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 it1924312

package.json → NodeServer/package.json

@@ -11,6 +11,7 @@
   "author": "Namit Nathwani",
   "license": "ISC",
   "dependencies": {
+    "axios": "^1.3.4",
     "bcryptjs": "^2.4.3",
     "cors": "^2.8.5",
     "express": "^4.18.2",

src/config/contants.ts → NodeServer/src/config/contants.ts

@@ -13,3 +13,4 @@ export const DEFAULT_CONTROLS = {
     use: true,
   },
 };
+export const UTILITY_SERVER = "http://127.0.0.1:8000";

src/config/types.ts → NodeServer/src/config/types.ts

@@ -47,6 +47,16 @@ export type AuthType = {
   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;
@@ -60,6 +70,10 @@ export type CandidateType = {
   dateOfBirth: string;
   jobIds: string[];
   profilePicture: string;
+  state: "INTIAL" | "READY";
+  resume?: string;
+  resumeData?: ResumeDataType;
+  selfIntro?: string;
 };
 
 export type OrganizationType = {
@@ -87,6 +101,48 @@ export type ControlsType = {
   };
 };
 
+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;
+    voiceVerification?: string;
+  };
+  score: {
+    primary: number;
+    primatyMatch: string[];
+    secondary: number;
+    secondaryMatch: string[];
+    similarity: number;
+    total: number;
+  };
+};
+
+export type AnalyseApplicationPayload = {
+  applicationId: string;
+  startTime: number;
+  endTime: number;
+};
+
 export interface TypedRequest<T extends Query, U> extends Request {
   body: U;
   query: T;

src/index.ts → NodeServer/src/index.ts

@@ -8,11 +8,11 @@ app.use(cors());
 
 // Routes
 const authRoute = require("./routes/auth");
-
-// Environment constants
+const userRoute = require("./routes/user");
+const jobsRoute = require("./routes/jobs");
+const applicationsRoute = require("./routes/application");
 
 // Service Initialisation
-
 mongoose.connect(MONGO_URL, {
   useFindAndModify: false,
   useNewUrlParser: true,
@@ -26,5 +26,8 @@ app.use(express.json());
 
 // Routes
 app.use("/auth", authRoute);
+app.use("/user", userRoute);
+app.use("/jobs", jobsRoute);
+app.use("/applications", applicationsRoute);
 
 app.listen(API_PORT, () => console.log(`Listening on port ${API_PORT}`));

src/middlewares/auth.ts → 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,
@@ -21,3 +23,37 @@ export const authMiddleware = (
     }
   }
 };
+
+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,
+      voiceVerification: 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;

src/models/Candidate.ts → NodeServer/src/models/Candidate.ts

 import { Schema, model } from "mongoose";
-import { AddressType, CandidateType } from "../config/types";
+import { AddressType, CandidateType, ResumeDataType } from "../config/types";
 
 const AddressSchema = new Schema<AddressType>(
   {
@@ -19,6 +19,18 @@ const ContactsSchema = new Schema<AddressType>(
   },
   { 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,
@@ -27,6 +39,10 @@ const candidateSchema = new Schema<CandidateType>({
   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 },
+  selfIntro: { type: String, require: false },
 });
 
 const Candidates = model<CandidateType>("candidates", candidateSchema);

