Merge remote-tracking branch 'origin/main'

build.gradle

@@ -86,14 +86,7 @@ sonarqube {
         property "sonar.login", "admin"
         property "sonar.password", "0520"
         property "sonar.sources", "src"
-        property "sonar.language", "java" // 분석을 Java로 제한
+        property "sonar.language", "java"
         property "sonar.sourceEncoding", "UTF-8"
-//        property "sonar.exclusions", "**/*Test*.*, **/Q*.JAVA"
-//        property "sonar.test.inclusions", "**/*Test.groovy, **/*Test.java"
-//        property "sonar.coverage.exclusions", "**/*Test*.*, **/Q*.java"
-//        property "sonar.java.junit.reportPaths", "${buildDir}/test-results"
-//        property "sonar.jacoco.reportPaths", "${buildDir}/jacoco/jacoco.exec"
-//        property "sonar.coverage.jacoco.xmlReportPaths", "build/reports/coverageReport/coverageReport.xml"    // Test Coverage Report 생성한 경우 사용
-//        property 'sonar.exclusions', 'com/xxx/**/*_.java,com/**/QMyEntity.java,....'
     }
-}
+}

ml/main.py

@@ -18,14 +18,14 @@ def __init__(self, input_size, hidden_size, num_layers, output_size):
         super(LSTMModel, self).__init__()
         self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
         self.fc = nn.Linear(hidden_size, output_size)
-    
+
     def forward(self, x):
         out, _ = self.lstm(x)
         out = self.fc(out)
-        
+
         return out
 
-input_size = 13
+input_size = 12
 hidden_size = 64
 num_layers = 2
 output_size = 4
@@ -46,21 +46,26 @@ async def predict(input_data: str = Form(...),type_num: int = Form(...)):
 
 
     # 모델에 대한 타입 지정을 num 에 해줘야함
-    # 모델의 타입은 직렬 정보에 따라서 1 = 2 = 3 = 4 = 5 = 6 = 
-
-
-
-
+    # 모델의 타입은 직렬 정보에 따라서 1 = 2 = 3 = 4 = 5 = 6 =
     model.load_state_dict(torch.load('lstm_model_'+str(type_num)+'.pth'))
     model.eval()
 
     input_data = ast.literal_eval(input_data)
-    filtered_values = [value for value in input_data if value != -1]
-    mean_value = np.mean(filtered_values)
-
-    input_data = [mean_value if value == -1 else value for value in input_data]
-    input_data.append(sum(input_data))
-
+    # 모두 0이면 리스트에 0 추가
+    if all(value == 0 for value in input_data):
+        input_data.append(0)
+    else:
+        # 0 값을 제외한 리스트 생성
+        filtered_values = [value for value in input_data if value != 0]
+
+        # 0이 없을 때만 평균값 계산 및 처리
+        if filtered_values:
+            mean_value = np.mean(filtered_values)
+            input_data = [mean_value if value == 0 else value for value in input_data]
+            input_data.append(sum(input_data))
+        else:
+            input_data.append(sum(input_data))
+
     print(f"model input data is {input_data}")
 
     result = predict_single_input(input_data)

ml/train.py

@@ -35,13 +35,12 @@ def data_preprocess(df):
 df=data_preprocess(df)
 
 
-
-type_1_df=df[df['applicationType']=='경찰직공무원(여)']
-type_2_df=df[df['applicationType']=='경찰직공무원(남)']
+type_1_df=df[df['applicationType']=='경찰직공무원(남)']
+type_2_df=df[df['applicationType']=='경찰직공무원(여)']
 type_3_df=df[df['applicationType']=='소방직공무원(남)']
-type_4_df=df[df['applicationType']=='교정직공무원(남)']
-type_5_df=df[df['applicationType']=='소방직공무원(여)']
-type_6_df=df[df['applicationType']=='교정직공무원(여)']
+type_4_df=df[df['applicationType']=='소방직공무원(여)']
+type_5_df=df[df['applicationType']=='경호직공무원(남)']
+type_6_df=df[df['applicationType']=='경호직공무원(여)']
 
 type_1_df=type_1_df.drop('applicationType',axis=1)
 type_2_df=type_2_df.drop('applicationType',axis=1)
@@ -50,7 +49,7 @@ def data_preprocess(df):
 type_5_df=type_5_df.drop('applicationType',axis=1)
 type_6_df=type_6_df.drop('applicationType',axis=1)
 
