Merge branch 'master' of...

Merge branch 'master' of http://gitlab.sliit.lk/22_23-j-36/easy-quest-smart-recruitment-tool-with-ai-backend into it19953298

.gitignore

-data/attempts
-
-# Logs
-logs
-*.log
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-
-# Runtime data
-pids
-*.pid
-*.seed
-*.pid.lock
-
-# Directory for instrumented libs generated by jscoverage/JSCover
-lib-cov
-
-# Coverage directory used by tools like istanbul
-coverage
-
-# nyc test coverage
-.nyc_output
-
-# Grunt intermediate storage (http://gruntjs.com/creating-plugins#storing-task-files)
-.grunt
-
-# Bower dependency directory (https://bower.io/)
-bower_components
-
-# node-waf configuration
-.lock-wscript
-
-# Compiled binary addons (https://nodejs.org/api/addons.html)
-build/Release
-
-# Dependency directories
-node_modules/
-jspm_packages/
-
-# TypeScript v1 declaration files
-typings/
-
-# Optional npm cache directory
-.npm
-
-# Optional eslint cache
-.eslintcache
-
-# Optional REPL history
-.node_repl_history
-
-# Output of 'npm pack'
-*.tgz
-
-# Yarn Integrity file
-.yarn-integrity
-
-# dotenv environment variables file
-.env
-
-# next.js build output
-.next

NodeServer/.gitignore

+lib
+node_modules
\ No newline at end of file

package.json → NodeServer/package.json

@@ -2,29 +2,36 @@
   "name": "keystrokedynamics",
   "version": "1.0.0",
   "description": "",
-  "main": "server.js",
   "scripts": {
-    "test": "echo \"Error: no test specified\" && exit 1",
-    "start": "node server.js",
-    "dev": "nodemon server.js",
+    "build": "yarn tsc",
+    "start": "yarn build && node lib/index.js",
+    "dev": "yarn build && nodemon lib/index.js",
     "burnthemall": "rm -rf node_modules package-lock.json && npm i"
   },
   "author": "Namit Nathwani",
   "license": "ISC",
   "dependencies": {
-    "express": "^4.17.1",
-    "express-winston": "^4.0.3",
+    "axios": "^1.3.4",
+    "bcryptjs": "^2.4.3",
+    "cors": "^2.8.5",
+    "express": "^4.18.2",
+    "jsonwebtoken": "^9.0.0",
     "lodash": "^4.17.19",
     "mathjs": "^7.2.0",
     "mongoose": "^5.9.25",
-    "simple-statistics": "^7.1.0",
-    "winston": "^3.3.3"
+    "simple-statistics": "^7.1.0"
   },
   "devDependencies": {
+    "@types/bcryptjs": "^2.4.2",
+    "@types/express": "^4.17.17",
+    "@types/jsonwebtoken": "^9.0.1",
+    "@types/lodash": "^4.14.191",
+    "@types/node": "^18.15.1",
     "eslint": "^7.5.0",
     "eslint-config-airbnb": "^18.2.0",
     "eslint-config-airbnb-base": "^14.2.0",
     "eslint-plugin-import": "^2.22.0",
-    "nodemon": "^2.0.4"
+    "nodemon": "^2.0.4",
+    "typescript": "^4.9.5"
   }
 }

NodeServer/src/config/contants.ts

+export const JWT_SECRET = "JWT_SECRET_SMART_RECRUITER";
+export const MONGO_URL =
+  "mongodb+srv://smart_req_admin:CvCbwaX6A4rnJuwv@smartreq.duejzyf.mongodb.net/App?retryWrites=true&w=majority";
+export const API_PORT = process.env.API_PORT || 5000;
+export const DEFAULT_CONTROLS = {
+  standard: {
+    sd: 1.5,
+    threshold: 65,
+    use: true,
+  },
+  fullStandard: {
+    threshold: 1,
+    use: true,
+  },
+};
+export const UTILITY_SERVER = "http://127.0.0.1:8000";

NodeServer/src/config/types.ts

+import { Request } from "express";
+import { Query } from "express-serve-static-core";
+
+export enum USER_TYPE {
+  ORGANIZATION = "ORGANIZATION",
+  CANDIDATE = "CANDIDATE",
+}
+
+export type KeyDetails = {
+  key: any;
+  code: any;
+  shiftKey: any;
+  time: number;
+};
+
+export type KeystrokeType = {
+  keys: string[];
+  times: number[] | any;
+  sums?: number[];
+  means?: number[];
+  sd?: number[];
+  filteredMeans?: number[];
+  filteredSd?: number[];
+  covMatrix?: number[][];
+};
+
+export type KeystrokeDataType = {
+  hold: KeystrokeType;
+  flight: KeystrokeType;
+  dd: KeystrokeType;
+  full: KeystrokeType;
+};
+
+export type AddressType = {
+  addressLine: string;
+  city: string;
+  country: string;
+};
+
+export type AuthType = {
+  userType: USER_TYPE;
+  email: string;
+  userId?: string;
+  password: string;
+  keystrokeDataTimestamps: number[];
+  keystrokeData: KeystrokeDataType;
+  controls?: ControlsType;
+};
+
+export type ResumeDataType = {
+  skills: string[] | null;
+  degree: string[] | null;
+  designation: string[] | null;
+  experience: string[] | null;
+  company_names: string[] | null;
+  no_of_pages: number;
+  total_experience: number;
+};
+
+export type CandidateType = {
+  _id?: string;
+  name: string;
+  bio: string;
+  contacts: {
+    email: string;
+    phone: string;
+    address: AddressType;
+    residentialAddress?: AddressType;
+  };
+  dateOfBirth: string;
+  jobIds: string[];
+  profilePicture: string;
+  state: "INTIAL" | "READY";
+  resume?: string;
+  resumeData?: ResumeDataType;
+};
+
+export type OrganizationType = {
+  _id?: string;
+  name: string;
+  description: string;
+  contacts: {
+    email: string;
+    phone: string[];
+    address: AddressType;
+    website: string;
+  };
+  profilePicture: string;
+};
+
+export type ControlsType = {
+  standard: {
+    sd: number;
+    threshold: number;
+    use: boolean;
+  };
+  fullStandard: {
+    threshold: number;
+    use: boolean;
+  };
+};
+
+export type JobType = {
+  _id: string;
+  title: string;
+  description: string;
+  primarySkills: string[];
+  secondarySkills?: string[];
+  salary: {
+    min: number;
+    max: number;
+    currency: string;
+  };
+  applications: string[];
+  organization: string;
+};
+
+export type ApplicationType = {
+  candidate: string;
+  job: string;
+  status: "Pending" | "Accepted" | "In progress" | "Rejected";
+  interview?: {
+    date: string;
+    time: string;
+    link: string;
+    videoRef?: string;
+  };
+  score: {
+    primary: number;
+    primatyMatch: string[];
+    secondary: number;
+    secondaryMatch: string[];
+    similarity: number;
+    total: number;
+  };
+};
+
+export interface TypedRequest<T extends Query, U> extends Request {
+  body: U;
+  query: T;
+}
+
+//PAYLOADS
+export type SignUpPayload = {
+  passwords: string[];
+  keydown: KeyDetails[][];
+  keyup: KeyDetails[][];
+  email: string;
+  userType: USER_TYPE;
+  candidate?: CandidateType;
+  organization?: OrganizationType;
+};
+
+export type SignInPayload = {
+  password: string;
+  keydown: KeyDetails[];
+  keyup: KeyDetails[];
+  email: string;
+  userType: USER_TYPE;
+};
+
+export type UpdatePasswordPayload = {
+  passwords: string[];
+  keydown: KeyDetails[][];
+  keyup: KeyDetails[][];
+  oldPassword: string;
+};

NodeServer/src/index.ts

+import express from "express";
+import mongoose from "mongoose";
+import cors from "cors";
+import { API_PORT, MONGO_URL } from "./config/contants";
+
+const app = express();
+app.use(cors());
+
+// Routes
+const authRoute = require("./routes/auth");
+const userRoute = require("./routes/user");
+const jobsRoute = require("./routes/jobs");
+
+// Service Initialisation
+mongoose.connect(MONGO_URL, {
+  useFindAndModify: false,
+  useNewUrlParser: true,
+  useUnifiedTopology: true,
+  useCreateIndex: true,
+});
+
+// Express Initialisation
+app.use(express.urlencoded({ extended: true }));
+app.use(express.json());
+
+app.use(express.urlencoded({ extended: false }));
+
+// Routes
+app.use("/auth", authRoute);
+app.use("/user", userRoute);
+app.use("/jobs", jobsRoute);
+
+app.listen(API_PORT, () => console.log(`Listening on port ${API_PORT}`));

NodeServer/src/middlewares/auth.ts

+import { Request, Response, NextFunction } from "express";
+import * as jwt from "jsonwebtoken";
+import { JWT_SECRET } from "../config/contants";
+import { TypedRequest, USER_TYPE } from "../config/types";
+import Auth from "../models/Auth";
+
+export const authMiddleware = (
+  req: Request,
+  res: Response,
+  next: NextFunction
+) => {
+  const token = req.header("auth_token");
+
+  if (!token) {
+    return res.status(401).send("Authorization denied");
+  } else {
+    const decodedToken = <jwt.JwtPayload>jwt.verify(token, JWT_SECRET);
+    if (decodedToken) {
+      req.query.userId = decodedToken.userId;
+      return next();
+    } else {
+      return res.status(401).send("Unauthorized Access");
+    }
+  }
+};
+
+export const organizationMiddleware = async (
+  req: TypedRequest<{ userId: string }, any>,
+  res: Response,
+  next: NextFunction
+) => {
+  try {
+    const org = await Auth.findOne({ userId: req.query.userId });
+    if (org && org.userType === USER_TYPE.ORGANIZATION) {
+      return next();
+    } else {
+      throw new Error("Organization not found");
+    }
+  } catch (error) {
+    return res.status(400).send(error);
+  }
+};
+
+export const candidateMiddleware = async (
+  req: TypedRequest<{ userId: string }, any>,
+  res: Response,
+  next: NextFunction
+) => {
+  try {
+    const org = await Auth.findOne({ userId: req.query.userId });
+    if (org && org.userType === USER_TYPE.CANDIDATE) {
+      return next();
+    } else {
+      throw new Error("Candidate not found");
+    }
+  } catch (error) {
+    return res.status(400).send(error);
+  }
+};