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/routes/application.ts

+import { Router } from "express";
+import {
+  ApplicationType,
+  TypedRequest,
+  AnalyseApplicationPayload,
+} from "../config/types";
+import { authMiddleware, candidateMiddleware } from "../middlewares/auth";
+import Application from "../models/Application";
+import Jobs from "../models/Job";
+import ResumeAPI from "../utilities/apis/resume";
+import VoiceAPI from "../utilities/apis/voice";
+
+const router = Router();
+
+router.post(
+  "/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 });
+    }
+  }
+);
+
+router.put(
+  "/update",
+  authMiddleware,
+  async (
+    req: TypedRequest<
+      {},
+      {
+        applicationId: string;
+        update: Partial<ApplicationType>;
+        candidateId: string;
+      }
+    >,
+    res
+  ) => {
+    let update = req.body.update;
+
+    if (update.interview?.videoRef) {
+      try {
+        VoiceAPI.verifyVoice({
+          video_url: update.interview?.videoRef,
+          user_id: req.body.candidateId,
+          application_id: req.body.applicationId,
+        });
+      } catch (error) {}
+    }
+
+    Application.findByIdAndUpdate(req.body.applicationId, {
+      $set: update,
+    })
+      .then((_application) => {
+        res.json({
+          success: true,
+          application: { ..._application, ...update },
+        });
+      })
+      .catch((err) => res.send(err));
+  }
+);
+
+router.put(
+  "/update/voice-verification",
+  async (
+    req: TypedRequest<
+      {},
+      {
+        applicationId: string;
+        update: number;
+      }
+    >,
+    res
+  ) => {
+    const update = req.body.update.toString();
+
+    Application.findByIdAndUpdate(req.body.applicationId, {
+      $set: { "interview.voiceVerification": update },
+    })
+      .then((_application) => {
+        res.send("success");
+      })
+      .catch((err) => res.send(err));
+  }
+);
+
+router.post(
+  "/analyse",
+  authMiddleware,
+  async (req: TypedRequest<{}, AnalyseApplicationPayload>, res) => {
+    const { applicationId, startTime, endTime } = req.body;
+    const payload = {
+      start: startTime,
+      end: endTime,
+      application_id: applicationId,
+    };
+
+    try {
+      const voiceData = await VoiceAPI.analyseVoice(payload);
+
+      return res.json({ voice: voiceData });
+    } catch (error) {
+      return res.status(500).send(error);
+    }
+  }
+);
+
+module.exports = router;

src/routes/auth.ts → NodeServer/src/routes/auth.ts

@@ -137,7 +137,7 @@ router.post("/login", async (req: TypedRequest<{}, SignInPayload>, res) => {
     }
 
     const token = await jwt.sign({ userId: auth.userId }, JWT_SECRET, {
-      expiresIn: "2h",
+      expiresIn: "5h",
     });
 
     return res.json({

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",
+              "selfIntro",
+            ],
+          },
+        });
+      }
+
+      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 });
+    }
+  }
+);
+
+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";
+import VoiceAPI from "../utilities/apis/voice";
+
+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: update });
+      if (req.body?.selfIntro) {
+        try {
+          VoiceAPI.enrollVoice({
+            user_id: req.query.userId,
+            video_url: req.body.selfIntro,
+          });
+        } catch (error) {}
+      }
+
+      return res.status(200).json({ data: req.body });
+    } catch (error) {
+      return res.status(400).send(error);
+    }
+  }
+);
+
+module.exports = router;

NodeServer/src/utilities/apis/facial.ts

+import { request } from "../requests";
+
+export default class FacialAPI {
+  static analyseEyeBlinks = (payload: {
+    start: number;
+    end: number;
+    application_id: string;
+  }) => request("<BASE_URL>/facial/eye-blinks", "POST", payload);
+}

NodeServer/src/utilities/apis/resume.ts

+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);
+}

NodeServer/src/utilities/apis/voice.ts

+import { request } from "../requests";
+
+export default class VoiceAPI {
+  static enrollVoice = (payload: { video_url: string; user_id: string }) =>
+    request("<BASE_URL>/voice/enroll", "POST", payload);
+
+  static verifyVoice = (payload: {
+    video_url: string;
+    user_id: string;
+    application_id: string;
+  }) => request("<BASE_URL>/voice/verify", "POST", payload);
+
+  static analyseVoice = (payload: {
+    start: number;
+    end: number;
+    application_id: string;
+  }) => request("<BASE_URL>/voice/analyse", "POST", payload);
+}

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

@@ -401,8 +401,15 @@ export const processAttempt = ({
     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;
 };