-
+print(type_3_df)
 def convert(df):
 
     data = {
@@ -62,15 +61,15 @@ def convert(df):
 
     # 피벗 테이블 생성
     pivot_table = df.groupby(['member_id', 'month'])['score'].sum().unstack()
-    
+
     return pivot_table
 
-type_1_df=convert(type_1_df)   
-type_2_df=convert(type_2_df)   
-type_3_df=convert(type_3_df)   
-type_4_df=convert(type_4_df)   
-type_5_df=convert(type_5_df)   
-type_6_df=convert(type_6_df)   
+type_1_df=convert(type_1_df)
+type_2_df=convert(type_2_df)
+type_3_df=convert(type_3_df)
+type_4_df=convert(type_4_df)
+type_5_df=convert(type_5_df)
+type_6_df=convert(type_6_df)
 
 
 type_1_df=type_1_df.fillna(type_1_df.mean())
@@ -106,24 +105,32 @@ def set_label(df):
 type_6_df = set_label(type_6_df)
 
 
+type_1_df=type_1_df.drop("total_score",axis=1)
+type_2_df=type_2_df.drop("total_score",axis=1)
+type_3_df=type_3_df.drop("total_score",axis=1)
+type_4_df=type_4_df.drop("total_score",axis=1)
+type_5_df=type_5_df.drop("total_score",axis=1)
+type_6_df=type_6_df.drop("total_score",axis=1)
+
+
 
 
 class LSTMModel(nn.Module):
     def __init__(self, input_size, hidden_size, num_layers, output_size):
         super(LSTMModel, self).__init__()
         self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
         self.fc = nn.Linear(hidden_size, output_size)
-    
+
     def forward(self, x):
-        out, _ = self.lstm(x)
+        out,_= self.lstm(x)
         out = self.fc(out)  # Remove this line
-        
+
         return out
-        
+
 df_list=[type_1_df,type_2_df,type_3_df,type_4_df,type_5_df,type_6_df]
 num=1
 for i in df_list:
-    
+
     # Convert the DataFrame to PyTorch tensors
     data_tensor = torch.tensor(i.drop(columns=['label']).values, dtype=torch.float32)
     label_tensor = torch.tensor(i['label'].values, dtype=torch.long)  # Change dtype to long
@@ -138,7 +145,7 @@ def forward(self, x):
     test_labels = label_tensor[train_size:]
 
     # Create an instance of the LSTMModel
-    input_size = 13
+    input_size = 12
     hidden_size = 64
     num_layers = 2
     output_size = 4
@@ -153,13 +160,13 @@ def forward(self, x):
     for epoch in range(num_epochs):
         model.train()
         optimizer.zero_grad()
-        
+
         outputs = model(train_data)
         loss = criterion(outputs.squeeze(), train_labels)  # Remove the dimension from outputs
-        
+
         loss.backward()
         optimizer.step()
-        
+
         if (epoch + 1) % 10 == 0:
             print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')
 

src/main/java/se/ton/t210/configuration/RestConfig.java

@@ -0,0 +1,29 @@
+package se.ton.t210.configuration;
+
+import com.fasterxml.jackson.databind.ObjectMapper;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+import org.springframework.http.HttpHeaders;
+import org.springframework.http.MediaType;
+import org.springframework.web.client.RestTemplate;
+
+@Configuration
+public class RestConfig {
+
+    @Bean
+    public HttpHeaders headers() {
+        HttpHeaders headers = new HttpHeaders();
+        headers.setContentType(MediaType.MULTIPART_FORM_DATA);
+        return headers;
+    }
+
+    @Bean
+    public ObjectMapper objectMapper() {
+        return new ObjectMapper();
+    }
+
+    @Bean
+    public RestTemplate restTemplate() {
+        return new RestTemplate();
+    }
+}