NodeServer/src/models/Application.ts

+import { Schema, model } from "mongoose";
+import { ApplicationType } from "../config/types";
+
+const applicationSchema = new Schema<ApplicationType>({
+  candidate: { type: Schema.Types.ObjectId, ref: "candidates" },
+  job: { type: Schema.Types.ObjectId, ref: "jobs" },
+  status: { type: String, require: false, default: "Pending" },
+  interview: {
+    type: {
+      date: String,
+      time: String,
+      link: String,
+      videoRef: String,
+    },
+    require: false,
+  },
+  score: {
+    type: {
+      primary: Number,
+      primatyMatch: [String],
+      secondary: Number,
+      secondaryMatch: [String],
+      similarity: Number,
+      total: Number,
+    },
+    require: false,
+  },
+});
+
+const Application = model<ApplicationType>("applications", applicationSchema);
+export default Application;

models/User.js → NodeServer/src/models/Auth.ts

-const mongoose = require('mongoose');
+import { Schema, model } from "mongoose";
+import { AuthType, USER_TYPE } from "../config/types";
 
-const userSchema = new mongoose.Schema({
-  username: { type: String, unique: true },
+const authSchema = new Schema<AuthType>({
+  userType: { type: String, require: true, default: USER_TYPE.CANDIDATE },
+  email: { type: String, unique: true },
+  userId: String,
   password: String,
-
   keystrokeDataTimestamps: [Date],
   keystrokeData: {
     hold: {
@@ -47,6 +49,18 @@ const userSchema = new mongoose.Schema({
       covMatrix: [[Number]],
     },
   },
+  controls: {
+    standard: {
+      sd: { type: Number, default: 1.5, require: false },
+      threshold: { type: Number, default: 65, require: false },
+      use: { type: Boolean, default: true, require: false },
+    },
+    fullStandard: {
+      threshold: { type: Number, default: 1, require: false },
+      use: { type: Boolean, default: true, require: false },
+    },
+  },
 });
 
-module.exports = mongoose.model('User', userSchema);
+const Auth = model<AuthType>("Auth", authSchema);
+export default Auth;

NodeServer/src/models/Candidate.ts

+import { Schema, model } from "mongoose";
+import { AddressType, CandidateType, ResumeDataType } from "../config/types";
+
+const AddressSchema = new Schema<AddressType>(
+  {
+    addressLine: String,
+    city: String,
+    country: String,
+  },
+  { id: false }
+);
+
+const ContactsSchema = new Schema<AddressType>(
+  {
+    email: { type: String, require: false },
+    phone: { type: String, require: false },
+    address: { type: AddressSchema, require: false },
+    residentialAddress: { type: AddressSchema, require: false },
+  },
+  { id: false }
+);
+const ResumeDataSchema = new Schema<ResumeDataType>(
+  {
+    skills: { type: [String], require: false },
+    degree: { type: [String], require: false },
+    designation: { type: [String], require: false },
+    experience: { type: [String], require: false },
+    company_names: { type: [String], require: false },
+    no_of_pages: { type: Number, require: false },
+    total_experience: { type: Number, require: false },
+  },
+  { id: false }
+);
+
+const candidateSchema = new Schema<CandidateType>({
+  name: String,
+  bio: String,
+  contacts: { type: ContactsSchema, require: false },
+  dateOfBirth: String,
+  jobIds: [{ type: Schema.Types.ObjectId, ref: "jobs" }],
+  profilePicture: String,
+  state: { type: String, default: "INTIAL" },
+  resume: { type: String, require: false },
+  resumeData: { type: ResumeDataSchema, require: false },
+});
+
+const Candidates = model<CandidateType>("candidates", candidateSchema);
+export default Candidates;

NodeServer/src/models/Job.ts

+import { Schema, model } from "mongoose";
+import { JobType } from "../config/types";
+
+const jobSchema = new Schema<JobType>({
+  title: String,
+  description: String,
+  primarySkills: { type: [String], require: true },
+  secondarySkills: { type: [String], require: false },
+  salary: {
+    min: Number,
+    max: Number,
+    currency: String,
+  },
+  applications: [{ type: Schema.Types.ObjectId, ref: "applications" }],
+  organization: [{ type: Schema.Types.ObjectId, ref: "organizations" }],
+});
+
+const Jobs = model<JobType>("jobs", jobSchema);
+export default Jobs;

NodeServer/src/models/Organization.ts

+import { Schema, model } from "mongoose";
+import { AddressType, OrganizationType } from "../config/types";
+
+const AddressSchema = new Schema<AddressType>(
+  {
+    addressLine: String,
+    city: String,
+    country: String,
+  },
+  { id: false }
+);
+
+const ContactsSchema = new Schema<AddressType>(
+  {
+    email: String,
+    phone: String,
+    address: { type: AddressSchema },
+    website: String,
+  },
+  { id: false }
+);
+
+const organizationSchema = new Schema<OrganizationType>({
+  name: String,
+  description: String,
+  contacts: { type: ContactsSchema, require: false },
+  profilePicture: String,
+});
+
+const Organizations = model<OrganizationType>(
+  "organizations",
+  organizationSchema
+);
+export default Organizations;

NodeServer/src/routes/auth.ts

+import { Router } from "express";
+import * as _ from "lodash";
+import * as jwt from "jsonwebtoken";
+import * as bcrypt from "bcryptjs";
+import {
+  ControlsType,
+  KeystrokeDataType,
+  SignInPayload,
+  SignUpPayload,
+  TypedRequest,
+  UpdatePasswordPayload,
+  USER_TYPE,
+} from "../config/types";
+import Auth from "../models/Auth";
+import Candidates from "../models/Candidate";
+import Organizations from "../models/Organization";
+import {
+  processKeystrokeData,
+  findUser,
+  createSignupDataFromProcessedData,
+  addAttemptToKeystrokeData,
+  computeDataTendencies,
+  processAttempt,
+  updatePasswordFromProcessedData,
+} from "../utilities/keystroke";
+import { DEFAULT_CONTROLS, JWT_SECRET } from "../config/contants";
+import { authMiddleware } from "../middlewares/auth";
+
+const router = Router();
+
+router.post("/signup", async (req: TypedRequest<{}, SignUpPayload>, res) => {
+  const {
+    passwords,
+    keydown,
+    keyup,
+    email,
+    userType,
+    candidate,
+    organization,
+  } = req.body;
+  const attemptCount = passwords.length;
+
+  const auth = await Auth.findOne({ email });
+
+  if (auth) {
+    return res
+      .status(403)
+      .json({ success: false, msg: "User already exists!" });
+  }
+
+  const processedAttempts: KeystrokeDataType[] = Array(attemptCount)
+    .fill(0)
+    .map((v, i) =>
+      processKeystrokeData({ keydown: keydown[i], keyup: keyup[i] })
+    );
+
+  const passwordsEqual = processedAttempts.every((v) =>
+    _.isEqual(v.full.keys, processedAttempts[0].full.keys)
+  );
+
+  if (!passwordsEqual) {
+    return res
+      .status(403)
+      .json({ success: false, msg: "Passwords don't match" });
+  }
+
+  try {
+    let newUser;
+    if (userType === USER_TYPE.CANDIDATE) {
+      newUser = await Candidates.create(candidate);
+    } else {
+      newUser = await Organizations.create(organization);
+    }
+
+    const signupData = await createSignupDataFromProcessedData(
+      email,
+      passwords,
+      userType,
+      newUser.id,
+      processedAttempts
+    );
+
+    await Auth.create(signupData);
+
+    return res.json({
+      success: true,
+    });
+  } catch (error) {
+    return res
+      .status(500)
+      .json({ success: false, msg: "Error signing up", error });
+  }
+});
+
+router.post("/login", async (req: TypedRequest<{}, SignInPayload>, res) => {
+  const { password, keydown, keyup, email, userType } = req.body;
+
+  const auth = await Auth.findOne({ email });
+
+  if (!auth) {
+    return res
+      .status(403)
+      .json({ success: false, msg: "User does not exist in DB" });
+  }
+
+  const processedAttempt: KeystrokeDataType = processKeystrokeData({
+    keydown,
+    keyup,
+  });
+
+  const isMatch = await bcrypt.compare(password, auth.password);
+
+  if (!isMatch) {
+    return res.status(403).json({ success: false, msg: "Invalid Credentials" });
+  }
+
+  const result = processAttempt({
+    userKeystrokeData: auth.keystrokeData,
+    attemptKeystrokeData: processedAttempt,
+    controls: auth.controls || DEFAULT_CONTROLS,
+  });
+
+  if (result.accepted) {
+    const newUserData = addAttemptToKeystrokeData({
+      userData: auth,
+      attemptKeystrokeData: processedAttempt,
+    });
+
+    await Auth.updateOne({ email }, { $set: newUserData });
+
+    let user;
+
+    if (userType === USER_TYPE.CANDIDATE) {
+      user = await Candidates.findById(auth.userId);
+    } else {
+      user = await Organizations.findById(auth.userId);
+    }
+
+    const token = await jwt.sign({ userId: auth.userId }, JWT_SECRET, {
+      expiresIn: "5h",
+    });
+
+    return res.json({
+      success: true,
+      user,
+      token,
+      keystrokeResult: {
+        result,
+        db: {
+          hold: auth.keystrokeData.hold.means,
+          flight: auth.keystrokeData.flight.means,
+          dd: auth.keystrokeData.dd.means,
+          full: auth.keystrokeData.full.means,
+        },
+        filteredDb: {
+          hold: auth.keystrokeData.hold.filteredMeans,
+          flight: auth.keystrokeData.flight.filteredMeans,
+          dd: auth.keystrokeData.dd.filteredMeans,
+          full: auth.keystrokeData.full.filteredMeans,
+        },
+        attempt: {
+          hold: processedAttempt.hold.times,
+          flight: processedAttempt.flight.times,
+          dd: processedAttempt.dd.times,
+          full: processedAttempt.full.times,
+        },
+        controls: auth.controls || DEFAULT_CONTROLS,
+      },
+    });
+  } else {
+    return res.status(403).json({ success: false, msg: "Invalid Credentials" });
+  }
+});
+
+router.get("/tendencies/:userId", authMiddleware, async (req, res) => {
+  findUser(req.params.userId)
+    .then((_auth) => {
+      if (_auth) {
+        return res.json({
+          db: _auth.keystrokeData,
+          calc: computeDataTendencies(_auth.keystrokeData),
+        });
+      } else {
+        throw new Error("User not found");
+      }
+    })
+    .catch((error) => res.status(500).send(error));
+});
+
+router.post(
+  "/update",
+  authMiddleware,
+  async (req: TypedRequest<{ userId: string }, ControlsType>, res) => {
+    try {
+      await Auth.updateOne(
+        { userId: req.query.userId },
+        { $set: { controls: req.body } }
+      );
+      return res.send("SUCCESS");
+    } catch (error) {
+      return res.status(400).send("FAILED");
+    }
+  }
+);
+
+router.post(
+  "/change-pwd",
+  authMiddleware,
+  async (req: TypedRequest<{ userId: string }, UpdatePasswordPayload>, res) => {
+    const { passwords, keydown, keyup, oldPassword } = req.body;
+    const attemptCount = passwords.length;
+
+    const auth = await Auth.findOne({ userId: req.query.userId });
+
+    const isMatch = await bcrypt.compare(oldPassword, auth.password);
+
+    if (!isMatch) {
+      return res
+        .status(403)
+        .json({ success: false, msg: "Invalid Credentials" });
+    }
+
+    const processedAttempts: KeystrokeDataType[] = Array(attemptCount)
+      .fill(0)
+      .map((v, i) =>
+        processKeystrokeData({ keydown: keydown[i], keyup: keyup[i] })
+      );
+
+    const passwordsEqual = processedAttempts.every((v) =>
+      _.isEqual(v.full.keys, processedAttempts[0].full.keys)
+    );
+
+    if (!passwordsEqual) {
+      return res
+        .status(403)
+        .json({ success: false, msg: "Passwords don't match" });
+    }
+
+    try {
+      const newPasswordData = await updatePasswordFromProcessedData(
+        passwords,
+        processedAttempts
+      );
+
+      Auth.updateOne({ userId: req.query.userId }, { $set: newPasswordData });
+
+      return res.json({
+        success: true,
+      });
+    } catch (error) {
+      return res
+        .status(500)
+        .json({ success: false, msg: "Error signing up", error });
+    }
+  }
+);
+
+module.exports = router;