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: 90000,
+    })
+      .then(async (response: AxiosResponse) => {
+        resolve(response.data);
+      })
+      .catch(async (error: AxiosError) => {
+        if (error?.response) {
+          return reject(error?.response?.data);
+        }
+        reject(error);
+      });
+  });

yarn.lock → NodeServer/yarn.lock

@@ -276,6 +276,20 @@ astral-regex@^2.0.0:
   resolved "https://registry.npmjs.org/astral-regex/-/astral-regex-2.0.0.tgz"
   integrity sha512-Z7tMw1ytTXt5jqMcOP+OQteU1VuNK9Y02uuJtKQ1Sv69jXQKKg5cibLwGJow8yzZP+eAc18EmLGPal0bp36rvQ==
 
+asynckit@^0.4.0:
+  version "0.4.0"
+  resolved "https://registry.yarnpkg.com/asynckit/-/asynckit-0.4.0.tgz#c79ed97f7f34cb8f2ba1bc9790bcc366474b4b79"
+  integrity sha512-Oei9OH4tRh0YqU3GxhX79dM/mwVgvbZJaSNaRk+bshkj0S5cfHcgYakreBjrHwatXKbz+IoIdYLxrKim2MjW0Q==
+
+axios@^1.3.4:
+  version "1.3.4"
+  resolved "https://registry.yarnpkg.com/axios/-/axios-1.3.4.tgz#f5760cefd9cfb51fd2481acf88c05f67c4523024"
+  integrity sha512-toYm+Bsyl6VC5wSkfkbbNB6ROv7KY93PEBBL6xyDczaIHasAiv4wPqQ/c4RjoQzipxRD2W5g21cOqQulZ7rHwQ==
+  dependencies:
+    follow-redirects "^1.15.0"
+    form-data "^4.0.0"
+    proxy-from-env "^1.1.0"
+
 balanced-match@^1.0.0:
   version "1.0.2"
   resolved "https://registry.npmjs.org/balanced-match/-/balanced-match-1.0.2.tgz"
@@ -441,6 +455,13 @@ color-name@~1.1.4:
   resolved "https://registry.npmjs.org/color-name/-/color-name-1.1.4.tgz"
   integrity sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==
 
+combined-stream@^1.0.8:
+  version "1.0.8"
+  resolved "https://registry.yarnpkg.com/combined-stream/-/combined-stream-1.0.8.tgz#c3d45a8b34fd730631a110a8a2520682b31d5a7f"
+  integrity sha512-FQN4MRfuJeHf7cBbBMJFXhKSDq+2kAArBlmRBvcvFE5BB1HZKXtSFASDhdlz9zOYwxh8lDdnvmMOe/+5cdoEdg==
+  dependencies:
+    delayed-stream "~1.0.0"
+
 complex.js@^2.0.11:
   version "2.1.1"
   resolved "https://registry.npmjs.org/complex.js/-/complex.js-2.1.1.tgz"
@@ -546,6 +567,11 @@ define-properties@^1.1.3, define-properties@^1.1.4:
     has-property-descriptors "^1.0.0"
     object-keys "^1.1.1"
 
+delayed-stream@~1.0.0:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/delayed-stream/-/delayed-stream-1.0.0.tgz#df3ae199acadfb7d440aaae0b29e2272b24ec619"
+  integrity sha512-ZySD7Nf91aLB0RxL4KGrKHBXl7Eds1DAmEdcoVawXnLD7SDhpNgtuII2aAkg7a7QS41jxPSZ17p4VdGnMHk3MQ==
+
 denque@^1.4.1:
   version "1.5.1"
   resolved "https://registry.npmjs.org/denque/-/denque-1.5.1.tgz"
@@ -934,6 +960,20 @@ flatted@^3.1.0:
   resolved "https://registry.npmjs.org/flatted/-/flatted-3.2.7.tgz"
   integrity sha512-5nqDSxl8nn5BSNxyR3n4I6eDmbolI6WT+QqR547RwxQapgjQBmtktdP+HTBb/a/zLsbzERTONyUB5pefh5TtjQ==
 
