에 전문가로서 조언이나 피드백을 제공해주세요." if intent == "피드백" else \
-                  "사용자의 목표에 맞는 단계별 학습 로드맵이나 실천 계획을 제시해주세요." if intent == "로드맵" else \
-                  "질문에 대해 상황을 이해하고 적절한 조언을 제공해주세요."
+    instruction = (
+        "사용자의 질문에 전문가로서 조언이나 피드백을 제공해주세요."
+        if intent == "피드백"
+        else (
+            "사용자의 목표에 맞는 단계별 학습 로드맵이나 실천 계획을 제시해주세요."
+            if intent == "로드맵"
+            else "질문에 대해 상황을 이해하고 적절한 조언을 제공해주세요."
+        )
+    )
+
+    return llm.invoke(
+        general_prompt.format(
+            goal=goal,
+            user_input=user_input,
+            intent=intent,
+            extra_instruction=instruction,
+        )
+    ).content.strip()
 
-    return llm.invoke(general_prompt.format(
-        goal=goal,
-        user_input=user_input,
-        intent=intent,
-        extra_instruction=instruction
-    )).content.strip()
 
 get_english_response = lambda u, i: get_response("영어", u, i)
 get_coding_response = lambda u, i: get_response("코딩", u, i)
 get_fitness_response = lambda u, i: get_response("운동", u, i)
 
 
-
 # 문제 생성
 def get_question(goal: str, user_input: str) -> str:
     instruction = f"{goal} 분야의 간단한 퀴즈나 연습 문제를 생성해주세요. 상황이나 감정도 반영해주세요."
-    return llm.invoke(general_prompt.format(
-        goal=goal,
-        user_input=user_input,
-        intent="문제 생성",
-        extra_instruction=instruction
-    )).content.strip()
+    return llm.invoke(
+        general_prompt.format(
+            goal=goal,
+            user_input=user_input,
+            intent="문제 생성",
+            extra_instruction=instruction,
+        )
+    ).content.strip()
+
 
 get_english_question = lambda u: get_question("영어", u)
 get_coding_question = lambda u: get_question("코딩", u)
 
 
-
 # 정보 제공
 def get_info(goal: str, user_input: str) -> str:
     instruction = f"{goal}과 관련된 시험, 자격증, 학습 팁, 활동 정보 등을 전문가 입장에서 자세히 제공해주세요."
-    return llm.invoke(general_prompt.format(
-        goal=goal,
-        user_input=user_input,
-        intent="정보 제공",
-        extra_instruction=instruction
-    )).content.strip()
+    return llm.invoke(
+        general_prompt.format(
+            goal=goal,
+            user_input=user_input,
+            intent="정보 제공",
+            extra_instruction=instruction,
+        )
+    ).content.strip()
+
 
 get_english_info = lambda u: get_info("영어", u)
 get_coding_info = lambda u: get_info("코딩", u)
 get_fitness_info = lambda u: get_info("운동", u)
 
 
-
 # 멘탈케어 응답
 def get_mentalcare(goal: str, user_input: str) -> str:
     instruction = f"""
 당신은 {goal} 분야의 학습이나 실천 과정에서 지친 사람에게
 공감과 위로, 회복을 돕는 따뜻한 메시지를 전달하는 전문가입니다.
 현재 감정과 상황을 고려하여 부담스럽지 않게 응원해주세요.
 """
-    return llm.invoke(general_prompt.format(
-        goal=goal,
-        user_input=user_input,
-        intent="멘탈케어",
-        extra_instruction=instruction
-    )).content.strip()
+    return llm.invoke(
+        general_prompt.format(
+            goal=goal,
+            user_input=user_input,
+            intent="멘탈케어",
+            extra_instruction=instruction,
+        )
+    ).content.strip()
+
 
 get_english_mentalcare = lambda u: get_mentalcare("영어", u)
 get_coding_mentalcare = lambda u: get_mentalcare("코딩", u)

app/llm/llm_provider.py

@@ -1,15 +1,14 @@
-
 from functools import lru_cache
 from langchain_community.chat_models import AzureChatOpenAI
 from app.core.config import settings
 
+
 @lru_cache()
 def get_agent():
     return AzureChatOpenAI(
         azure_deployment=settings.AZURE_OPENAI_DEPLOYMENT_NAME,
         azure_endpoint=settings.AZURE_OPENAI_ENDPOINT,
         openai_api_key=settings.AZURE_OPENAI_API_KEY,
         openai_api_version=settings.AZURE_OPENAI_API_VERSION,
-        temperature=0.5
+        temperature=0.5,
     )
-