Merge pull request 'feat: add kfu-m-24-1 stubs folder; include eng-it-lean stubs api' (#50) from kfu-m-24-1/eng-it-lean into master

Reviewed-on: #50
Reviewed-by: nekitboy1998 <nekitboy1998@gmail.com>

server/index.js

@@ -42,6 +42,7 @@ app.use(require('./root'))
 /**
  * Добавляйте сюда свои routers.
  */
+app.use('/kfu-m-24-1', require('./routers/kfu-m-24-1'))
 app.use('/epja-2024-1', require('./routers/epja-2024-1'))
 app.use('/todo', require('./routers/todo/routes'))
 app.use('/dogsitters-finder', require('./routers/dogsitters-finder'))

server/routers/epja-2024-1/index.js

@@ -7,4 +7,6 @@ router.use('/cats', require('./cats/index'))
 
 router.use('/ecliptica', require('./ecliptica/index'))
 
+router.use('/sdk', require('./sdk/index'))
+
 module.exports = router

server/routers/epja-2024-1/sdk/index.js

@@ -0,0 +1,123 @@
+const router = require('express').Router();
+const { v4: uuidv4 } = require('uuid');
+
+const workout1 = {
+    id: uuidv4(),
+    title: "Toned upper body",
+    exercises: [
+        { title: "Push ups", repsOrDuration: 12, isTimeBased: false },
+        { title: "Plank", repsOrDuration: 4, isTimeBased: true },
+        { title: "Bicep curl", repsOrDuration: 12, isTimeBased: false, weight: 5 },
+        { title: "Bicep curl", repsOrDuration: 12, isTimeBased: false, weight: 5 },
+        { title: "Bicep curl", repsOrDuration: 12, isTimeBased: false, weight: 5 },
+        { title: "Bicep curl", repsOrDuration: 12, isTimeBased: false, weight: 5 },
+    ],
+    tags: ['Weights', 'Arms', 'Abs', 'Chest', 'Back']
+};
+
+const workout2 = {
+    id: uuidv4(),
+    title: "Tom Platz's legs",
+    exercises: [
+        { title: "Squats", repsOrDuration: 12, isTimeBased: false, weight: 40 },
+        { title: "Leg Press", repsOrDuration: 4, isTimeBased: false, weight: 65 },
+        { title: "Lunges", repsOrDuration: 2, isTimeBased: true }
+    ],
+    tags: ['Weights', 'Legs']
+};
+
+const workout3 = {
+    id: uuidv4(),
+    title: "HIIT",
+    exercises: [
+        { title: "Jumping rope", repsOrDuration: 100, isTimeBased: false },
+        { title: "Burpees", repsOrDuration: 3, isTimeBased: true },
+        { title: "Jumping Jacks", repsOrDuration: 50, isTimeBased: false }
+    ],
+    tags: ['Cardio']
+}
+
+const savedWorkouts = [workout1, workout3];
+
+const trainingWorkouts = [workout2];
+
+router.post('/workout', (req, res) => {
+    const newWorkout = { ...req.body, id: uuidv4() };
+    savedWorkouts.push(newWorkout);
+    res.status(201).json(newWorkout);
+});
+
+router.get('/workouts', (req, res) => {
+    res.json(savedWorkouts);
+});
+
+router.post('/training/workout', (req, res) => {
+    const newWorkout = { ...req.body, id: uuidv4() };
+    trainingWorkouts.push(newWorkout);
+    res.status(201).json(newWorkout);
+});
+
+const trainings = [{ id: uuidv4(), calories: 450, date: new Date("Thu Oct 03 2024 10:05:24 GMT+0300 (Moscow Standard Time)"), emoji: "fuzzy", hours: 1, minutes: 30, isWorkoutSaved: true, workout: workout1.id }];
+
+const days = [
+    new Date("Thu Oct 03 2024 10:05:24 GMT+0300 (Moscow Standard Time)"),
+
+];
+
+router.post('/training', (req, res) => {
+    const newTraining = { ...req.body, id: uuidv4() };
+    trainings.push(newTraining);
+    days.push(newTraining.date);
+    res.status(201).json(newTraining);
+});
+
+router.get('/training', (req, res) => {
+    const { date } = req.query;
+    if (!date) {
+        return res.status(400).json({ message: 'Date query parameter is required' });
+    }
+    const formattedDate = new Date(date);
+    const result = trainings.find(t => new Date(t.date).toDateString() === formattedDate.toDateString());
+    if (result) {
+        res.json(result);
+    } else {
+        res.status(404).json({ message: 'Training not found for the specified date' });
+    }
+});
+
+router.get('/training/workout', (req, res) => {
+    const { id } = req.query;
+    if (!id) {
+        return res.status(400).json({ message: 'Id query parameter is required' });
+    }
+    const result = trainingWorkouts.find(w => w.id === id);
+    if (result) {
+        res.json(result);
+    } else {
+        res.status(404).json({ message: 'Training with such workout not found' });
+    }
+});
+
+router.get('/workout', (req, res) => {
+    const { id } = req.query;
+    if (!id) {
+        return res.status(400).json({ message: 'Id query parameter is required' });
+    }
+    const result = savedWorkouts.find(w => w.id === id);
+    if (result) {
+        res.json(result);
+    } else {
+        res.status(404).json({ message: 'Workout not found' });
+    }
+});
+
+router.get('/trainings', (req, res) => {
+    res.json(trainings);
+});
+
+router.get('/days', (req, res) => {
+    res.json(days);
+})
+
+
+module.exports = router;

server/routers/kfu-m-24-1/eng-it-lean/dictionaries/data/dictionaries.json

@@ -0,0 +1,12 @@
+[
+  {
+    "id": 0,
+    "description": "1000 часто используемых",
+    "imageFilename": "kart1.jpg"
+  },
+  {
+    "id": 1,
+    "description": "10 слов в Data Science",
+    "imageFilename": "kart1.jpg"
+  }
+]

server/routers/kfu-m-24-1/eng-it-lean/dictionaries/data/dictionaryWords.json

@@ -0,0 +1,150 @@
+[
+  {
+    "id": 0,
+    "words": [
+      {
+        "id": 0,
+        "word": "Tech",
+        "definition": "short for technical, relating to the knowledge, machines, or methods used in science and industry. Tech is a whole industry, which includes IT",
+        "examples": [
+          "“As a DevOps engineer I have been working in Tech since 2020.”"
+        ],
+        "synonyms": ["IT"]
+      },
+      {
+        "id": 1,
+        "word": "career path",
+        "definition": "the series of jobs or roles that constitute a person's career, especially one in a particular field",
+        "examples": [
+          "“Technology is an evolving field with a variety of available career paths.”"
+        ],
+        "synonyms": []
+      }
+    ]
+  },
+  {
+    "id": 1,
+    "words": [
+      {
+        "id": 0,
+        "word": "Machine Learning",
+        "translation": "Машинное обучение",
+        "definition": "An approach to artificial intelligence where computers learn from data without being explicitly programmed.",
+        "synonyms": ["Trainable Algorithms", "Automated Learning"],
+        "examples": [
+          "We used machine learning techniques to forecast product demand.",
+          "The movie recommendation system is based on machine learning algorithms.",
+          "Machine learning helped improve the accuracy of speech recognition in our application."
+        ]
+      },
+      {
+        "id": 1,
+        "word": "Neural Network",
+        "translation": "Нейронная сеть",
+        "definition": "A mathematical model inspired by the structure and function of biological neural networks, consisting of interconnected nodes organized in layers that can process information.",
+        "synonyms": ["Artificial Neural Network", "Deep Neural Network"],
+        "examples": [
+          "To process large amounts of data, we created a deep learning neural network.",
+          "This neural network is capable of generating realistic images.",
+          "Using neural networks significantly improved the quality of text translation."
+        ]
+      },
+      {
+        "id": 2,
+        "word": "Algorithm",
+        "translation": "Алгоритм",
+        "definition": "A step-by-step procedure or set of instructions for solving a problem or performing a computation.",
+        "synonyms": ["Procedure", "Method"],
+        "examples": [
+          "The algorithm we developed quickly finds the optimal delivery route.",
+          "This algorithm sorts an array with a minimal number of operations.",
+          "Encryption algorithms ensure secure transmission of data over the internet."
+        ]
+      },
+      {
+        "id": 3,
+        "word": "Data Model",
+        "translation": "Модель данных",
+        "definition": "An abstract representation of the structure of data, describing how data is organized and related to each other.",
+        "synonyms": ["Data Structure", "Schema"],
+        "examples": [
+          "Our data model allows us to efficiently manage relationships between customers and orders.",
+          "The data model was designed considering scalability and performance requirements.",
+          "This data model is used for storing information about social network users."
+        ]
+      },
+      {
+        "id": 4,
+        "word": "Regression",
+        "translation": "Регрессия",
+        "definition": "A statistical method used to determine the relationship between one variable and others.",
+        "synonyms": ["Linear Regression", "Nonlinear Regression"],
+        "examples": [
+          "We applied linear regression to analyze the impact of advertising campaigns on sales.",
+          "Results from the regression analysis showed a strong correlation between customer age and purchase frequency.",
+          "Regression helped us assess how changes in environmental conditions affect crop yield."
+        ]
+      },
+      {
+        "id": 5,
+        "word": "Clustering",