+follow-redirects@^1.15.0:
+  version "1.15.2"
+  resolved "https://registry.yarnpkg.com/follow-redirects/-/follow-redirects-1.15.2.tgz#b460864144ba63f2681096f274c4e57026da2c13"
+  integrity sha512-VQLG33o04KaQ8uYi2tVNbdrWp1QWxNNea+nmIB4EVM28v0hmP17z7aG1+wAkNzVq4KeXTq3221ye5qTJP91JwA==
+
+form-data@^4.0.0:
+  version "4.0.0"
+  resolved "https://registry.yarnpkg.com/form-data/-/form-data-4.0.0.tgz#93919daeaf361ee529584b9b31664dc12c9fa452"
+  integrity sha512-ETEklSGi5t0QMZuiXoA/Q6vcnxcLQP5vdugSpuAyi6SVGi2clPPp+xgEhuMaHC+zGgn31Kd235W35f7Hykkaww==
+  dependencies:
+    asynckit "^0.4.0"
+    combined-stream "^1.0.8"
+    mime-types "^2.1.12"
+
 forwarded@0.2.0:
   version "0.2.0"
   resolved "https://registry.npmjs.org/forwarded/-/forwarded-0.2.0.tgz"
@@ -1410,7 +1450,7 @@ mime-db@1.52.0:
   resolved "https://registry.npmjs.org/mime-db/-/mime-db-1.52.0.tgz"
   integrity sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg==
 
-mime-types@~2.1.24, mime-types@~2.1.34:
+mime-types@^2.1.12, mime-types@~2.1.24, mime-types@~2.1.34:
   version "2.1.35"
   resolved "https://registry.npmjs.org/mime-types/-/mime-types-2.1.35.tgz"
   integrity sha512-ZDY+bPm5zTTF+YpCrAU9nK0UgICYPT0QtT1NZWFv4s++TNkcgVaT0g6+4R2uI4MjQjzysHB1zxuWL50hzaeXiw==
@@ -1682,6 +1722,11 @@ proxy-addr@~2.0.7:
     forwarded "0.2.0"
     ipaddr.js "1.9.1"
 
+proxy-from-env@^1.1.0:
+  version "1.1.0"
+  resolved "https://registry.yarnpkg.com/proxy-from-env/-/proxy-from-env-1.1.0.tgz#e102f16ca355424865755d2c9e8ea4f24d58c3e2"
+  integrity sha512-D+zkORCbA9f1tdWRK0RaCR3GPv50cMxcrz4X8k5LTSUD1Dkw47mKJEZQNunItRTkWwgtaUSo1RVFRIG9ZXiFYg==
+
 pstree.remy@^1.1.8:
   version "1.1.8"
   resolved "https://registry.npmjs.org/pstree.remy/-/pstree.remy-1.1.8.tgz"

PythonServer/.gitignore

+__pycache__
+**/__pycache__
+voices/auth/embed/**
+voices/auth/temp/**
+voices/interviews/**
+videos/interviews/**
+resumes
+models/**
+data/**
\ No newline at end of file