NodeServer/src/routes/jobs.ts

+import { Router } from "express";
+import {
+  ApplicationType,
+  JobType,
+  TypedRequest,
+  USER_TYPE,
+} from "../config/types";
+import {
+  authMiddleware,
+  candidateMiddleware,
+  organizationMiddleware,
+} from "../middlewares/auth";
+import Application from "../models/Application";
+import Auth from "../models/Auth";
+import Jobs from "../models/Job";
+import ResumeAPI from "../utilities/apis/resume";
+
+const router = Router();
+
+router.get(
+  "/",
+  authMiddleware,
+  async (req: TypedRequest<{ userId: string }, null>, res) => {
+    try {
+      const user = await Auth.findOne({ userId: req.query.userId });
+
+      let jobs;
+
+      if (user.userType === USER_TYPE.CANDIDATE) {
+        jobs = await Jobs.find()
+          .populate({
+            path: "applications",
+            select: ["candidate", "status"],
+          })
+          .populate({ path: "organization" });
+      } else {
+        jobs = await Jobs.find({ organization: req.query.userId }).populate({
+          path: "applications",
+          populate: {
+            path: "candidate",
+            select: [
+              "name",
+              "contacts",
+              "dateOfBirth",
+              "profilePicture",
+              "resume",
+              "resumeData",
+            ],
+          },
+        });
+      }
+
+      return res.json({ jobs, success: true });
+    } catch (error) {
+      return res.json({ error, success: false });
+    }
+  }
+);
+
+router.post(
+  "/",
+  authMiddleware,
+  organizationMiddleware,
+  async (req: TypedRequest<{ userId: string }, JobType>, res) => {
+    try {
+      const newJob = new Jobs({ ...req.body, organization: req.query.userId });
+      const job = await newJob.save();
+      return res.json({ success: true, job });
+    } catch (error) {
+      return res.json({ success: false, error });
+    }
+  }
+);
+
+router.put(
+  "/",
+  authMiddleware,
+  organizationMiddleware,
+  async (req: TypedRequest<{ userId: string }, JobType>, res) => {
+    try {
+      const job = await Jobs.findByIdAndUpdate(req.body._id, {
+        $set: req.body,
+      });
+      return res.json({ success: true, job });
+    } catch (error) {
+      return res.json({ success: false, error });
+    }
+  }
+);
+
+router.delete(
+  "/",
+  authMiddleware,
+  organizationMiddleware,
+  async (req: TypedRequest<{ userId: string }, { jobId: string }>, res) => {
+    try {
+      await Jobs.deleteOne({
+        organization: req.query.userId,
+        _id: req.body.jobId,
+      });
+      return res.json({ success: true });
+    } catch (error) {
+      return res.json({ success: false });
+    }
+  }
+);
+
+router.put(
+  "/apply",
+  authMiddleware,
+  candidateMiddleware,
+  async (
+    req: TypedRequest<
+      { userId: string },
+      { application: ApplicationType; resumeUrl: string }
+    >,
+    res
+  ) => {
+    try {
+      const { application, resumeUrl } = req.body;
+      const job = await Jobs.findById(application.job);
+
+      const data: any = await ResumeAPI.getResumeScores({
+        user_id: req.query.userId,
+        resume_url: resumeUrl,
+        primary_skills: job.primarySkills,
+        secondary_skills: job.secondarySkills,
+        job_desc: job.description,
+      });
+
+      const score: ApplicationType["score"] = {
+        primary: data.primary_score,
+        primatyMatch: data.primary_match,
+        secondary: data.secondary_score,
+        secondaryMatch: data.secondary_match,
+        similarity: data.similarity,
+        total: data.primary_score + data.secondary_score + data.similarity,
+      };
+
+      const newApplication = new Application({ ...application, score });
+      const _application = await newApplication.save();
+      job.applications.push(_application.id);
+      await job.save();
+      return res.json({
+        success: true,
+        applicationId: _application.id,
+      });
+    } catch (error) {
+      return res.json({ success: false, error });
+    }
+  }
+);
+
+module.exports = router;

NodeServer/src/routes/user.ts

+import { Router } from "express";
+import { CandidateType, TypedRequest } from "../config/types";
+import { authMiddleware } from "../middlewares/auth";
+import Candidates from "../models/Candidate";
+import ResumeAPI from "../utilities/apis/resume";
+
+const router = Router();
+
+router.post(
+  "/candidate",
+  authMiddleware,
+  async (req: TypedRequest<{ userId: string }, CandidateType>, res) => {
+    try {
+      const update = req.body;
+      if (req.body?.resume) {
+        const data: any = await ResumeAPI.extractResumeData({
+          user_id: req.query.userId,
+          resume_url: req.body.resume,
+        });
+        update.resumeData = data;
+      }
+      await Candidates.findByIdAndUpdate(req.query.userId, { $set: req.body });
+      return res.status(200).json({ data: req.body });
+    } catch (error) {
+      return res.status(400).send(error);
+    }
+  }
+);
+
+module.exports = router;

NodeServer/src/utilities/apis/resume.ts

+import { ResumeDataType } from "../../config/types";
+import { request } from "../requests";
+
+export default class ResumeAPI {
+  static extractResumeData = (payload: {
+    resume_url: string;
+    user_id: string;
+  }) => request("<BASE_URL>/resume/extract", "POST", payload);
+
+  static getResumeScores = (payload: {
+    resume_url: string;
+    user_id: string;
+    primary_skills: string[];
+    secondary_skills: string[];
+    job_desc: string;
+  }) => request("<BASE_URL>/resume/get-scores", "POST", payload);
+}

utilities/userUtility.js → NodeServer/src/utilities/keystroke.ts

-const ss = require("simple-statistics");
-const _ = require("lodash");
-const { logger } = require("./loggers");
+import * as _ from "lodash";
+import * as bcrypt from "bcryptjs";
+import {
+  AuthType,
+  ControlsType,
+  KeyDetails,
+  KeystrokeDataType,
+  KeystrokeType,
+  USER_TYPE,
+} from "../config/types";
+import Auth from "../models/Auth";
 
-const User = require("../models/User");
+const ss = require("simple-statistics");
 
 const SD_MULTIPLIER = 2.5;
 const THRESHOLD_PERCENT = 65;
@@ -10,16 +18,23 @@ const FULL_STANDARD_THRESHOLD = 1;
 const STANDARD_NORM_MULTIPLIER = 1000;
 const TYPES = ["hold", "flight", "dd", "full"];
 