+        "translation": "Кластеризация",
+        "definition": "The process of grouping similar objects into clusters so that objects within the same cluster are more similar to each other than to those in other clusters.",
+        "synonyms": ["Grouping", "Segmentation"],
+        "examples": [
+          "Clustering allowed us to divide customers into several groups according to their purchasing behavior.",
+          "Clustering methods are used to automatically group news by topic.",
+          "As a result of clustering, several market segments were identified, each with its own characteristics."
+        ]
+      },
+      {
+        "id": 6,
+        "word": "Supervised Learning",
+        "translation": "Обучение с учителем",
+        "definition": "A type of machine learning where the algorithm learns from labeled data, meaning data for which correct answers are known.",
+        "synonyms": ["Controlled Learning", "Labeled Classification"],
+        "examples": [
+          "Supervised learning is used to classify emails as spam or not-spam.",
+          "This approach was used to create a model that predicts real estate prices based on multiple parameters.",
+          "Supervised learning helps diagnose diseases at early stages through medical data analysis."
+        ]
+      },
+      {
+        "id": 7,
+        "word": "Data Labeling",
+        "translation": "Разметка данных",
+        "definition": "The process of assigning labels or classes to data so it can be used in supervised learning.",
+        "synonyms": ["Data Annotation", "Tagging"],
+        "examples": [
+          "Before starting model training, we labeled the data by assigning each photo an animal category.",
+          "Data labeling includes marking user reviews as positive or negative.",
+          "Text documents were labeled with special tags for subsequent analysis."
+        ]
+      },
+      {
+        "id": 8,
+        "word": "Hyperparameters",
+        "translation": "Гиперпараметры",
+        "definition": "Parameters that define the structure and behavior of a machine learning model, set before the learning process begins.",
+        "synonyms": ["Model Settings", "Configuration Parameters"],
+        "examples": [
+          "Optimizing hyperparameters enabled us to enhance the performance of our machine learning model.",
+          "Hyperparameters include settings such as the number of layers in a neural network and the learning rate.",
+          "Choosing the right hyperparameters is crucial for achieving high model accuracy."
+        ]
+      },
+      {
+        "id": 9,
+        "word": "Model Validation",
+        "translation": "Валидация модели",
+        "definition": "The process of evaluating the quality of a model by testing it on new, previously unseen data.",
+        "synonyms": ["Model Testing", "Model Verification"],
+        "examples": [
+          "After completing the training, we validated the model using a test dataset.",
+          "During model validation, its ability to make accurate predictions on new data is checked.",
+          "Validation showed that the model is robust against changes in data and has low generalization error."
+        ]
+      }
+    ]
+  }
+]

server/routers/kfu-m-24-1/eng-it-lean/dictionaries/index.js

@@ -0,0 +1,21 @@
+const router = require("express").Router();
+
+module.exports = router;
+
+const data = require("./data/dictionaries.json");
+const wordsData = require("./data/dictionaryWords.json");
+
+router.get("/", (req, res) => {
+  res.send(data);
+});
+
+router.get("/:id", (req, res) => {
+  const id = parseInt(req.params.id);
+  const words = wordsData.find((word) => word.id === id);
+
+  if (!words) {
+    return res.status(404).send("Not found");
+  }
+
+  res.send(words);
+});

server/routers/kfu-m-24-1/eng-it-lean/index.js

@@ -0,0 +1,13 @@
+const router = require("express").Router();
+
+const dictionariesRouter = require("./dictionaries");
+module.exports = router;
+
+const delay =
+  (ms = 1000) =>
+  (req, res, next) => {
+    setTimeout(next, ms);
+  };
+
+router.use(delay());
+router.use("/dictionaries", dictionariesRouter);

server/routers/kfu-m-24-1/index.js

@@ -0,0 +1,7 @@
+const { Router } = require('express')
+const router = Router()
+
+router.use('/eng-it-lean', require('./eng-it-lean/index'))
+
+module.exports = router
+