Update: 데이터 분석 모델 수정.

client/src/components/ChartTemp.js

@@ -13,31 +13,37 @@ function ChartTemp() {
 
   useEffect(() => {
     if (isLogined()) {
-      axios.get(routesClient.userWeather, { withCredentials: true })
+      axios
+        .get(routesClient.userWeather, { withCredentials: true })
         .then((res) => {
-          console.log('temp', res.data.contents)
-          const userWeather = res.data.contents.weather_in
-          const userWeatherPredict = res.data.contents.weather_predict
-          const Array = []
-          const Array2 = []
+          console.log("temp", res.data.contents);
+          const userWeather = res.data.contents.weather_in;
+          const userWeatherPredict = res.data.contents.weather_predict;
+          const Array = [];
+          const Array2 = [];
           for (let i = 3; i < 9; i++) {
-            Array.push(userWeather[i].temp)
-            Array2.push(userWeather[i].collected_at.split('T')[1].split('.')[0])
+            Array.push(userWeather[i].temp);
+            Array2.push(
+              userWeather[i].collected_at.split("T")[1].split(".")[0]
+            );
           }
           for (let j = 0; j < 3; j++) {
-            Array.push(userWeatherPredict[j].temp)
-            Array2.push(userWeatherPredict[j].collected_at.split('T')[1].split('.')[0])
+            Array.push(userWeatherPredict[j].temp);
+            Array2.push(
+              userWeatherPredict[j].collected_at.split("T")[1].split(".")[0]
+            );
           }
-          setTemp(Array)
-          setNewLabel(Array2)
-        })
+          setTemp(Array);
+          setNewLabel(Array2);
+        });
     } else {
       axios.get(routesClient.outsideLoc + `3743011`).then((res) => {
         const outWeather = res.data.contents.weather_out;
         const Array = [];
         const Array2 = [];
         console.log(outWeather);
-        let i = outWeather.length - 9;
+        // let i = outWeather.length - 9;
+        let i = 0;
         for (i; i < outWeather.length; i++) {
           Array.push(outWeather[i].temp);
           Array2.push(outWeather[i].collected_at.split("T")[1].split(".")[0]);

server/.gitignore

@@ -16,3 +16,4 @@ __pycache__
 src/data_processing/temp.csv
 src/data_processing/model.h5
 src/data_processing/config.py
+src/data_processing/models

server/src/data_processing/main.py

@@ -20,7 +20,6 @@ dbconfig = {"host": DB["host"], "port": DB["port"], "user": DB["user"],
             "password": DB["password"], "database": DB["database"]}
 
 data_dir = os.getcwd() + "/src/data_processing/temp.csv"
-model_dir = os.getcwd() + "/src/data_processing/model.h5"
 
 
 def makeDateForm():
@@ -51,33 +50,28 @@ for user in users:
     standard_df, mean_df, std_df = preprocess(cursor, host)
 
     # 데이터 분석
-    modeling(standard_df)
+    modeling(standard_df, host["email"])
 
     # 데이터 분석 결과 저장
 
     collected_at = makeDateForm()
 
-    model_file = open(model_dir, 'rb')
-    model_file_data = model_file.read()
+    model_file_path = "/src/data_processing/models/{0}/model.h5".format(
+        host["email"])
 
     params = {"mean": mean_df.to_json(), "std": std_df.to_json()}
 
-    cursor.execute("INSERT INTO \"Data_Processings\" (host,collected_at,model_file,params) VALUES (%s,%s,%s,%s)",
+    cursor.execute("INSERT INTO \"Data_Processings\" (host,collected_at,model_file_path,params) VALUES (%s,%s,%s,%s)",
                    (host["email"],
                     collected_at,
-                    model_file_data,
+                    model_file_path,
                     Json(params),))
 
     connection.commit()
-    model_file.close()
 
     if os.path.isfile(data_dir):
         os.remove(data_dir)
 
-    if os.path.isfile(model_dir):
-        os.remove(model_dir)
-
-
 # Cursor와 Connection 종료
 cursor.close()
 connection.close()

server/src/data_processing/model.py

@@ -3,7 +3,7 @@ import os
 import tensorflow as tf
 
 
-def modeling(standard_df):
+def modeling(standard_df, host):
 
     n = len(standard_df)
     test_size = int(0.3 * n)
@@ -43,4 +43,5 @@ def modeling(standard_df):
     model.compile(loss='mse', optimizer='adam')
     # model.fit(train_feature, train_label, epochs=50, batch_size=1000)
 
-    model.save(os.getcwd() + '/src/data_processing/model.h5')
+    model.save(os.getcwd() +
+               '/src/data_processing/models/{0}/model.h5'.format(host))

server/src/data_processing/prediction.py

@@ -10,7 +10,6 @@ import tensorflow as tf
 import numpy as np
 
 
-
 if __name__ == "__main__":
 
     dbconfig = {"host": DB["host"], "port": DB["port"], "user": DB["user"],
@@ -18,7 +17,6 @@ if __name__ == "__main__":
     user_email = sys.argv[1]
 
     data_dir = os.getcwd() + "/src/data_processing/temp.csv"
-    model_dir = os.getcwd() + "/src/data_processing/model.h5"
 
     # DB Connect and Make Cursor
     connection = psycopg2.connect(
@@ -27,13 +25,10 @@ if __name__ == "__main__":
 
     # Get Model and Params, then Make File and Variable
     cursor.execute(
-        "SELECT model_file, params FROM \"Data_Processings\" WHERE host=%s", (user_email,))
+        "SELECT model_file_path, params FROM \"Data_Processings\" WHERE host=%s", (user_email,))
     model_params = cursor.fetchone()
 