src/main/java/se/ton/t210/configuration/TokenFilterConfig.java

@@ -32,10 +32,15 @@ public FilterRegistrationBean<Filter> addAccessTokenFilter() {
         FilterRegistrationBean<Filter> filterRegistrationBean = new FilterRegistrationBean<>();
         filterRegistrationBean.setFilter(new TokenFilter(tokenSecret, accessTokenCookieKey));
         filterRegistrationBean.addUrlPatterns(
-            "/api/reissue/token",
             "/html/dashboard.html",
             "/html/personal-information.html",
             "/html/record.html",
+            "/html/application-information1.html",
+            "/html/application-information2.html",
+            "/html/application-information3.html",
+            "/html/application-information4.html",
+            "/html/application-information5.html",
+            "/html/application-information6.html",
             "/html/setting-account.html"
         );
         return filterRegistrationBean;
@@ -45,15 +50,19 @@ public FilterRegistrationBean<Filter> addAccessTokenFilter() {
     public FilterRegistrationBean<Filter> addRefreshTokenFilter() {
         FilterRegistrationBean<Filter> filterRegistrationBean = new FilterRegistrationBean<>();
         filterRegistrationBean.setFilter(new TokenFilter(tokenSecret, refreshTokenCookieKey));
-        filterRegistrationBean.addUrlPatterns("/api/reissue/token");
+        filterRegistrationBean.addUrlPatterns(
+            "/api/reissue/token"
+        );
         return filterRegistrationBean;
     }
 
     @Bean
     public FilterRegistrationBean<Filter> addEmailTokenFilter() {
         FilterRegistrationBean<Filter> filterRegistrationBean = new FilterRegistrationBean<>();
         filterRegistrationBean.setFilter(new TokenFilter(tokenSecret, emailAuthTokenCookieKey));
-        filterRegistrationBean.addUrlPatterns("/api/member/signUp");
+        filterRegistrationBean.addUrlPatterns(
+            "/api/member/signUp"
+        );
         return filterRegistrationBean;
     }
 }

src/main/java/se/ton/t210/configuration/filter/TokenFilter.java

@@ -32,7 +32,7 @@ protected void doFilterInternal(HttpServletRequest request, HttpServletResponse
             secret.validateToken(refreshToken);
         } catch (AuthException e) {
             response.setStatus(HttpStatus.UNAUTHORIZED.value());
-            response.sendRedirect("/html/error-401.html");
+            response.sendRedirect("/html/sign-in.html");
             response.getOutputStream().write(e.getMessage().getBytes());
             return;
         }

src/main/java/se/ton/t210/controller/PageController.java

@@ -0,0 +1,34 @@
+package se.ton.t210.controller;
+
+import org.springframework.stereotype.Controller;
+import org.springframework.web.bind.annotation.GetMapping;
+import se.ton.t210.configuration.annotation.LoginMember;
+import se.ton.t210.domain.type.ApplicationType;
+import se.ton.t210.dto.LoginMemberInfo;
+
+@Controller
+public class PageController {
+
+    @GetMapping("/html/application-information")
+    public String information(@LoginMember LoginMemberInfo memberInfo) {
+        if(memberInfo.getApplicationType() == ApplicationType.PoliceOfficerMale) {
+            return "redirect:/html/application-information1.html";
+        }
+        if(memberInfo.getApplicationType() == ApplicationType.PoliceOfficerFemale) {
+            return "redirect:/html/application-information2.html";
+        }
+        if(memberInfo.getApplicationType() == ApplicationType.FireOfficerMale) {
+            return "redirect:/html/application-information3.html";
+        }
+        if(memberInfo.getApplicationType() == ApplicationType.FireOfficerFemale) {
+            return "redirect:/html/application-information4.html";
+        }
+        if(memberInfo.getApplicationType() == ApplicationType.CorrectionalOfficerFemale) {
+            return "redirect:/html/application-information5.html";
+        }
+        if(memberInfo.getApplicationType() == ApplicationType.CorrectionalOfficerMale) {
+            return "redirect:/html/application-information6.html";
+        }
+        return "/html/application-information1.html";
+    }
+}

src/main/java/se/ton/t210/controller/ScoreController.java

@@ -1,11 +1,13 @@
 package se.ton.t210.controller;
 