PythonServer/README.md

+## Install python
+
+install python 3.9 and set the env path
+
+## Create conda python 3.9 env
+
+conda create -n server tensorflow python=3.9
+
+conda install scipy=1.9.3<br/>
+
+- download and install correct scipy wheel https://pypi.org/project/scipy/#files : pip install <filename>.whl
+
+pip install gensim==3.8.1<br/>
+pip install texthero==1.1.0<br/>
+
+conda install -c conda-forge importlib_metadata<br/>
+pip install pyresparser==1.0.6<br/>
+pip install soundfile==0.10.3.post1<br/>
+pip install librosa==0.9.2
+
+conda install -c blaze sqlite3
+conda install tqdm=4.65.0
+conda install pandas=1.5.3
+conda install pytesseract
+conda install "uvicorn[standard]"
+conda install -c conda-forge fastapi
+conda install websockets
+
+pip install moviepy
+pip install "libclang>=13.0.0"
+pip install "tensorflow-io-gcs-filesystem>=0.23.1"
+pip install pyannote.audio
+pip install pyannote.core
+pip install PyAudio
+pip install python_speech_features
+
+pip install fer==22.4.0
+
+[OPTIONAL]
+pip install pillow==9.0.0
+conda install "numpy<1.24.0"
+conda install cudatoolkit
+conda update --all
+
+### Install dlib
+
+- Install Cmake : https://cmake.org/download/
+- Install C++ compiler : https://visualstudio.microsoft.com/visual-cpp-build-tools
+- pip install cmake
+- pip install dlib==19.24
+
+### End dlib
+
+pip install imutils==0.5.4
+
+## Run server
+
+uvicorn main:app --reload
+
+## Datasets & models
+
+voice: https://drive.google.com/file/d/1wWsrN2Ep7x6lWqOXfr4rpKGYrJhWc8z7/view
+models: https://drive.google.com/file/d/1TWjIiyyInXHaXySKymM6VcsIR5YMVQ1V/view?usp=share_link
+shape_predictor_68_face_landmarks: https://www.kaggle.com/datasets/sergiovirahonda/shape-predictor-68-face-landmarksdat
+
+## Folders
+
+voices>auth>embed
+voices>auth>temp
+voices>interviews
+models
+data>voice
+
+### Other conda commands
+
+DELETE ENV : conda remove -n ENV_NAME --all
+EXPORT ENV : conda env export > server_env.yaml
+CREATE ENV BY ENV FILE : conda env create -f server_env.yaml
+LIST ENV : conda list --export > requirements.txt
+INSTALL ENV : conda install --file requirements.txt

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
+
+class EnrollVoice(BaseModel):
+    video_url: str
+    user_id:str
+
+class VerifyVoice(BaseModel):
+    video_url: str
+    application_id:str
+    user_id:str
+
+class AnalyseVoice(BaseModel):
+    start: int
+    end:int
+    application_id:str
+
+class CountBlinks(BaseModel):
+    start: int
+    end:int
+    application_id:str
\ No newline at end of file

PythonServer/conda-requirements.txt

+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: win-64
+absl-py=1.4.0=pypi_0
+aiohttp=3.8.4=pypi_0
+aiosignal=1.3.1=pypi_0
+alembic=1.10.2=pypi_0
+antlr4-python3-runtime=4.9.3=pypi_0
+anyio=3.6.2=pyhd8ed1ab_0
+aom=3.5.0=h63175ca_0
+asteroid-filterbanks=0.4.0=pypi_0
+astunparse=1.6.3=pypi_0
+async-timeout=4.0.2=pypi_0
+attrs=22.2.0=pypi_0
+audioread=3.0.0=pypi_0
+backports-cached-property=1.0.2=pypi_0
+blis=0.7.9=pypi_0
+brotli=1.0.9=hcfcfb64_8
+brotli-bin=1.0.9=hcfcfb64_8
+brotlipy=0.7.0=py39ha55989b_1005
+bzip2=1.0.8=h8ffe710_4
+ca-certificates=2022.12.7=h5b45459_0
+cachetools=5.3.0=pypi_0
+catalogue=1.0.2=pypi_0
+certifi=2022.12.7=pyhd8ed1ab_0
+cffi=1.15.1=py39h68f70e3_3
+chardet=5.1.0=pypi_0
+charset-normalizer=3.1.0=pypi_0
+click=8.1.3=win_pyhd8ed1ab_2
+cmaes=0.9.1=pypi_0
+colorama=0.4.6=pyhd8ed1ab_0
+colorlog=6.7.0=pypi_0
+commonmark=0.9.1=pypi_0
+contourpy=1.0.7=py39h1f6ef14_0
+cryptography=40.0.1=py39hb6bd5e6_0
+cycler=0.11.0=pyhd8ed1ab_0
+cymem=2.0.7=pypi_0
+decorator=5.1.1=pyhd8ed1ab_0
+docopt=0.6.2=pypi_0
+docx2txt=0.8=pypi_0
+einops=0.3.2=pypi_0
+en-core-web-sm=2.3.1=pypi_0
+expat=2.5.0=h1537add_0
+fastapi=0.95.0=pyhd8ed1ab_0
+ffmpeg=5.1.2=gpl_h5b1d025_106
+filelock=3.10.7=pypi_0
+flatbuffers=23.3.3=pypi_0
+font-ttf-dejavu-sans-mono=2.37=hab24e00_0
+font-ttf-inconsolata=3.000=h77eed37_0
+font-ttf-source-code-pro=2.038=h77eed37_0
+font-ttf-ubuntu=0.83=hab24e00_0
+fontconfig=2.14.2=hbde0cde_0
+fonts-conda-ecosystem=1=0
+fonts-conda-forge=1=0
+fonttools=4.39.3=py39ha55989b_0
+freetype=2.12.1=h546665d_1
+frozenlist=1.3.3=pypi_0
+fsspec=2023.3.0=pypi_0
+future=0.18.3=pyhd8ed1ab_0
+gast=0.4.0=pypi_0
+gensim=3.8.1=pypi_0
+gettext=0.21.1=h5728263_0
+glib=2.74.1=h12be248_1
+glib-tools=2.74.1=h12be248_1
+google-auth=2.17.0=pypi_0
+google-auth-oauthlib=0.4.6=pypi_0
+google-pasta=0.2.0=pypi_0
+greenlet=2.0.2=pypi_0
+grpcio=1.53.0=pypi_0
+gst-plugins-base=1.22.0=h001b923_2
+gstreamer=1.22.0=h6b5321d_2
+h11=0.14.0=pyhd8ed1ab_0
+h5py=3.8.0=pypi_0
+hmmlearn=0.2.8=pypi_0
+huggingface-hub=0.13.3=pypi_0
+hyperpyyaml=1.1.0=pypi_0
+icu=70.1=h0e60522_0
+idna=3.4=pyhd8ed1ab_0
+imageio=2.27.0=pyh24c5eb1_0
+imageio-ffmpeg=0.4.8=pyhd8ed1ab_0
+importlib-metadata=6.1.0=pyha770c72_0
+importlib-resources=5.12.0=pyhd8ed1ab_0
+importlib_metadata=6.1.0=hd8ed1ab_0
+importlib_resources=5.12.0=pyhd8ed1ab_0
+intel-openmp=2023.0.0=h57928b3_25922
+joblib=1.2.0=pyhd8ed1ab_0
+jsonschema=4.17.3=pypi_0
+julius=0.2.7=pypi_0
+keras=2.10.0=pypi_0
+keras-preprocessing=1.1.2=pypi_0
+kiwisolver=1.4.4=py39h1f6ef14_1
+krb5=1.20.1=heb0366b_0
+lcms2=2.15=h3e3b177_1
+lerc=4.0.0=h63175ca_0
+libblas=3.9.0=16_win64_mkl
+libbrotlicommon=1.0.9=hcfcfb64_8
+libbrotlidec=1.0.9=hcfcfb64_8
+libbrotlienc=1.0.9=hcfcfb64_8
+libcblas=3.9.0=16_win64_mkl
+libclang=16.0.0=pypi_0
+libclang13=15.0.7=default_h77d9078_1
+libdeflate=1.18=hcfcfb64_0
+libffi=3.4.2=h8ffe710_5
+libglib=2.74.1=he8f3873_1
+libhwloc=2.9.0=h51c2c0f_0
+libiconv=1.17=h8ffe710_0
+libjpeg-turbo=2.1.5.1=hcfcfb64_0
+liblapack=3.9.0=16_win64_mkl
+libogg=1.3.4=h8ffe710_1
+libopus=1.3.1=h8ffe710_1
+libpng=1.6.39=h19919ed_0
+librosa=0.9.2=pypi_0
+libsqlite=3.40.0=hcfcfb64_0
+libtiff=4.5.0=h6c8260b_6
+libvorbis=1.3.7=h0e60522_0
+libwebp-base=1.3.0=hcfcfb64_0
+libxcb=1.13=hcd874cb_1004
+libxml2=2.10.3=hc3477c8_6
+libzlib=1.2.13=hcfcfb64_4
+llvmlite=0.39.1=pypi_0
+m2w64-gcc-libgfortran=5.3.0=6
+m2w64-gcc-libs=5.3.0=7
+m2w64-gcc-libs-core=5.3.0=7
+m2w64-gmp=6.1.0=2
+m2w64-libwinpthread-git=5.0.0.4634.697f757=2
+mako=1.2.4=pypi_0
+markdown=3.4.3=pypi_0
+markupsafe=2.1.2=pypi_0
+matplotlib=3.7.1=py39hcbf5309_0
+matplotlib-base=3.7.1=py39haf65ace_0
+mkl=2022.1.0=h6a75c08_874
+moviepy=1.0.3=pyhd8ed1ab_1
+mpmath=1.3.0=pypi_0
+msys2-conda-epoch=20160418=1
+multidict=6.0.4=pypi_0
+munkres=1.1.4=pyh9f0ad1d_0
+murmurhash=1.0.9=pypi_0
+networkx=2.8.8=pypi_0
+nltk=3.8.1=pypi_0
+numba=0.56.4=pypi_0
+numpy=1.23.5=pypi_0
+oauthlib=3.2.2=pypi_0
+olefile=0.46=pypi_0
+omegaconf=2.3.0=pypi_0
+openh264=2.3.1=h63175ca_2
+openjpeg=2.5.0=ha2aaf27_2
+openssl=3.1.0=hcfcfb64_0
+opt-einsum=3.3.0=pypi_0
+optuna=3.1.0=pypi_0
+packaging=23.0=pyhd8ed1ab_0
+pandas=1.5.3=py39h2ba5b7c_0
+pcre2=10.40=h17e33f8_0
+pdfminer=20191125=pyhd8ed1ab_1
+pdfminer-six=20221105=pypi_0
+pillow=9.4.0=py39haa1d754_2
+pip=23.0.1=pyhd8ed1ab_0
+plac=1.1.3=pypi_0
+platformdirs=3.2.0=pypi_0
+plotly=5.13.1=pypi_0
+ply=3.11=py_1
+pooch=1.7.0=pypi_0
+preprocess=1.2.3=py_1
+preshed=3.0.8=pypi_0
+primepy=1.3=pypi_0
+proglog=0.1.9=py_0
+protobuf=3.19.6=pypi_0
+pthread-stubs=0.4=hcd874cb_1001
+pthreads-win32=2.9.1=hfa6e2cd_3
+pyannote-audio=2.1.1=pypi_0
+pyannote-core=4.5=pypi_0
+pyannote-database=4.1.3=pypi_0
+pyannote-metrics=3.2.1=pypi_0
+pyannote-pipeline=2.3=pypi_0
+pyasn1=0.4.8=pypi_0
+pyasn1-modules=0.2.8=pypi_0
+pyaudio=0.2.13=pypi_0