-    blob_model = model_params[0]
-    model_file = open(model_dir, "wb")
-    model_file.write(blob_model)
-    model_file.close()
+    model_file_path = model_params[0]
 
     params = model_params[1]
     mean = json.loads(params["mean"])
@@ -90,16 +85,12 @@ if __name__ == "__main__":
     for col in feature_cols:
         new_data[col] = (new_data[col] - mean[col]) / std[col]
 
-    model_pro = tf.keras.models.load_model(model_dir)
+    model_pro = tf.keras.models.load_model(os.getcwd() + model_file_path)
     prediction = model_pro.predict(new_data)
     prediction = prediction * std['temp_out'] + mean['temp_out']
 
-
     # 사용한 파일 삭제
     if os.path.isfile(data_dir):
         os.remove(data_dir)
 
-    if os.path.isfile(model_dir):
-        os.remove(model_dir)
-
     print(prediction)

server/src/db/eue.sql

-/*
-    # EUE Server Database Schema
-
-    - DataBase : PostgreSQL
-    - 실제 서버 동작시 Sequelize가 models 디렉토리의 모델들에 따라 테이블을 생성합니다. 따라서 해당 SQL파일은 참고용으로 사용합니다.
-
-    1. LOC_DO
-        - 행정구역 도/특별시/특별자치시 이름과 코드 저장
-        - LOCSIGUNGU와 LOC_EMD에 참조됨
-
-    2. LOC_SGG
-        - 행정구역 시/군/구 이름과 코드 저장
-        - LOC_DO를 참조
-        - LOC_EMD에 참조됨
-
-    3. LOC_EMD
-        - 행정구역 읍/면/동 이름과 코드
-        - LOC_DO와 LOC_SGG를 참조
-        - USERS와 WEATHER_OUT에 참조됨
-
-    4. USERS
-        - 사용자 ID, PassWord, 거주지역코드
-        - LOC_EMD를 참조
-    
-    5. WEATHER_IN
-        - 사용자 ID, 수집 날짜 및 시간, 온도, 습도, 광도
-        - USERS와 LOC_EMD 참조
-
-    6. WEATHER_OUT
-        - 지역 코드, 수집 날짜 및 시간, 온도, 습도, 기압, 풍속
-        - LOC_EMD 참조
-    
-*/
-
-CREATE TABLE DOE
-(
-    CODE INT NOT NULL,
-    NAME_DO VARCHAR(20) NOT NULL,
-    PRIMARY KEY(CODE_DO)
-);
-
-CREATE TABLE SGG
-(
-    CODE INT NOT NULL,
-    CODE_DO INT NOT NULL,
-    NAME_SGG VARCHAR(20) NOT NULL,
-    PRIMARY KEY(CODE),
-    FOREIGN KEY(CODE_DO) REFERENCES DO(CODE) ON UPDATE CASCADE ON DELETE RESTRICT
-);
-
-CREATE TABLE EMD
-(
-    CODE INT NOT NULL,
-    CODE_DO INT NOT NULL,
-    CODE_SGG INT NOT NULL,
-    NAME_EMD VARCHAR(20) NOT NULL,
-    PRIMARY KEY(CODE),
-    FOREIGN KEY(CODE_DO) REFERENCES DO(CODE) ON UPDATE CASCADE ON DELETE RESTRICT,
-    FOREIGN KEY(CODE_SGG) REFERENCES SGG(CODE) ON UPDATE CASCADE ON DELETE RESTRICT
-);
-
-CREATE TABLE USERS
-(
-    EMAIL VARCHAR(320) UNIQUE NOT NULL,
-    PW VARCHAR(20) NOT NULL,
-    LOC_CODE INT NOT NULL,
-    PRIMARY KEY(EMAIL),
-    FOREIGN KEY(LOC_CODE) REFERENCES EMD(CODE) ON UPDATE CASCADE ON DELETE RESTRICT
-);
-
-CREATE TABLE WEATHER_IN
-(
-    HOST VARCHAR(320) NOT NULL,
-    COLLECTED_AT TIMESTAMP NOT NULL,
-    TEMP FLOAT DEFAULT 0,
-    HUMI FLOAT DEFAULT 0,
-    LIGHTS FLOAT DEFAULT 0,
-    PRIMARY KEY(EMAIL,COLLECTED_AT),
-    FOREIGN KEY(EMAIL) REFERENCES USERS(EMAIL) ON UPDATE CASCADE ON DELETE RESTRICT
-);
-
-CREATE TABLE WEATHER_OUT
-(
-    LOC_CODE INT NOT NULL,
-    COLLECTED_AT TIMESTAMP NOT NULL,
-    TEMP FLOAT DEFAULT 0,
-    HUMI FLOAT DEFAULT 0,
-    PRESS FLOAT DEFAULT 0,
-    WIND_SPEED FLOAT DEFAULT 0,
-    PRIMARY KEY(LOC_CODE, COLLECTED_AT),
-    FOREIGN KEY(LOC_CODE) REFERENCES EMD(CODE) ON UPDATE CASCADE ON DELETE RESTRICT
-);
\ No newline at end of file

server/src/models/data_processing.js

@@ -18,8 +18,8 @@ export class Data_Processing extends Model {
           primaryKey: true,
           defaultValue: Date.now(),
         },
-        model_file: {
-          type: DataTypes.BLOB,
+        model_file_path: {
+          type: DataTypes.STRING,
         },
         params: {
           type: DataTypes.JSON,