-const norm = (a) => Math.sqrt(ss.sum(a.map((v) => v ** 2)));
+const norm = (a: number[]) => Math.sqrt(ss.sum(a.map((v) => v ** 2)));
 
-const euclidean = (d1, d2, w = 1) => Math.sqrt(w * Math.abs(d2 - d1) ** 2);
+const euclidean = (d1: number, d2: number, w = 1) =>
+  Math.sqrt(w * Math.abs(d2 - d1) ** 2);
 
-const cityblock = (d1, d2) => Math.abs(d1 - d2);
+const cityblock = (d1: number, d2: number) => Math.abs(d1 - d2);
 
-const cityblockArray = (a1, a2) =>
+const cityblockArray = (a1: number[], a2: number[]) =>
   ss.sum(a1.map((v, i) => Math.abs(a1[i] - a2[i])));
 
-const covariance = (x, y, xMean, yMean, n) =>
+const covariance = (
+  x: number[],
+  y: number[],
+  xMean: number,
+  yMean: number,
+  n: number
+) =>
   (1 / (n - 1)) *
   ss.sum(
     Array(n)
@@ -27,8 +42,14 @@ const covariance = (x, y, xMean, yMean, n) =>
       .map((v, i) => (x[i] - xMean) * (y[i] - yMean))
   );
 
-const processKeystrokeData = ({ keydown, keyup }) => {
-  const data = {
+export const processKeystrokeData = ({
+  keydown,
+  keyup,
+}: {
+  keydown: KeyDetails[];
+  keyup: KeyDetails[];
+}): KeystrokeDataType => {
+  const data: KeystrokeDataType = {
     hold: {
       keys: [],
       times: [],
@@ -52,11 +73,6 @@ const processKeystrokeData = ({ keydown, keyup }) => {
     const { code: upCode, key: upKey, time: upTime } = keyup[i];
     const holdTime = upTime - downTime;
 
-    if (downKey !== upKey || downCode !== upCode) {
-      logger.error(`Found a mismatch ${downKey} & ${upKey}`);
-      logger.error(`Keydown: ${keydown}\nKeyup: ${keyup}`);
-    }
-
     data.full.keys.push(`H:${downKey}`);
     data.full.times.push(holdTime);
 
@@ -86,28 +102,33 @@ const processKeystrokeData = ({ keydown, keyup }) => {
   return data;
 };
 
-const computeStandardTendencies = (data) => {
+const computeStandardTendencies = (data: KeystrokeType) => {
   const transposedData = _.unzip(data.times);
 
-  const means = data.means.map((v, i) => ss.mean(transposedData[i]));
-  const sd = data.sd.map((v, i) => ss.standardDeviation(transposedData[i]));
+  const means = data?.means?.map((v, i) => ss.mean(transposedData[i]));
+  const sd = data?.sd?.map((v, i) => ss.standardDeviation(transposedData[i]));
 
   return { means, sd };
 };
 
-const computeFilteredTendencies = (data, SDFilterMultiplier = 2) => {
+const computeFilteredTendencies = (
+  data: KeystrokeType,
+  SDFilterMultiplier = 2
+) => {
   const transposedData = _.unzip(data.times).map((row) =>
     row.filter(
       // Each Row is now the columns of original matrix
       // Each element (attempt for a given keystroke) is checked and filtered
-      (v, i) => euclidean(v, data.means[i]) < SDFilterMultiplier * data.sd[i]
+      (v: any, i) =>
+        euclidean(v, data?.means?.[i]) <
+        SDFilterMultiplier * (data?.sd?.[i] || 1)
     )
   );
 
-  const filteredMeans = data.filteredMeans.map((v, i) =>
+  const filteredMeans = data?.filteredMeans?.map((v, i) =>
     ss.mean(transposedData[i].length === 0 ? [0] : transposedData[i])
   );
-  const filteredSd = data.filteredSd.map((v, i) =>
+  const filteredSd = data?.filteredSd?.map((v, i) =>
     ss.standardDeviation(
       transposedData[i].length === 0 ? [0] : transposedData[i]
     )
@@ -116,11 +137,12 @@ const computeFilteredTendencies = (data, SDFilterMultiplier = 2) => {
   return { filteredMeans, filteredSd };
 };
 
-const computeMahalanobis = (data, means) => {
-  const transTime = _.unzip(data.times);
+const computeMahalanobis = (data: KeystrokeType, means: number[]) => {
+  const times = data.times as number[][];
+  const transTime = _.unzip(times);
 
   const obsCount = data.times.length;
-  const featureCount = data.times[0].length;
+  const featureCount = times[0]?.length;
 
   const covMatrix = new Array(featureCount)
     .fill(0)
@@ -148,63 +170,83 @@ const computeMahalanobis = (data, means) => {
   return { covMatrix };
 };
 
-const computeDataTendencies = (keystrokeData) => {
+export const computeDataTendencies = (keystrokeData: KeystrokeDataType) => {
   TYPES.map((type) => {
-    const { means, sd } = computeStandardTendencies(keystrokeData[type]);
-    keystrokeData[type].means = means;
-    keystrokeData[type].sd = sd;
+    const key = type as keyof KeystrokeDataType;
+    const { means, sd } = computeStandardTendencies(keystrokeData[key]);
+    keystrokeData[key].means = means;
+    keystrokeData[key].sd = sd;
 
     const { filteredMeans, filteredSd } = computeFilteredTendencies(
-      keystrokeData[type]
+      keystrokeData[key]
     );
-    keystrokeData[type].filteredMeans = filteredMeans;
-    keystrokeData[type].filteredSd = filteredSd;
+    keystrokeData[key].filteredMeans = filteredMeans;
+    keystrokeData[key].filteredSd = filteredSd;
 
-    const { covMatrix } = computeMahalanobis(keystrokeData[type], means);
-    keystrokeData[type].covMatrix = covMatrix;
+    const { covMatrix } = computeMahalanobis(keystrokeData[key], means || []);
+    keystrokeData[key].covMatrix = covMatrix;
 
-    return type;
+    return key;
   });
 
   return keystrokeData;
 };
 
-const createSignupDataFromProcessedData = (
-  username,
-  passwords,
-  processedData
+export const createSignupDataFromProcessedData = async (
+  email: string,
+  passwords: string[],
+  userType: USER_TYPE,
+  userId: string,
+  processedData: KeystrokeDataType[] | any
 ) => {
-  let signupData = {
-    username,
-    password: passwords[0],
+  const keystroke = {
+    keys: [],
+    times: [],
+    sums: [],
+    means: [],
+    sd: [],
+    filteredMeans: [],
+    filteredSd: [],
+    covMatrix: [],
+  };
+
+  const salt = await bcrypt.genSalt(10);
+
+  const hashedPassword = await bcrypt.hash(passwords[0], salt);
+
+  let signupData: AuthType = {
+    email,
+    userType,
+    userId,
+    password: hashedPassword,
     keystrokeDataTimestamps: [],
     keystrokeData: {
-      hold: {},
-      flight: {},
-      dd: {},
-      full: {},
+      hold: keystroke,
+      flight: keystroke,
+      dd: keystroke,
+      full: keystroke,
     },
   };
 
   signupData = processedData.reduce((acc, v, i) => {
     TYPES.map((type) => {
+      const key = type as keyof KeystrokeDataType;
       if (i === 0) {
         // Keys of the processed data is checked for equality
         // So the first one is used for the dataset
-        acc.keystrokeData[type].keys = v[type].keys;
+        acc.keystrokeData[key].keys = v[key].keys;
+        const length = v[key].keys.length;
 
         // Length of the processedData array is the number of attempts
         // Times is an array of arrays of each attempt
-        acc.keystrokeData[type].times = Array(v.length).fill([]);
-
-        acc.keystrokeData[type].means = Array(v[type].keys.length).fill(0);
-        acc.keystrokeData[type].sd = Array(v[type].keys.length).fill(0);
-        acc.keystrokeData[type].filteredMeans = Array(v[type].keys.length).fill(
-          0
-        );
-        acc.keystrokeData[type].filteredSd = Array(v[type].keys.length).fill(0);
+        acc.keystrokeData[key].times = Array(v.length).fill([]);
+
+        acc.keystrokeData[key].means = Array(length).fill(0);
+        acc.keystrokeData[key].sd = Array(length).fill(0);
+        acc.keystrokeData[key].filteredMeans = Array(length).fill(0);
+        acc.keystrokeData[key].filteredSd = Array(length).fill(0);
       }
-      acc.keystrokeData[type].times[i] = v[type].times;
+      acc.keystrokeData[key].times[i] = v[key].times;
 
       return type;
     });
@@ -219,67 +261,61 @@ const createSignupDataFromProcessedData = (
   return signupData;
 };
 
-const findUser = (username) => User.findOne({ username }).exec();
-
-const signUpNewUser = ({
-  username,
-  password,
-  keystrokeData,
-  keystrokeDataTimestamps,
-}) =>
-  User.create({
-    username,
-    password,
-    keystrokeData,
-    keystrokeDataTimestamps,
-  });
-
-const updateUser = ({ username, updateData }) =>
-  User.updateOne({ username }, updateData).exec();
+export const findUser = (userId: string) => Auth.findOne({ userId }).exec();
 
-const addAttemptToKeystrokeData = ({ userData, attemptKeystrokeData }) => {
+export const addAttemptToKeystrokeData = ({
+  userData,
+  attemptKeystrokeData,
+}: {
+  userData: AuthType;
+  attemptKeystrokeData: KeystrokeDataType;
+}) => {
   TYPES.map((type) => {
-    userData.keystrokeData[type].times.push(attemptKeystrokeData[type].times);
+    const key = type as keyof KeystrokeDataType;
+    userData.keystrokeData[key].times.concat(attemptKeystrokeData[key].times);
     return type;
   });
   userData.keystrokeDataTimestamps.push(Date.now());
 
   userData.keystrokeData = computeDataTendencies(userData.keystrokeData);
 
