배포 v1.5.6

배포 v1.5.6

Author: skqorrla

.github/workflows/Deploy-ML-EC2.yml

@@ -19,16 +19,23 @@ jobs:
       uses: appleboy/[email protected]
       env:
         OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+        CLICK_LOG: ${{ secrets.CLICK_LOG }}
+        DEEPFM_TRAIN_MODEL_PATH: ${{ secrets.DEEPFM_TRAIN_MODEL_PATH }}
+        DEEPFM_TRAIN_ENCODERS_PATH: ${{ secrets.DEEPFM_TRAIN_ENCODERS_PATH }}
+        DEEPFM_TRAIN_KEY2INDEX_PATH: ${{ secrets.DEEPFM_TRAIN_KEY2INDEX_PATH }}
       with:
         host: ${{ secrets.EC2_HOST_ML }}
         username: ${{ secrets.EC2_USER }}
         key: ${{ secrets.EC2_SSH_KEY }}
         port: 22
-        envs: OPENAI_API_KEY
+        envs: OPENAI_API_KEY CLICK_LOG DEEPFM_TRAIN_MODEL_PATH DEEPFM_TRAIN_ENCODERS_PATH DEEPFM_TRAIN_KEY2INDEX_PATH
         script: |
           export OPENAI_API_KEY=$OPENAI_API_KEY
-          echo "OPENAI_API_KEY is $OPENAI_API_KEY"
-
+          export CLICK_LOG=$CLICK_LOG
+          export DEEPFM_TRAIN_MODEL_PATH=$DEEPFM_TRAIN_MODEL_PATH
+          export DEEPFM_TRAIN_ENCODERS_PATH=$DEEPFM_TRAIN_ENCODERS_PATH
+          export DEEPFM_TRAIN_KEY2INDEX_PATH=$DEEPFM_TRAIN_KEY2INDEX_PATH
+          
           # 프로젝트 디렉터리로 이동
           cd ~/MLOps
           

MLOps/app/main.py

@@ -10,7 +10,7 @@
 from datetime import datetime
 
 # 라우터 임포트
-from app.routers import recommendation, chatbot
+from app.routers import recommendation, chatbot, crowd
 
 # 환경 변수 설정
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
@@ -47,6 +47,7 @@ async def global_exception_handler(request: Request, exc: Exception):
 # 라우터 등록
 app.include_router(recommendation.router)
 app.include_router(chatbot.router)
+app.include_router(crowd.router)
 
 @app.get("/")
 async def root():
@@ -68,7 +69,7 @@ async def root():
                     "stats": "/api/chat/stats"
                 },
                 "description": "OpenAI GPT 기반 데이트 코스 추천 챗봇",
-                "status": "active" if hasattr(chatbot, 'openai_service') else "inactive"
+                "status": "active" if hasattr(chatbot, 'langchain_agent_service') else "inactive"
             }
         },
         "documentation": "/docs"
@@ -84,7 +85,7 @@ async def health_check():
 
     # 챗봇 상태 확인
     chatbot_status = "inactive"
-    if hasattr(chatbot, 'openai_service') and chatbot.openai_service:
+    if hasattr(chatbot, 'langchain_agent_service') and chatbot.langchain_agent_service:
         chatbot_status = "active"
 
     # 전체 상태 결정
@@ -112,7 +113,7 @@ async def get_overall_stats():
                 "type": "ELK + DeepCTR"
             },
             "chatbot": {
-                "status": "active" if hasattr(chatbot, 'openai_service') else "inactive",
+                "status": "active" if hasattr(chatbot, 'langchain_agent_service') else "inactive",
                 "type": "OpenAI GPT",
                 "active_sessions": len(getattr(chatbot, 'active_sessions', {}))
             }

MLOps/app/model/deepfm_train.py

@@ -22,9 +22,9 @@ def __init__(self, data_path):
         self.feature_names = None
         self.model_input = None
         self.target = "yn"        
-        self.model_path = "/app/app/model/deepfm_model.pt"
-        self.encoders_path = "/app/app/model/label_encoders.pkl"
-        self.key2index_path = "/app/app/model/key2index.pkl"
+        self.model_path = os.environ.get("DEEPFM_TRAIN_MODEL_PATH", "")
+        self.encoders_path = os.environ.get("DEEPFM_TRAIN_ENCODERS_PATH", "")
+        self.key2index_path = os.environ.get("DEEPFM_TRAIN_KEY2INDEX_PATH", "")
         self.model = None
         self.max_len = None
         self.label_encoders = {}
@@ -177,7 +177,7 @@ def encode_sequence(x):
         return model.predict(model_input)
 
 if __name__ == "__main__":
-    deepfm_train = DeepFMModdelTrain("../data/final_click_log.csv")
+    deepfm_train = DeepFMModdelTrain(os.environ.get("CLICK_LOG", ""))
     deepfm_train.preprocess()
     model = deepfm_train.train()
     # 예시 데이터

MLOps/app/routers/crowd.py

@@ -0,0 +1,38 @@
+from fastapi import APIRouter
+from app.schema.crowd_schema import CrowdResponse, CrowdLevel, CrowdInfo
+
+router = APIRouter()
+
+@router.get("/api/crowd", response_model=CrowdResponse)
+async def get_crowd(hour: int):
+    """
+    ## 혼잡도 예측 API
+    - **hour**: 시간 (정수)
+    """
+    dummy_row_data = [
+        CrowdInfo(
+            area_nm="충정로역",
+            x="37.55969632013057",
+            y="126.96369132536704",
+            area_congest_lvl="붐빔",
+            area_congest_num=4
+        ),
+        CrowdInfo(
+            area_nm="서울역",
+            x="37.55659428234287",
+            y="126.97302795181167",
+            area_congest_lvl="붐빔",
+            area_congest_num=4
+        )
+    ]
+
+    dummy_crowd_level = CrowdLevel(
+        total=120,
+        row=dummy_row_data
+    )
+
+    return CrowdResponse(
+        success=True,
+        message="혼잡도 예측 성공!",
+        crowdLevel=dummy_crowd_level
+    ) 

MLOps/app/schema/crowd_schema.py

@@ -0,0 +1,18 @@
+from pydantic import BaseModel
+from typing import List
+
+class CrowdInfo(BaseModel):
+    area_nm: str
+    x: str
+    y: str
+    area_congest_lvl: str
+    area_congest_num: int
+
+class CrowdLevel(BaseModel):
+    total: int
+    row: List[CrowdInfo]
+
+class CrowdResponse(BaseModel):
+    success: bool
+    message: str
+    crowdLevel: CrowdLevel 

MLOps/app/services/deepctr_service.py

@@ -4,12 +4,13 @@
 from app.services.elk_client import ELKClient
 from app.services.user_data_service import UserDataService
 import pandas as pd
+import os
 
 class DeepCTRService:
     """DeepCTR 모델 서비스"""
 
     def __init__(self):
-        self.model = DeepFMModdelTrain("/app/data/final_click_log.csv")
+        self.model = DeepFMModdelTrain(os.environ.get("CLICK_LOG", ""))
         self.elk_client = ELKClient()
         self.user_data_service = UserDataService()
 

MLOps/app/services/user_data_service.py

@@ -1,11 +1,12 @@
 import pandas as pd
 from typing import Dict
+import os
 
 class UserDataService:
     """사용자 데이터 관리 서비스"""
 
     def __init__(self):
-        self.data_path = "/app/data/final_click_log.csv"
+        self.data_path = os.environ.get("CLICK_LOG", "")
         self.user_data = pd.read_csv(self.data_path)
 
     def get_user_info(self, userid: str) -> Dict: