♻️ Refactor code for readability and consistency

Several classes across various modules have been refactored to improve
readability by adding line breaks to long functions and making
indentation consistent. No functionality changes have been introduced.
In addition, unnecessary imports have been removed improving overall
cleanliness of the code.

.editorconfig

@@ -0,0 +1,9 @@
+root = true
+
+[{*.kt,*.kts}]
+charset = utf-8
+end_of_line = lf
+indent_size = 2
+indent_style = space
+insert_final_newline = true
+trim_trailing_whitespace = true

restaurants/README.md

@@ -1,36 +1,46 @@
 # Restaurants
 
-This module is responsible for fetching restaurant items from USP (through [USP Restaurant API][1]) and persist them in a PostgresQL table, while also providing an API for the retrieval of these items.
+This module is responsible for fetching restaurant items from USP (through [USP Restaurant API][1]) and persist them in
+a PostgresQL table, while also providing an API for the retrieval of these items.
 
 ## Fetching from USP
 
-We trust the [USP Restaurant API][1] definitions, and with them the [USPRestaurantItemRepository][3] is able to fetch items from USP's api.
+We trust the [USP Restaurant API][1] definitions, and with them the [USPRestaurantItemRepository][3] is able to fetch
+items from USP's api.
 
 ### Parsing the menu
 
-It's important for this application to parse all values, as clients are going to expect a smarter output instead of just the menus (otherwise, why would this module be necessary?). For that we have the [MenuParsers][4]. We try with our best effort to parse the menus, however there is some difficulty as restaurants won't always report items in the same way.
+It's important for this application to parse all values, as clients are going to expect a smarter output instead of just
+the menus (otherwise, why would this module be necessary?). For that we have the [MenuParsers][4]. We try with our best
+effort to parse the menus, however there is some difficulty as restaurants won't always report items in the same way.
 
 A future point of improvement is being able to parse menus without such a brittle and forced solution.
 
 ## Saving to Postgres
 
-Every item obtained from USP is piped into a PostgresQL item. This enables us to respond to clients even when USP's servers are down (which happen quite often).
+Every item obtained from USP is piped into a PostgresQL item. This enables us to respond to clients even when USP's
+servers are down (which happen quite often).
 
 ## Cache
 
-When an item is successfully obtained, it's first persisted on the Postgres table (see above), and then kept in an in-memory cache. This cache allow us to quickly respond users while keeping costs down.
+When an item is successfully obtained, it's first persisted on the Postgres table (see above), and then kept in an
+in-memory cache. This cache allow us to quickly respond users while keeping costs down.
 
-It's a good idea to use a cache here, as the data that clients want is usually the menus for the current week, making cache hits very likely.
+It's a good idea to use a cache here, as the data that clients want is usually the menus for the current week, making
+cache hits very likely.
 
 ## Restaurant Job
 
-In order to have all records available - even for restaurants that aren't accessed regularly - we continuously try to get all restaurants from USP, using a fixed rate schedule in [RestaurantJob class][5].
+In order to have all records available - even for restaurants that aren't accessed regularly - we continuously try to
+get all restaurants from USP, using a fixed rate schedule in [RestaurantJob class][5].
 
 This will both refresh our current cache and save any new menus to our persistent storage.
 
 
-
 [1]: https://github.com/JopiterApp/USP-Restaurant-API
+
 [3]: src/main/kotlin/repository/usp/USPRestaurantItemRepository.kt
+
 [4]: src/main/kotlin/repository/usp/MenuParser.kt
+
 [5]: src/main/kotlin/RestaurantJob.kt

restaurants/build.gradle.kts

@@ -15,7 +15,6 @@
  * You should have received a copy of the GNU Affero General Public License
  * along with this program.  If not, see <https://www.gnu.org/licenses/>.
  */
-import org.jetbrains.kotlin.noarg.gradle.NoArgExtension
 
 plugins {
   kotlin("plugin.spring")
@@ -51,4 +50,4 @@ flyway {
   url = System.getenv("JDBC_URL") ?: "jdbc:postgresql://localhost:5432/jopiter"
   user = System.getenv("DATABASE_USER") ?: "jopiter"
   password = System.getenv("DATABASE_PASS") ?: "password"
-}
+}

restaurants/classifier/ClassifierDesign.md

@@ -1,9 +1,9 @@
 # Classifier Design
 
-
 ### Temos:
 
-Arquivos CSV onde a primeira coluna representa o nome do item e todas as outras colunas representam detalhes/categorias/classes dos itens.
+Arquivos CSV onde a primeira coluna representa o nome do item e todas as outras colunas representam
+detalhes/categorias/classes dos itens.
 
 ### Queremos
 
@@ -22,27 +22,37 @@ CSV:
 João,Olhos Castanhos,Cabelo Preto
 
 Tupla:
+
 ```
 [nome = João, colunas=[Olhos Castanhos, Cabelo Preto]]
 ```
 
-
 ## Dados do Classificador
 
 ### Para treinar:
-Receberá todas as tuplas de um determinado grupo. Todas são semelhantes entre si, e possuem a mesma quantidade de colunas. O classificador só será capaz de comparar tipos que ele já conhece
+
+Receberá todas as tuplas de um determinado grupo. Todas são semelhantes entre si, e possuem a mesma quantidade de
+colunas. O classificador só será capaz de comparar tipos que ele já conhece
 
 ### Para prever/classificar
+
 Receberá apenas o nome e terá que deduzir as outras tuplas a partir disso
 
 ## Implementação do classificador
+
 1. CSV é convertido para N-Tuplas
 2. Tuplas passam por tratamento
-   1. corpus <= Transformar o nome com BagOfWords (lib smile kotlin nlp bag) passando a lista de stop words (TODO: melhorar lista) e um stemmer (FIXME, explicar melhor) de palavras em português
-   2. vocabulario <= todos os nomes das tuplas, após passado as stop words e o stemmer
-   3. corpusVetorizado <= Com o corpus (nomes como bag of words) e o vocabulário, contar quantas vezes cada vocábulo aparece em cada documento, armazenando cada documento
-   4. dadosProntosParaModelagem <= Usamos o algorítimo tfidf para melhorar a definição de importância de cada termo de cada documento (FIXME, explicar melhor)
+    1. corpus <= Transformar o nome com BagOfWords (lib smile kotlin nlp bag) passando a lista de stop words (TODO:
+       melhorar lista) e um stemmer (FIXME, explicar melhor) de palavras em português
+    2. vocabulario <= todos os nomes das tuplas, após passado as stop words e o stemmer
+    3. corpusVetorizado <= Com o corpus (nomes como bag of words) e o vocabulário, contar quantas vezes cada vocábulo
+       aparece em cada documento, armazenando cada documento
+    4. dadosProntosParaModelagem <= Usamos o algorítimo tfidf para melhorar a definição de importância de cada termo de
+       cada documento (FIXME, explicar melhor)
 3. Com os dados prontos para modelagem, treinamos um modelo diferente para cada uma das N colunas:
-   1. Primeiro através de um algorítimo OVR (One Versus Rest) (FIXME explicar melhor OVR)
-   2. Este OVR será alimentado com os dados organizados em um vetor de suporte (support vector machine / SVM) (FIXME adicionar artigo que  explicar porque svm é o melhor)
-4. Estes modelos gerados compõem o clasificador, e podem ser exportados para armazenamento (economizando assim computação futura). É possível estimar pratos futuros neste classificador, tratando o novo documento da mesma forma que os antigos. Dessa forma temos as probabilidades que os modelos dão para cada uma das N colunas
+    1. Primeiro através de um algorítimo OVR (One Versus Rest) (FIXME explicar melhor OVR)
+    2. Este OVR será alimentado com os dados organizados em um vetor de suporte (support vector machine / SVM) (FIXME
+       adicionar artigo que explicar porque svm é o melhor)
+4. Estes modelos gerados compõem o clasificador, e podem ser exportados para armazenamento (economizando assim
+   computação futura). É possível estimar pratos futuros neste classificador, tratando o novo documento da mesma forma
+   que os antigos. Dessa forma temos as probabilidades que os modelos dão para cada uma das N colunas

restaurants/classifier/src/main/kotlin/app/jopiter/restaurants/classifier/StopwordsProvider.kt

@@ -1,7 +1,5 @@
 package app.jopiter.restaurants.classifier
 
-import kotlin.streams.toList
-
 @Suppress("RECEIVER_NULLABILITY_MISMATCH_BASED_ON_JAVA_ANNOTATIONS")
 class StopwordsProvider {
 
@@ -10,4 +8,5 @@ class StopwordsProvider {
   }
 }
 
-private fun portugueseStopwordsFile() = StopwordsProvider::class.java.classLoader.getResourceAsStream("portuguese_stopwords.txt")!!
+private fun portugueseStopwordsFile() =
+  StopwordsProvider::class.java.classLoader.getResourceAsStream("portuguese_stopwords.txt")!!

restaurants/classifier/src/test/kotlin/app/jopiter/restaurants/classifier/ClassifierTests.kt

@@ -18,8 +18,6 @@
 
 package app.jopiter.restaurants.classifier
 
-import app.jopiter.restaurants.classifier.ClassifiableRow
-import app.jopiter.restaurants.classifier.Classifier
 import io.kotest.core.spec.style.FunSpec
 import io.kotest.core.spec.style.funSpec
 import io.kotest.matchers.doubles.shouldBeGreaterThanOrEqual

restaurants/src/main/kotlin/app/jopiter/restaurants/RestaurantController.kt

@@ -21,7 +21,6 @@ package app.jopiter.restaurants
 import app.jopiter.restaurants.classifier.RestaurantItemClassifier
 import app.jopiter.restaurants.model.Campus
 import app.jopiter.restaurants.model.ClassifiedRestaurantItem
-import app.jopiter.restaurants.model.RestaurantItem
 import app.jopiter.restaurants.repository.RestaurantItemRepository
 import io.swagger.v3.oas.annotations.Operation
 import io.swagger.v3.oas.annotations.Parameter
@@ -40,48 +39,56 @@ import java.time.LocalDate
 @RestController
 @RequestMapping("\${api.base.path}/restaurants")
 class RestaurantController(
-    private val restaurantItemRepository: RestaurantItemRepository,
-    private val restaurantItemClassifier: RestaurantItemClassifier
+  private val restaurantItemRepository: RestaurantItemRepository,
+  private val restaurantItemClassifier: RestaurantItemClassifier
 ) {
 
-    @Operation(
-        summary = "List all restaurants and their campi",
-        description = "List all restaurants that are available, including to which campus it belongs",
-        tags = ["restaurant"],
-        responses = [
-            ApiResponse(responseCode = "200", content = [
-                Content(array = ArraySchema(schema = Schema(implementation = Campus::class)),
-                    mediaType = "application/json")
-            ])
+  @Operation(
+    summary = "List all restaurants and their campi",
+    description = "List all restaurants that are available, including to which campus it belongs",
+    tags = ["restaurant"],
+    responses = [
+      ApiResponse(
+        responseCode = "200", content = [
+          Content(
+            array = ArraySchema(schema = Schema(implementation = Campus::class)),
+            mediaType = "application/json"
+          )
         ]
-    )
-    @GetMapping
-    fun list() = Campus.values().toList()
+      )
+    ]
+  )
+  @GetMapping
+  fun list() = Campus.values().toList()
 
-    @Operation(
-        summary = "List Items",
-        description = "Retrieves all items for the chosen dates and restaurant",
-        tags = ["restaurant"],
+  @Operation(
+    summary = "List Items",
+    description = "Retrieves all items for the chosen dates and restaurant",
+    tags = ["restaurant"],
 
-        parameters = [
-            Parameter(
-                name = "restaurantId", description = "The restaurant ID as defined by /restaurants", required = true
-            ),
-            Parameter(
-                name = "date", description = "The dates you want to fetch items for. ISO_LOCAL_DATE format (yyyy-MM-dd)"
-            )
-        ],
+    parameters = [
+      Parameter(
+        name = "restaurantId", description = "The restaurant ID as defined by /restaurants", required = true
+      ),
+      Parameter(
+        name = "date", description = "The dates you want to fetch items for. ISO_LOCAL_DATE format (yyyy-MM-dd)"
+      )
+    ],
 