-  // console.log(userData);
-
   return userData;
 };
 
-const processStandard = (userKeystrokeData, attemptKeystrokeData, controls) => {
+const processStandard = (
+  userKeystrokeData: KeystrokeDataType,
+  attemptKeystrokeData: KeystrokeDataType,
+  controls: ControlsType["standard"]
+) => {
   const { threshold = THRESHOLD_PERCENT, sd: sdMult = SD_MULTIPLIER } =
     controls;
 
-  const scores = {
+  const scores: any = {
     distance: {},
     inRange: {},
     inRangePercent: {},
   };
 
   TYPES.map((type) => {
-    scores.distance[type] = Array(userKeystrokeData[type].means.length).fill(0);
-    const isInRange = Array(userKeystrokeData[type].means.length).fill(false);
-    let inRangeCount = 0;
+    const key = type as keyof KeystrokeDataType;
+    scores.distance[key] = Array(userKeystrokeData[key].means?.length).fill(0);
+    const isInRange = Array(userKeystrokeData[key].means?.length).fill(false);
+    let inRangeCount: any = 0;
     let totalCount = 0;
-    scores.inRangePercent[type] = 0;
-    scores.inRange[type] = false;
+    scores.inRangePercent[key] = 0;
+    scores.inRange[key] = false;
 
-    userKeystrokeData[type].means.map((v, i) => {
-      const mean = userKeystrokeData[type].means[i];
-      const sd = userKeystrokeData[type].sd[i];
-      const attemptTime = attemptKeystrokeData[type].times[i];
+    userKeystrokeData[key].means?.map((v, i) => {
+      const mean = userKeystrokeData[key].means?.[i] || 0;
+      const sd = userKeystrokeData[key].sd?.[i] || 0;
+      const attemptTime = attemptKeystrokeData[key].times[i];
 
       const low = mean - sd * sdMult;
       const high = mean + sd * sdMult;
 
       const distance = cityblock(mean, attemptTime);
 
-      scores.distance[type][i] = distance;
+      scores.distance[key][i] = distance;
       isInRange[i] = attemptTime >= low && attemptTime <= high;
 
       inRangeCount += !!isInRange[i];
@@ -287,88 +323,158 @@ const processStandard = (userKeystrokeData, attemptKeystrokeData, controls) => {
       return v;
     });
 
-    scores.inRangePercent[type] = (inRangeCount / totalCount) * 100;
-    if (scores.inRangePercent[type] >= threshold) {
-      scores.inRange[type] = true;
+    scores.inRangePercent[key] = (inRangeCount / totalCount) * 100;
+    if (scores.inRangePercent[key] >= threshold) {
+      scores.inRange[key] = true;
     }
 
-    return type;
+    return key;
   });
 
   return scores;
 };
 
 const processFullStandard = (
-  userKeystrokeData,
-  attemptKeystrokeData,
-  controls
+  userKeystrokeData: KeystrokeDataType,
+  attemptKeystrokeData: KeystrokeDataType,
+  controls: ControlsType["fullStandard"]
 ) => {
   const { threshold = FULL_STANDARD_THRESHOLD } = controls;
 
-  const scores = {
+  const scores: any = {
     normedDistance: {},
     distance: {},
     inRange: {},
   };
 
   TYPES.map((type) => {
-    scores.distance[type] = 0;
-    scores.inRange[type] = false;
+    const key = type as keyof KeystrokeDataType;
+    scores.distance[key] = 0;
+    scores.inRange[key] = false;
 
     const distance = cityblockArray(
-      attemptKeystrokeData[type].times,
-      userKeystrokeData[type].means
+      attemptKeystrokeData[key].times,
+      userKeystrokeData[key].means || []
     );
-    scores.distance[type] = distance;
+    scores.distance[key] = distance;
 
     const normedDistance =
       (distance * STANDARD_NORM_MULTIPLIER) /
-      (norm(attemptKeystrokeData[type].times) *
-        norm(userKeystrokeData[type].means));
+      (norm(attemptKeystrokeData[key].times) *
+        norm(userKeystrokeData[key].means || []));
 
-    scores.normedDistance[type] = normedDistance;
+    scores.normedDistance[key] = normedDistance;
     if (normedDistance <= threshold) {
-      scores.inRange[type] = true;
+      scores.inRange[key] = true;
     }
 
-    return type;
+    return key;
   });
 
   return scores;
 };
 
-const processAttempt = ({
-  userKeystrokeData: ukd,
-  attemptKeystrokeData: akd,
+export const processAttempt = ({
+  userKeystrokeData,
+  attemptKeystrokeData,
   controls,
+}: {
+  userKeystrokeData: KeystrokeDataType;
+  attemptKeystrokeData: KeystrokeDataType;
+  controls: ControlsType;
 }) => {
-  const { standard: stControls, fullStandard: stPopControls } = controls;
+  const { standard, fullStandard } = controls;
 
   const result = {
     standard: {
-      ...stControls,
-      ...processStandard(ukd, akd, stControls),
+      ...standard,
+      ...processStandard(userKeystrokeData, attemptKeystrokeData, standard),
     },
     fullStandard: {
-      ...stPopControls,
-      ...processFullStandard(ukd, akd, stPopControls),
+      ...fullStandard,
+      ...processFullStandard(
+        userKeystrokeData,
+        attemptKeystrokeData,
+        fullStandard
+      ),
     },
     accepted: false,
   };
 
-  result.accepted =
-    result.standard.inRange.full || result.fullStandard.inRange.full;
+  const standardCheck = controls.standard.use
+    ? result.standard.inRange.full
+    : true;
+
+  const fullStandardCheck = controls.fullStandard.use
+    ? result.fullStandard.inRange.full
+    : true;
+
+  result.accepted = standardCheck || fullStandardCheck;
 
   return result;
 };
 
-module.exports = {
-  processKeystrokeData,
-  createSignupDataFromProcessedData,
-  findUser,
-  signUpNewUser,
-  updateUser,
-  addAttemptToKeystrokeData,
-  computeDataTendencies,
-  processAttempt,
+export const updatePasswordFromProcessedData = async (
+  passwords: string[],
+  processedData: KeystrokeDataType[] | any
+) => {
+  const keystroke = {
+    keys: [],
+    times: [],
+    sums: [],
+    means: [],
+    sd: [],
+    filteredMeans: [],
+    filteredSd: [],
+    covMatrix: [],
+  };
+
+  const salt = await bcrypt.genSalt(10);
+
+  const hashedPassword = await bcrypt.hash(passwords[0], salt);
+
+  let newPasswordData: Partial<AuthType> = {
+    password: hashedPassword,
+    keystrokeDataTimestamps: [],
+    keystrokeData: {
+      hold: keystroke,
+      flight: keystroke,
+      dd: keystroke,
+      full: keystroke,
+    },
+  };
+
+  newPasswordData = processedData.reduce((acc, v, i) => {
+    TYPES.map((type) => {
+      const key = type as keyof KeystrokeDataType;
+      if (i === 0) {
+        // Keys of the processed data is checked for equality
+        // So the first one is used for the dataset
+        acc.keystrokeData[key].keys = v[key].keys;
+        const length = v[key].keys.length;
+
+        // Length of the processedData array is the number of attempts
+        // Times is an array of arrays of each attempt
+        acc.keystrokeData[key].times = Array(v.length).fill([]);
+
+        acc.keystrokeData[key].means = Array(length).fill(0);
+        acc.keystrokeData[key].sd = Array(length).fill(0);
+        acc.keystrokeData[key].filteredMeans = Array(length).fill(0);
+        acc.keystrokeData[key].filteredSd = Array(length).fill(0);
+      }
+      acc.keystrokeData[key].times[i] = v[key].times;
+
+      return type;
+    });
+
+    acc.keystrokeDataTimestamps.push(Date.now());
+
+    return acc;
+  }, newPasswordData);
+
+  newPasswordData.keystrokeData = computeDataTendencies(
+    newPasswordData.keystrokeData
+  );
+
+  return newPasswordData;
 };

NodeServer/src/utilities/requests.ts

+import axios, { AxiosError, AxiosResponse, AxiosRequestConfig } from "axios";
+
+import { UTILITY_SERVER } from "../config/contants";
+
+axios.interceptors.response.use(
+  (response) => response,
+  (error: AxiosError) => {
+    return Promise.reject(error);
+  }
+);
+
+export const request = (
+  url: AxiosRequestConfig["url"],
+  method: AxiosRequestConfig["method"],
+  requestData?: AxiosRequestConfig["data"] | AxiosRequestConfig["params"],
+  contentType?: string
+) =>
+  new Promise(async (resolve, reject) => {
+    const endpoint = url?.replace?.("<BASE_URL>", UTILITY_SERVER);
+
+    const params = method === "GET" ? requestData : null;
+    const data = method === "GET" ? null : requestData;
+
+    const headers = {
+      "Content-Type": contentType || "application/json",
+    };
+
+    axios({
+      url: endpoint,
+      method,
+      data,
+      params,
+      headers,
+      timeout: 30000,
+    })
+      .then(async (response: AxiosResponse) => {
+        resolve(response.data);
+      })
+      .catch(async (error: AxiosError) => {
+        if (error?.response) {
+          return reject(error?.response?.data);
+        }
+        reject(error);
+      });
+  });

NodeServer/tsconfig.json

+{
+  "compilerOptions": {
+    "target": "es5",
+    "module": "commonjs",
+    "lib": ["es6"],
+    "allowJs": true,
+    "outDir": "lib",
+    "rootDir": "src",
+    "strict": false,
+    "esModuleInterop": true,
+    "resolveJsonModule": true
+  },
+  "compileOnSave": true,
+  "include": ["src"]
+}