+import com.fasterxml.jackson.core.JsonProcessingException;
 import org.springframework.http.ResponseEntity;
 import org.springframework.validation.annotation.Validated;
-import org.springframework.web.bind.annotation.*;
+import org.springframework.web.bind.annotation.GetMapping;
+import org.springframework.web.bind.annotation.PostMapping;
+import org.springframework.web.bind.annotation.RequestBody;
+import org.springframework.web.bind.annotation.RestController;
 import se.ton.t210.configuration.annotation.LoginMember;
-import se.ton.t210.domain.Member;
-import se.ton.t210.domain.type.ApplicationType;
 import se.ton.t210.dto.*;
 import se.ton.t210.service.ScoreService;
 
@@ -32,8 +34,8 @@ public ResponseEntity<ScoreCountResponse> scoreCount(@LoginMember LoginMemberInf
 
     @GetMapping("/api/score/evaluate")
     public ResponseEntity<CalculateEvaluationScoreResponse> evaluationScore(
-                                                         @Valid @NotNull Long evaluationItemId,
-                                                         @Valid @NotNull Float score) {
+            @Valid @NotNull Long evaluationItemId,
+            @Valid @NotNull Float score) {
         final int evaluationScore = scoreService.evaluate(evaluationItemId, score);
         return ResponseEntity.ok(new CalculateEvaluationScoreResponse(evaluationScore));
     }
@@ -45,8 +47,8 @@ public ResponseEntity<List<MonthlyScoreResponse>> scores(@LoginMember LoginMembe
     }
 
     @GetMapping("/api/score/expect")
-    public ResponseEntity<ExpectScoreResponse> expect(@LoginMember LoginMemberInfo member) {
-        final ExpectScoreResponse scoreResponse = scoreService.expect(member.getId(), member.getApplicationType(), LocalDate.now());
+    public ResponseEntity<ExpectScoreResponse> expect(@LoginMember LoginMemberInfo member) throws JsonProcessingException {
+        final ExpectScoreResponse scoreResponse = scoreService.expect(member, LocalDate.now());
         return ResponseEntity.ok(scoreResponse);
     }
 

src/main/java/se/ton/t210/domain/type/ApplicationType.java

@@ -1,35 +1,32 @@
 package se.ton.t210.domain.type;
 
 public enum ApplicationType {
-    PoliceOfficerMale("/static/default.jpg", "경찰직공무원(남)"),
-    PoliceOfficerFemale("/static/default.jpg", "경찰직공무원(여)"),
-    FireOfficerMale("/static/default.jpg", "소방직공무원(남)"),
-    FireOfficerFemale("/static/default.jpg", "소방직공무원(여)"),
-    CorrectionalOfficerMale("/static/default.jpg", "경호직공무원(남)"),
-    CorrectionalOfficerFemale("/static/default.jpg", "경호직공무원(여)");
+    PoliceOfficerMale("/static/default.jpg", "경찰직공무원(남)", 1),
+    PoliceOfficerFemale("/static/default.jpg", "경찰직공무원(여)", 2),
+    FireOfficerMale("/static/default.jpg", "소방직공무원(남)", 3),
+    FireOfficerFemale("/static/default.jpg", "소방직공무원(여)", 4),
+    CorrectionalOfficerMale("/static/default.jpg", "경호직공무원(남)", 5),
+    CorrectionalOfficerFemale("/static/default.jpg", "경호직공무원(여)", 6);
 
     private final String iconImageUrl;
     private final String standardName;
+    private final int mlServerIndex;
 
-    ApplicationType(String iconImageUrl, String standardName) {
+    ApplicationType(String iconImageUrl, String standardName, int mlServerIndex) {
         this.iconImageUrl = iconImageUrl;
         this.standardName = standardName;
+        this.mlServerIndex = mlServerIndex;
     }
 
-    public String getStandardName() {
+    public String standardName() {
         return standardName;
     }
 
     public String iconImageUrl() {
         return iconImageUrl;
     }
 
-    public static String getKeyByName(String name) {
-        for (ApplicationType ApplicationType : values()) {
-            if (ApplicationType.standardName.equals(name)) {
-                return ApplicationType.toString();
-            }
-        }
-        return null;
+    public int mlServerIndex() {
+        return mlServerIndex;
     }
 }