Feedback

.github/ISSUE_TEMPLATE/bug_report.md

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+---
+name: Bug Report
+about: 버그를 레포트 할 때 사용하는 템플릿
+title: "[BUG] "
+labels: ""
+assignees: ""
+
+---
+
+## Describe the bug
+- 
+
+## To Reproduce
+-
+
+## Expected behavier
+-
+
+## Screenshots

.github/ISSUE_TEMPLATE/feature_request.md

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+---
+name: Feature Request
+about: 기능을 추가할 때 사용하는 템플릿
+title: "[FEAT] "
+labels: ""
+assignees: ""
+
+---
+
+## Background
+- 
+
+## To Do
+- [ ] To do 1
+- [ ] To do 2

.github/pull_request_template.md

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+## Overview
+- 
+
+## Change Log
+- 
+
+## To Reviewer
+- 
+
+## Issue Tags
+- Closed: #
+- See also: #

.github/workflows/backend-gitaction.yml

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+name: backend
+
+on:
+  push:
+    branches: [ "develop_backend" ]
+
+jobs:
+  build:
+    runs-on: ubuntu-20.04
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.9"]
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        cd GiftHub_backend
+        pip install -r requirements.txt
+    - name: Test Backend Django Excecution
+      run: |
+        cd GiftHub_backend
+        python manage.py test
+
+  deploy:
+    # needs를 통해 build job이 수행 성공시에 작업되도록 설정
+    needs: build
+    runs-on: ubuntu-20.04
+
+    steps:
+    # NCP 로그인 / git pull & Docker run
+    - name: NCP login and git pull and docker run
+      uses: appleboy/ssh-action@master
+      with:
+        host: ${{ secrets.BACKEND_HOST }}
+        username: ${{ secrets.BACKEND_USERNAME }}
+        password: ${{ secrets.BACKEND_PASSWORD }}
+        port: ${{ secrets.BACKEND_PORT }}
+        script: |
+          cd ${{ secrets.BACKEND_DIR_PATH }}
+          git checkout develop_backend
+          git pull
+          bash deploy.sh

.gitignore

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+*.pyc
+*.sqlite3
+*.json
+*.pt
+*.pkl
+__pycache__
+GiftHub_mlflow/mlruns
+GiftHub_mlflow/mlflow_pyfunc_custom
+GiftHub_mlflow/mlartifacts

GiftHub_backend/Dockerfile

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+FROM python:3.9.18
+# set unbuffered mode
+ENV PYTHONUNBUFFERD 1
+COPY . /home/GiftHub_backend
+WORKDIR /home/GiftHub_backend
+# RUN sed -i "s/DEBUG = True/DEBUG = False/g" /home/ksh/level2-3-recsys-finalproject-recsys-04/GiftHub_backend/GiftHubProject/settings.py
+RUN pip install -r requirements.txt
+CMD ["python", "manage.py", "runserver", "0.0.0.0:8000"]
+EXPOSE 8000

GiftHub_backend/GiftHubApp/admin.py

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+from django.contrib import admin
+from GiftHubApp.database.models import *
+
+# Register your models here.
+admin.site.register(Temp02)
+admin.site.register(User)

GiftHub_backend/GiftHubApp/api/requests/model_request.py

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+import pandas as pd
+from django.conf import settings
+
+from GiftHubApp.database.sql_executor import *
+from GiftHubApp.database.serializers import *
+from GiftHubApp.database.models import *
+from GiftHubApp.api.requests.request import APIRequest
+
+# mlflow naver serving
+def predict_lgbm(product_id: str):
+    queryset = FilteredRawdata.objects
+    serializer = FilteredRawdataSerializer(queryset, many=True)
+    df = pd.DataFrame.from_dict(serializer.data)
+    df = df.drop("id", axis=1) # id 제외
+
+    api = APIRequest()
+    columns = list(df.columns)
+    data = list(df.loc[:].values.tolist())
+    params = {
+        "columns":columns,
+        "rows":data,
+        "product_id":product_id
+    }
+    api.set_data(params)
+    api.set_url(settings.MODEL_SERVING_URL + settings.LGBM_SLASH)
+    predictions = api.post()
+    predictions = pd.DataFrame(predictions["rows"], columns=predictions["columns"])
+
+    return predictions
+
+# amazon product serving
+def predict_bert4rec(list_product_id: list):
+    api = APIRequest()
+    params = {
+        "matrix":list_product_id
+    }
+    api.set_data(params)
+    api.set_url(settings.MODEL_SERVING_URL + settings.BERT4REC_SLASH)
+    predictions = api.post()
+    predictions = predictions["list"]
+
+    return predictions
+
+def predict_ease(df_user_interaction: pd.DataFrame):
+    api = APIRequest()
+    columns = list(df_user_interaction.columns)
+    data = list(df_user_interaction.loc[:].values.tolist())
+    params = {
+        "columns":columns,
+        "rows":data
+    }
+    api.set_data(params)
+    api.set_url(settings.MODEL_SERVING_URL + settings.EASE_SLASH)
+    predictions = api.post()
+    predictions = predictions["list"]
+
+    return predictions
+
+def predict_lightgcn(list_product_id: list):
+    api = APIRequest()
+    params = {
+        "matrix":list_product_id
+    }
+    api.set_data(params)
+    api.set_url(settings.MODEL_SERVING_URL + settings.LGCN_SLASH)
+    predictions = api.post()
+    predictions = predictions["list"]
+
+    return predictions

GiftHub_backend/GiftHubApp/api/requests/request.py

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+import os
+import requests
+
+class APIRequest():
+    def __init__(self):
+        self.headers = {'Content-Type': 'application/json'}
+        self.url = ""
+        self.params = {}
+        self.data = {}
+
+    def set_url(self, url: str):
+        if not isinstance(url, str):
+            raise TypeError("Provided value is not of type str.")
+        self.url = url
+
+    def set_params(self, params: dict):
+        """
+        Use get
+        """
+        if not isinstance(params, dict):
+            raise TypeError("Provided value is not of type dict.")
+        self.params = params
+
+    def set_data(self, data: dict):
+        """
+        Use post
+        """
+        if not isinstance(data, dict):
+            raise TypeError("Provided value is not of type dict.")
+        self.data = data
+
+    def get(self):
+        response = requests.get(self.url, params=self.params)
+        if response.status_code == 200:
+            # 요청 성공
+            return response.json()  # 응답 데이터 처리
+        else:
+            # 요청 실패
+            raise response.json()
+
+    def post(self):
+        response = requests.post(self.url, json=self.data, headers=self.headers)
+        if response.status_code == 200:
+            # 요청 성공
+            return response.json()  # 응답 데이터 처리
+        else:
+            # 요청 실패
+            raise response.json()