NodeServer/yarn-error.log

+Arguments: 
+  C:\Program Files\nodejs\node.exe C:\Program Files (x86)\Yarn\bin\yarn.js add -D @types/jsonwebtoke
+
+PATH: 
+  C:\Program Files (x86)\Common Files\Intel\Shared Libraries\redist\intel64\compiler;C:\Program Files\Common Files\Oracle\Java\javapath;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0\;C:\Windows\System32\OpenSSH\;C:\Program Files (x86)\Yarn\bin\;C:\xampp\php;C:\composer;C:\Program Files\Git\cmd;C:\Program Files\Java\jdk-11.0.9;C:\Users\User\AppData\Local\Android\Sdk\platform-tools;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0\;C:\WINDOWS\System32\OpenSSH\;C:\Program Files\CMake\bin;C:\Program Files\Java\jdk-11.0.9\bin;C:\Users\User\AppData\Local\Programs\Python\Python39\Scripts;C:\Users\User\AppData\Local\Programs\Python\Python39\;C:\Program Files\nodejs\;C:\mongosh\bin;;C:\Program Files (x86)\NVIDIA Corporation\PhysX\Common;C:\Program Files\NVIDIA Corporation\NVIDIA NvDLISR;C:\Users\User\AppData\Local\Programs\Python\Python39\Scripts\;C:\Users\User\AppData\Local\Programs\Python\Python39\;C:\Users\User\AppData\Local\Microsoft\WindowsApps;C:\Users\User\AppData\Local\Programs\Microsoft VS Code\bin;C:\Users\User\AppData\Local\Yarn\bin;C:\Program Files\heroku\bin;C:\Users\User\AppData\Local\Android\Sdk\platform-tools;C:\Users\User\AppData\Local\Android\Sdk\tools;C:\Program Files\Java\jdk-11.0.9;C:\Users\User\.deta\bin;C:\Users\User\AppData\Roaming\npm;;C:\Users\User\AppData\Local\Microsoft\WindowsApps
+
+Yarn version: 
+  1.22.5
+
+Node version: 
+  16.13.1
+
+Platform: 
+  win32 x64
+
+Trace: 
+  Error: https://registry.yarnpkg.com/@types%2fjsonwebtoke: Not found
+      at Request.params.callback [as _callback] (C:\Program Files (x86)\Yarn\lib\cli.js:66097:18)
+      at Request.self.callback (C:\Program Files (x86)\Yarn\lib\cli.js:140749:22)
+      at Request.emit (node:events:390:28)
+      at Request.<anonymous> (C:\Program Files (x86)\Yarn\lib\cli.js:141721:10)
+      at Request.emit (node:events:390:28)
+      at IncomingMessage.<anonymous> (C:\Program Files (x86)\Yarn\lib\cli.js:141643:12)
+      at Object.onceWrapper (node:events:509:28)
+      at IncomingMessage.emit (node:events:402:35)
+      at endReadableNT (node:internal/streams/readable:1343:12)
+      at processTicksAndRejections (node:internal/process/task_queues:83:21)
+
+npm manifest: 
+  {
+    "name": "keystrokedynamics",
+    "version": "1.0.0",
+    "description": "",
+    "main": "server.js",
+    "scripts": {
+      "test": "echo \"Error: no test specified\" && exit 1",
+      "start": "node server.js",
+      "dev": "nodemon server.js",
+      "burnthemall": "rm -rf node_modules package-lock.json && npm i"
+    },
+    "author": "Namit Nathwani",
+    "license": "ISC",
+    "dependencies": {
+      "express": "^4.18.2",
+      "express-winston": "^4.0.3",
+      "jsonwebtoken": "^9.0.0",
+      "lodash": "^4.17.19",
+      "mathjs": "^7.2.0",
+      "mongoose": "^5.9.25",
+      "simple-statistics": "^7.1.0",
+      "winston": "^3.3.3"
+    },
+    "devDependencies": {
+      "@types/express": "^4.17.17",
+      "@types/lodash": "^4.14.191",
+      "eslint": "^7.5.0",
+      "eslint-config-airbnb": "^18.2.0",
+      "eslint-config-airbnb-base": "^14.2.0",
+      "eslint-plugin-import": "^2.22.0",
+      "nodemon": "^2.0.4",
+      "typescript": "^4.9.5"
+    }
+  }
+
+yarn manifest: 
+  No manifest
+
+Lockfile: 
+  # THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
+  # yarn lockfile v1
+  
+  
+  "@babel/code-frame@7.12.11":
+    version "7.12.11"
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+  
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+      enabled "2.0.x"
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+  
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+  
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PythonServer/.gitignore

+__pycache__
+**/__pycache__
\ No newline at end of file

PythonServer/Candidates_score.csv

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+resumes/Dhaval_Thakkar_Resume.pdf,Dhaval Thakkar,9191729595,thakkar.dhaval.haresh@gmail.com,['UNIFYND TECHNOLOGIES PVT. LTD'],None,"['UNIFYND TECHNOLOGIES PVT. LTD. | Data Scientist', 'Mumbai, MH, India | June 2018 – Present', '• Led the development of a Templatized OCR Engine with GUI to onboard 2000+ retailers from different malls. The', 'microservice deployed is currently operating at an accuracy of 81%', '• Built a Customer Segmentation model to target customers with relevant coupons, rewards, and content resulting', 'in a 3x increase in revenue and 2x increase in coupon utilization', '• Built a Dynamic Coupon Pricing Engine for malls that led to a 5x increase in coupon consumption on the coupon', 'marketplace', '• Built a Pricing Engine and Customer Segmentation Model for a logistics company which saw a 32% reduction in', 'Customer Attrition and a 12% increase in Repeat Purchase Rate', '• Developed an Automated End to End Reporting system to track KPIs performance for 10 malls that saves 60', 'hours of manual labour each month', 'UNIFYND TECHNOLOGIES PVT. LTD. | Intern Data Scientist Mumbai, MH, India | Sept 2017 - June 2018', '• Built a Smart Cryptocurrency trading platform which used social data and historical prices to optimize current', 'portfolio. Boosted overall profit from the portfolio by 30%', '• Worked with Product and Marketing teams to identify the power users of an app which resulted in 43% increase in', 'activity and a 65% increase in revenue from these users', 'ZIFF, INC | Deep Learning Intern', 'Provo, UT, USA | May 2017 – Aug 2017', '• Demonstrated competency in Hyperparameter Optimization, Image Augmentation and Learning Rate decay', 'strategies using the Keras Library', '• Deployed a Multi-Class Image classifier microservice written on Flask as a container on AWS EC2 using Docker']","['Html', 'Data analytics', 'Marketing', 'Segmentation', 'Content', 'Algorithms', 'Numpy', 'Pandas', 'Github', 'R', 'Logistics', 'Css', 'Operating systems', 'Testing', 'Flask', 'Mysql', 'Scrapy', 'Machine learning', 'Security', 'Keras', 'Python', 'Kpis', 'System', 'Docker', 'Reporting', 'Analytics', 'Aws', 'Engineering', 'Anaconda', 'Networking', 'Sql']",5.75,['Bachelor of Engineering'],350,44,"['ocr', 'aws', 'python', 'gcp']",42,"['data', 'ocr', 'science']",1,32,50.0,168.0
+resumes/python-developer-resume-2.pdf,Python Developer,456-7890,ggonzalez@email.com,None,None,"['Python Developer Intern', 'Knewton', 'April 2016 - April 2017', '· Worked alongside another developer to implement RESTful APIs', 'Chicago, IL', 'in Django that enabled internal analytics team to increase', 'reporting speed by 24%', '· Using Selenium, built out a unit testing infrastructure for a client', 'web application that reduced the number of bugs reported by', 'the client by 11% month over month']","['Django', 'Math', 'Oracle', 'Requests', 'Github', 'Api', 'Database', 'Css', 'Design', 'Postgresql', 'Testing', 'Agile', 'Apis', 'Selenium', 'Rest', 'Python', 'Writing', 'System', 'Updates', 'Javascript', 'Reporting', 'Analytics', 'Aws', 'Sql', 'Process']",1.0,"['B.S.', 'M.S.']",223,22,"['python', 'aws']",28,"['science', 'data']",1,20,50.0,120.0
+resumes/software-engineer-resume-1.pdf,New York,456-7890,cmcturland@email.com,None,None,"['Software Engineer', 'Embark', 'January 2015 - current / New York, NY', 'Worked with product managers to re-architect a multi-page web', 'app into a single page web-app, boosting yearly revenue by $1.4M', 'Constructed the logic for a streamlined ad-serving platform that', 'scaled to our 35M users, which improved the page speed by 15%', 'after implementation', 'Tested software for bugs and operating speed, fixing bugs and', 'documenting processes to increase efficiency by 18%', 'Iterated platform for college admissions, collaborating with a group', 'of 4 engineers to create features across the software', 'Software Engineer', 'MarketSmart', 'April 2012 - January 2015 / Washington, DC', 'Built RESTful APIs that served data to the JavaScript front-end', 'based on dynamically chosen user inputs that handled over 500,000', 'concurrent users', 'Built internal tool using NodeJS and Pupeteer.js to automate QA and', 'monitoring of donor-facing web app, which improved CTR by 3%', 'Reviewed code and conducted testing for 3 additional features on', 'donor-facing web app that increased contributions by 12%', 'Software Engineer Intern', 'Marketing Science Company', 'April 2011 - March 2012 / Pittsburgh, PA', 'Partnered with a developer to implement RESTful APIs in Django,', 'enabling analytics team to increase reporting speed by 24%', 'Using Selenium I built out a unit testing infrastructure for a client', 'application that reduced the number of bugs reported by the client', 'by 11% month over month']","['Django', 'Marketing', 'Unix', 'Nosql', 'R', 'Css', 'Postgresql', 'Testing', 'Mysql', 'Sci', 'Apis', 'Selenium', 'Admissions', 'Python', 'Html5', 'Javascript', 'Reporting', 'Analytics', 'C', 'Sql', 'Aws']",3.67,['B.S.'],233,22,"['python', 'aws']",28,"['science', 'data']",1,10,50.0,110.0
+resumes/Santhosh_Narayanan.pdf,SANTHOSH NARAYANAN,417-6755,santhosn@usc.edu,None,None,"['on an EC2 server supported by S3 and RDS.', '\uf0a7  Maintained AWS infrastructure for institute’s annual technical festival website, by hosting the website', 'on an EC2 Ubuntu server.', 'K J Somaiya Inst. of Engg. & I.T – Penetration tester', 'December 2016 – January 2016', '\uf0a7  Conducted penetration testing for institute’s online admission and examination portal.', '\uf0a7  Performed authentication checks, access control checks, per screen checks (XSS, SQL injection.).', '\uf0a7  Delivered error free application, incorporating patches for the respective bugs using ASP.NET']","['Html', 'Jupyter', 'Access', 'Numpy', 'Php', 'Matplotlib', 'Oracle', 'Pandas', 'Computer science', 'Css', 'Purchasing', 'Schedule', 'Scheduling', 'Flask', 'Testing', 'Lan', 'Mysql', 'Scrapy', 'Security', 'Programming', 'Website', 'Keras', 'Python', 'System', 'Wordpress', 'Spyder', 'Technical', 'Ubuntu', 'Javascript', 'Java', 'Aws', 'Engineering', 'Sql', 'Certification']",,None,367,22,"['python', 'aws']",14,['science'],1,7,50.0,93.0