-        responses = [
-            ApiResponse(responseCode = "200", content = [
-                Content(array = ArraySchema(schema = Schema(implementation = ClassifiedRestaurantItem::class)),
-                    mediaType = "application/json")
-            ])
+    responses = [
+      ApiResponse(
+        responseCode = "200", content = [
+          Content(
+            array = ArraySchema(schema = Schema(implementation = ClassifiedRestaurantItem::class)),
+            mediaType = "application/json"
+          )
         ]
-    )
-    @GetMapping("/items")
-    fun items(
-        @RequestParam("restaurantId") restaurantId: Int,
-        @RequestParam("date") @DateTimeFormat(iso = DATE) dates: Set<LocalDate>,
-    ) = restaurantItemRepository.get(restaurantId, dates).map(restaurantItemClassifier::classify)
+      )
+    ]
+  )
+  @GetMapping("/items")
+  fun items(
+    @RequestParam("restaurantId") restaurantId: Int,
+    @RequestParam("date") @DateTimeFormat(iso = DATE) dates: Set<LocalDate>,
+  ) = restaurantItemRepository.get(restaurantId, dates).map(restaurantItemClassifier::classify)
 }

restaurants/src/main/kotlin/app/jopiter/restaurants/RestaurantJob.kt

@@ -23,21 +23,18 @@ import app.jopiter.restaurants.repository.RestaurantItemRepository
 import org.slf4j.LoggerFactory
 import org.springframework.scheduling.annotation.Scheduled
 import org.springframework.stereotype.Component
-import java.time.LocalDate.now
-import java.util.concurrent.ScheduledExecutorService
-import java.util.concurrent.TimeUnit.MINUTES
 
 @Component
 class RestaurantJob(
-    private val repository: RestaurantItemRepository
+  private val repository: RestaurantItemRepository
 ) {
 
-    private val logger = LoggerFactory.getLogger(this::class.java)
+  private val logger = LoggerFactory.getLogger(this::class.java)
 
-    @Scheduled(fixedRate = 15_000)
-    fun run() = Restaurant.entries.forEach {
-        logger.info("Starting scheduled execution to fetch $it")
-        repository.fetchFromUsp(it.id)
-        logger.info("Finished scheduled execution to fetch $it")
-    }
+  @Scheduled(fixedRate = 15_000)
+  fun run() = Restaurant.entries.forEach {
+    logger.info("Starting scheduled execution to fetch $it")
+    repository.fetchFromUsp(it.id)
+    logger.info("Finished scheduled execution to fetch $it")
+  }
 }

restaurants/src/main/kotlin/app/jopiter/restaurants/classifier/RestaurantItemClassifier.kt

@@ -5,8 +5,8 @@ import app.jopiter.restaurants.model.DessertFoodGroup
 import app.jopiter.restaurants.model.DessertItem
 import app.jopiter.restaurants.model.DessertPreparation
 import app.jopiter.restaurants.model.ProteinFoodGroup
-import app.jopiter.restaurants.model.ProteinPreparation
 import app.jopiter.restaurants.model.ProteinItem
+import app.jopiter.restaurants.model.ProteinPreparation
 import app.jopiter.restaurants.model.RestaurantItem
 import app.jopiter.restaurants.model.VegetarianFoodGroup
 import app.jopiter.restaurants.model.VegetarianItem
@@ -45,17 +45,29 @@ class RestaurantItemClassifier(
 
   fun classifyProtein(item: String): ProteinItem {
     val classification = proteinClassifier.classify(item)
-    return ProteinItem(item, ProteinFoodGroup.find(classification.foodGroup), ProteinPreparation.find(classification.preparation))
+    return ProteinItem(
+      item,
+      ProteinFoodGroup.find(classification.foodGroup),
+      ProteinPreparation.find(classification.preparation)
+    )
   }
 
   fun classifyVegetarian(item: String): VegetarianItem {
     val classification = vegetarianClassifier.classify(item)
-    return VegetarianItem(item, VegetarianFoodGroup.find(classification.foodGroup), VegetarianPreparation.find(classification.preparation))
+    return VegetarianItem(
+      item,
+      VegetarianFoodGroup.find(classification.foodGroup),
+      VegetarianPreparation.find(classification.preparation)
+    )
   }
 
   fun classifyDessert(item: String): DessertItem {
     val classification = dessertClassifier.classify(item)
-    return DessertItem(item, DessertFoodGroup.find(classification.foodGroup), DessertPreparation.find(classification.preparation))
+    return DessertItem(
+      item,
+      DessertFoodGroup.find(classification.foodGroup),
+      DessertPreparation.find(classification.preparation)
+    )
   }
 
-}
+}

restaurants/src/main/kotlin/app/jopiter/restaurants/model/ClassifiedRestaurantItem.kt

@@ -1,7 +1,6 @@
 package app.jopiter.restaurants.model
 
 import com.fasterxml.jackson.annotation.JsonFormat
-import org.springframework.stereotype.Component
 import java.time.LocalDate