PythonServer/Job_description.txt

+About the job
+
+Borneo.io is building the next-generation ML Powered data privacy platform for hyper-growth companies. The Data Scientist role is at the core of Borneo's engineering. You will be building models, manipulating big data, and working with APIs essential to the Borneo product.
+We are growing fast and expanding our data science family with outstanding minds and diverse personalities.
+
+As a Data Scientist at Borneo, you'll have the opportunity to:
+
+    Work with some of the largest data sets used by some of the leading global technology companies
+    Help build a predictive product and inform features at the ground level.
+    Lead the way in leveraging unstructured data for predictive modeling, anomaly detection, and drive privacy compliance.
+
+Responsibilities:
+
+    Identify, automate data collection processes
+    Dive into complex data sets to analyze trends and identify opportunities for improvement.
+    Build predictive models and machine-learning algorithms
+    Present information using data visualization techniques
+    Propose solutions and strategies to business challenges
+    Have a data-driven decision making approach
+
+Requirements:
+
+    5-8 years of relevant experience, B2B startup experience preferred
+    Proven experience as a Data Scientist or Data Analyst
+    Experience in building ML models and deploying them to production
+    A solid understanding of data science fundamentals, statistical techniques, NLP algorithms
+    Understand research papers and create quick proof of concept relevant to the product
+    Expert in implementing quick prototypes that shows business value
+    Experience with programming languages such as NodeJs /Python/JavaScript: Cloud technologies: AWS/GCP/K8 etc.
+

PythonServer/README.md

+### Create conda python 3.9 env
+
+conda create -n server python=3.9
+
+### PIP Packages
+
+gensim==3.8.1
+texthero==1.1.0
+pyresparser
+
+### CONDA Packages
+
+tqdm
+pdf2image
+pandas
+pytesseract
+"uvicorn[standard]"
+fastapi
+
+### Run server
+
+uvicorn main:app --reload

PythonServer/baseModels/payloads.py

+from typing import List, Union
+from pydantic import BaseModel
+
+class ExtractResume(BaseModel):
+    resume_url: str
+    user_id:str
+
+class ResumeScores(BaseModel):
+    resume_url: str
+    user_id:str
+    primary_skills:List[str] = []
+    secondary_skills:List[str] = []
+    job_desc:str
\ No newline at end of file

PythonServer/main.py

+from fastapi import FastAPI
+
+import routes.resume as resumes
+
+app = FastAPI()
+app.include_router(resumes.router)
+
+@app.get("/")
+def read_root():
+    return {"status": "running"}
\ No newline at end of file

PythonServer/requirements.txt

+tqdm==4.61.2
+pdf2image==1.16.0
+pandas==1.3.0
+pytesseract==0.3.8

PythonServer/resumeSample.py

+
+import pandas as pd
+import os
+
+from scripts.processing import document_processing
+from tqdm import tqdm
+
+import warnings
+warnings.filterwarnings("ignore")
+
+skills = {
+ "primary" : ['Python', 'Machine Learning', 'node.js', 'AWS', 'Kubernetese', 'NLP', 'GCP', 'predective', 'OCR'],
+ "secondary" : ['data', 'science', 'modeling', 'anomaly', 'privacy', 'visualization', 'OCR'],
+}
+
+
+def document_score(df):
+    
+    # Page score
+    df.loc[df['no_of_pages'] == 1, ['page_score']] = 100
+    df.loc[df['no_of_pages'] == 2, ['page_score']] = 60
+    df.loc[(df['no_of_pages'] > 2) |
+           (df['no_of_pages'] == 0), ['page_score']] = 30
+    
+    # Word score
+    df.loc[(df['words'] >= 200) & (df['words'] < 400),
+           ['word_score']] = 100
+    df.loc[(df['words'] >= 400) & (df['words'] < 600),
+           ['word_score']] = 70
+    df.loc[((df['words'] > 0) & (df['words'] < 200))|
+           (df['words'] > 600) | (df['words'].isnull()),
+           ['word_score']] = 40
+    
+    df['document_score'] = (df['page_score'] + df['word_score']) * 0.25
+    
+    df.drop(['word_score', 'page_score'], axis=1, inplace=True)
+    
+    return df
+
+if __name__=='__main__':
+
+    resume_dir = 'resumes/'
+    jd_file = 'Job_description.txt'
+    list_of_resumes = os.listdir(resume_dir)  # list_of_resumes = ['Dhaval_Thakkar_Resume.pdf', 'asdasdasd.pdf']
+    
+    df = pd.DataFrame()
+    for file in tqdm(list_of_resumes):
+        result = document_processing(resume_dir+file, skills, jd_file)
+        candidate = result.skills_match()
+        df = pd.concat([df, candidate], ignore_index=True)
+    
+    df = document_score(df)
+        
+    # Final score
+    df['Score'] = df['primary_score'] + df['secondary_score'] + df['document_score'] + df['document_similarity']
+    
+    df = df.sort_values('Score', ascending=False)
+    df = df.reset_index(drop=True)
+
+    print(df)
+    
+    df.to_csv('Candidates_score.csv', index=False)
\ No newline at end of file

PythonServer/routes/resume.py

+from fastapi import APIRouter
+import os
+from pyresparser import ResumeParser
+import urllib
+from baseModels.payloads import ExtractResume, ResumeScores
+
+from scripts.processing import document_processing
+
+
+router = APIRouter(prefix='/resume')
+
+@router.post("/extract")
+def extract(payload:ExtractResume):
+    filename = 'resumes/'+payload.user_id+'.pdf'
+    urllib.request.urlretrieve(payload.resume_url, filename)
+    pyres_data = ResumeParser(filename).get_extracted_data()
+    os.remove(filename)
+    return pyres_data
+
+@router.post("/get-scores")
+def get_scores(payload:ResumeScores):
+    filename = 'resumes/'+payload.user_id+'.pdf'
+    urllib.request.urlretrieve(payload.resume_url, filename)
+    skills = {"primary":payload.primary_skills, "secondary":payload.secondary_skills}
+
+    result = document_processing(filename, skills, payload.job_desc)
+    candidate = result.skills_match()
+
+    os.remove(filename)
+    return candidate
\ No newline at end of file

PythonServer/scripts/processing.py

+import pandas as pd
+import texthero as hero
+
+from pyresparser.utils import extract_text
+
+from PIL import Image
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+# skills = {
+#  "primary" : ['Python', 'Machine Learning', 'node.js', 'AWS', 'Kubernetese', 'NLP', 'GCP', 'predective', 'OCR'],
+#  "secondary" : ['data', 'science', 'modeling', 'anomaly', 'privacy', 'visualization', 'OCR'],
+# }
+
+class document_processing:
+    
+    def __init__(self, resume, skills, job_desc):
+        self.resume = resume
+        self.skills = skills
+        self.job_desc = job_desc
+        
+    def extract_resume(self):
+        filepath = self.resume
+        
+        extension = filepath.split('.')[-1]
+        extension = '.'+extension
+
+        resume_txt = extract_text(filepath, extension=extension)
+        
+        return resume_txt
+         
+    def find_unigram(df, column):
+        
+        unigrams  = (df[column].str.lower()
+                    .str.replace(r'[^a-z\s]', '')
+                    .str.split(expand=True)
+                    .stack()).reset_index(drop=True)
+        
+        unigrams = hero.clean(unigrams)
+        un_df = pd.DataFrame(unigrams, columns = ['text'])
+        
+        return un_df
+    
+    def find_match(self, source, match):
+        
+        # Remove the null values 
+        match.dropna(inplace=True)
+        match.reset_index(drop=True)
+        match.columns = ['text']
+        match['text'] = hero.clean(match['text'])
+        
+        # Find the max val
+        max_val = len(match)  #9
+
+
+        #1. source (main_df) Python, Python, Python, Python 
+        #2. match (primary skill)       
+        # Find the skills that match with the resume
+        df = pd.merge(source, match, on = 'text') 
+        df.drop_duplicates(inplace=True) # Python
+        df.reset_index(drop=True)
+        
+        # Skills matching
+        match_skills = len(df) #5
+        
+        if match_skills == 0:
+            lst_skills = []
+            score = 0
+        elif match_skills > 0:
+            lst_skills = df['text'].tolist()
+            score = int((match_skills / max_val) * 100)
+        
+        return score, lst_skills
+    
+    def fill_data(self, source, target, column):
+   
+        source.loc[0, column] = str(target[column])
+        
+        return source   
+    
+    def resume_cosine_score(self, text):
+        
+        jd_txt = self.job_desc
+        jd_txt = pd.Series(jd_txt)
+        jd_txt = hero.clean(jd_txt)
+        jd_txt = jd_txt[0]
+
+        [[1,24],[24,1]]
+
+        text_list = [text, jd_txt]
+        cv = CountVectorizer()
+        count_matrix = cv.fit_transform(text_list)
+        match_percentage = cosine_similarity(count_matrix)[0][1] * 100
+        match_percentage = round(match_percentage, 2)
+
+        return match_percentage
+        
+    
+    def skills_match(self):
+        
+        skills = self.skills
+        
+        pyres_text = self.extract_resume()
+        self.text = pyres_text
+        
+        ocr_ser = pd.Series(pyres_text)
+        cleaned_words = hero.clean(ocr_ser) 
+        
+        main_df = pd.DataFrame(cleaned_words[0].split(), columns = ['text'])
+        self.clean_data = main_df
+        
+        pri_score, pri_match = self.find_match(main_df, pd.DataFrame(skills['primary']))
+        sec_score, sec_match = self.find_match(main_df, pd.DataFrame(skills['secondary']))
+
+        doc_sim = self.resume_cosine_score(cleaned_words[0])
+        
+        return {'primary_score': pri_score,
+                'primary_match': pri_match,
+                'secondary_score': sec_score,
+                'secondary_match': sec_match,
+                'similarity': int(doc_sim)}

routes/user.js

-const express = require("express");
-const _ = require("lodash");
-const { logger } = require("../utilities/loggers");
-const {
-  processKeystrokeData,
-  findUser,
-  signUpNewUser,
-  updateUser,
-  createSignupDataFromProcessedData,
-  addAttemptToKeystrokeData,
-  computeDataTendencies,
-  processAttempt,
-} = require("../utilities/userUtility");
-
-const router = express.Router();
-
-router.use(express.json({ extended: false }));
-router.use(express.urlencoded({ extended: false }));
-
-router.post("/validateKeystroke", (req, res) => {
-  const processedKeystrokeData = processKeystrokeData(req.body);
-  res.json(processedKeystrokeData);
-});
-
-router.get("/find/:username", async (req, res) => {
-  res.json(await findUser(req.params.username));
-});
-
-router.get("/tendencies/:username", async (req, res) => {
-  const user = await findUser(req.params.username);
-  return res.json({
-    db: (await findUser(req.params.username)).keystrokeData,
-    calc: computeDataTendencies(user.keystrokeData),
-  });
-});
-
-router.post("/signup", async (req, res) => {
-  const { passwords, keydown, keyup, username } = req.body;
-  const attemptCount = passwords.length;
-
-  const processedAttempts = Array(attemptCount)
-    .fill()
-    .map((v, i) =>
-      processKeystrokeData({ keydown: keydown[i], keyup: keyup[i] })
-    );
-
-  const passwordsEqual = processedAttempts.every((v) =>
-    _.isEqual(v.full.keys, processedAttempts[0].full.keys)
-  );
-
-  const userInDb = await findUser(username);
-
-  if (!passwordsEqual) {
-    // If the entered passwords don't match
-    return res
-      .status(403)
-      .json({ success: false, msg: "Passwords don't match" });
-  }
-
-  if (userInDb) {
-    // If the user already exists in the Database
-    return res
-      .status(403)
-      .json({ success: false, msg: "User already exists in DB" });
-  }
-
-  const signupData = createSignupDataFromProcessedData(
-    username,
-    passwords,
-    processedAttempts
-  );
-
-  try {
-    const newUserData = await signUpNewUser(signupData);
-    logger.info(`Signed up ${username}`);
-    return res.json({
-      success: true,
-      username: newUserData.username,
-    });
-  } catch (error) {
-    logger.error(error);
-    logger.debug(req.body);
-    return res
-      .status(500)
-      .json({ success: false, msg: "Error signing up", error });
-  }
-});
-
-router.post("/login", async (req, res) => {
-  const { password, keydown, keyup, username, controls } = req.body;
-
-  const processedAttempt = processKeystrokeData({ keydown, keyup });
-
-  const userInDb = await findUser(username);
-
-  if (!userInDb) {
-    // If the user does not exist in the Database
-    return res
-      .status(403)
-      .json({ success: false, msg: "User does not exist in DB" });
-  }
-
-  const credentialsValid =
-    password === userInDb.password &&
-    _.isEqual(processedAttempt.hold.keys, userInDb.keystrokeData.hold.keys) &&
-    _.isEqual(
-      processedAttempt.flight.keys,
-      userInDb.keystrokeData.flight.keys
-    ) &&
-    _.isEqual(processedAttempt.dd.keys, userInDb.keystrokeData.dd.keys) &&
-    _.isEqual(processedAttempt.full.keys, userInDb.keystrokeData.full.keys);
-
-  if (!credentialsValid) {
-    return res.status(403).json({ success: false, msg: "Invalid Credentials" });
-  }
-
-  const result = processAttempt({
-    userKeystrokeData: userInDb.keystrokeData,
-    attemptKeystrokeData: processedAttempt,
-    controls,
-  });
-
-  if (result.accepted) {
-    const newUserData = addAttemptToKeystrokeData({
-      userData: userInDb,
-      attemptKeystrokeData: processedAttempt,
-    });
-    newUserData.__v += 1;
-
-    await updateUser({
-      username,
-      updateData: newUserData,
-    });
-  }
-
-  return res.json({
-    result,
-    db: {
-      hold: userInDb.keystrokeData.hold.means,
-      flight: userInDb.keystrokeData.flight.means,
-      dd: userInDb.keystrokeData.dd.means,
-      full: userInDb.keystrokeData.full.means,
-    },
-    filteredDb: {
-      hold: userInDb.keystrokeData.hold.filteredMeans,
-      flight: userInDb.keystrokeData.flight.filteredMeans,
-      dd: userInDb.keystrokeData.dd.filteredMeans,
-      full: userInDb.keystrokeData.full.filteredMeans,
-    },
-    attempt: {
-      hold: processedAttempt.hold.times,
-      flight: processedAttempt.flight.times,
-      dd: processedAttempt.dd.times,
-      full: processedAttempt.full.times,
-    },
-  });
-});
-
-module.exports = router;

server.js

-const express = require("express");
-const mongoose = require("mongoose");
-
-// Utilities
-const { logger, expressWinstonLogger } = require("./utilities/loggers.js");
-
-// Routes
-const userRoute = require("./routes/user");
-
-// Environment constants
-const API_PORT = process.env.API_PORT || 3001;
-const MONGO_URL = "";
-
-// Service Initialisation
-mongoose.connect(MONGO_URL, {
-  useFindAndModify: false,
-  useNewUrlParser: true,
-  useUnifiedTopology: true,
-});
-const db = mongoose.connection;
-
-db.on("error", logger.error.bind(logger, "connection error:"));
-db.once("open", () => {
-  logger.info("Connected to MongoDB Instance");
-});
-
-// Express Initialisation
-const app = express();
-
-app.use(express.urlencoded({ extended: false }));
-app.use(expressWinstonLogger);
-
-app.use((req, res, next) => {
-  res.header("Access-Control-Allow-Origin", "*");
-  res.header(
-    "Access-Control-Allow-Headers",
-    "Origin, X-Requested-With, Content-Type, Accept"
-  );
-  res.header("Access-Control-Allow-Methods", "GET, POST, OPTIONS");
-
-  if (req.method === "OPTIONS") {
-    return res.status(200).end();
-  }
-
-  return next();
-});
-
-// Routes
-app.use("/user", userRoute);
-
-app.get("/", (req, res) => {
-  res.json({ msg: "Default Route" });
-});
-
-app.listen(API_PORT, () => logger.info(`Listening on port ${API_PORT}`));

utilities/loggers.js

-const winston = require('winston');
-const expressWinston = require('express-winston');
-
-// const winstonLevels = {
-// error: 0,
-// warn: 1,
-// info: 2,
-// verbose: 3,
-// debug: 4,
-// silly: 5,
-// };
-
-module.exports.logger = winston.createLogger({
-  transports: [
-    new winston.transports.Console(),
-  ],
-  format: winston.format.combine(
-    winston.format.colorize(),
-    winston.format.simple(),
-  ),
-});
-
-module.exports.expressWinstonLogger = expressWinston.logger({
-  transports: [
-    new winston.transports.Console({ level: 'verbose' }),
-  ],
-  format: winston.format.combine(
-    winston.format.colorize(),
-    winston.format.simple(),
-  ),
-  msg() {
-    return '{{res.statusCode}} {{req.method}} {{req.url}} {{res.responseTime}}ms';
-  },
-  colorize: true,
-
-  meta: false,
-
-  statusLevels: false, // default value
-  level(req, res) {
-    let level = '';
-    if (res.statusCode >= 100) { level = 'verbose'; }
-    if (res.statusCode >= 400) { level = 'warn'; }
-    if (res.statusCode >= 500) { level = 'error'; }
-    // Ops is worried about hacking attempts so make Unauthorized and Forbidden critical
-    // if (res.statusCode == 401 || res.statusCode == 403) { level = "critical"; }
-    return level;
-  },
-});