+pycparser=2.21=pyhd8ed1ab_0
+pycryptodome=3.17=pypi_0
+pydantic=1.10.7=py39ha55989b_0
+pydeprecate=0.3.2=pypi_0
+pygments=2.14.0=pypi_0
+pyopenssl=23.1.1=pyhd8ed1ab_0
+pyparsing=3.0.9=pyhd8ed1ab_0
+pyqt=5.15.7=py39hb77abff_3
+pyqt5-sip=12.11.0=py39h99910a6_3
+pyresparser=1.0.6=pypi_0
+pyrsistent=0.19.3=pypi_0
+pysocks=1.7.1=pyh0701188_6
+python=3.9.16=h4de0772_0_cpython
+python-dateutil=2.8.2=pyhd8ed1ab_0
+python-speech-features=0.6=pypi_0
+python_abi=3.9=3_cp39
+pytorch-lightning=1.6.5=pypi_0
+pytorch-metric-learning=1.7.3=pypi_0
+pytz=2023.3=pyhd8ed1ab_0
+pyyaml=6.0=pypi_0
+qt-main=5.15.8=h88fe7eb_7
+regex=2023.3.23=pypi_0
+requests=2.28.2=pyhd8ed1ab_0
+requests-oauthlib=1.3.1=pypi_0
+resampy=0.4.2=pypi_0
+rich=12.6.0=pypi_0
+rsa=4.9=pypi_0
+ruamel-yaml=0.17.21=pypi_0
+ruamel-yaml-clib=0.2.7=pypi_0
+scikit-learn=1.2.0=py39hd77b12b_1
+scipy=1.9.3=py39hfbf2dce_2
+semver=2.13.0=pypi_0
+sentencepiece=0.1.97=pypi_0
+setuptools=67.6.1=pyhd8ed1ab_0
+shellingham=1.5.0.post1=pypi_0
+simplejson=3.18.4=pypi_0
+singledispatchmethod=1.0=pypi_0
+sip=6.7.7=py39h99910a6_0
+six=1.16.0=pyh6c4a22f_0
+smart-open=6.3.0=pypi_0
+sniffio=1.3.0=pyhd8ed1ab_0
+sortedcontainers=2.4.0=pypi_0
+soundfile=0.10.3.post1=pypi_0
+spacy=2.3.9=pypi_0
+speechbrain=0.5.14=pypi_0
+sqlalchemy=2.0.7=pypi_0
+sqlite3=3.8.6=0
+srsly=1.0.6=pypi_0
+starlette=0.26.1=pyhd8ed1ab_0
+svt-av1=1.4.1=h63175ca_0
+sympy=1.11.1=pypi_0
+tabulate=0.9.0=pypi_0
+tbb=2021.8.0=h91493d7_0
+tenacity=8.2.2=pypi_0
+tensorboard=2.10.1=pypi_0
+tensorboard-data-server=0.6.1=pypi_0
+tensorboard-plugin-wit=1.8.1=pypi_0
+tensorflow=2.10.1=pypi_0
+tensorflow-estimator=2.10.0=pypi_0
+tensorflow-io-gcs-filesystem=0.31.0=pypi_0
+termcolor=2.2.0=pypi_0
+texthero=1.1.0=pypi_0
+thinc=7.4.6=pypi_0
+threadpoolctl=3.1.0=pyh8a188c0_0
+tk=8.6.12=h8ffe710_0
+toml=0.10.2=pyhd8ed1ab_0
+torch=1.13.1=pypi_0
+torch-audiomentations=0.11.0=pypi_0
+torch-pitch-shift=1.2.2=pypi_0
+torchaudio=0.13.1=pypi_0
+torchmetrics=0.11.4=pypi_0
+tornado=6.2=py39ha55989b_1
+tqdm=4.65.0=pyhd8ed1ab_1
+typer=0.7.0=pypi_0
+typing-extensions=4.5.0=hd8ed1ab_0
+typing_extensions=4.5.0=pyha770c72_0
+tzdata=2023c=h71feb2d_0
+ucrt=10.0.22621.0=h57928b3_0
+unicodedata2=15.0.0=py39ha55989b_0
+unidecode=1.3.6=pypi_0
+urllib3=1.26.15=pyhd8ed1ab_0
+uvicorn=0.21.1=py39hcbf5309_0
+vc=14.3=hb6edc58_10
+vs2015_runtime=14.34.31931=h4c5c07a_10
+wasabi=0.10.1=pypi_0
+werkzeug=2.2.3=pypi_0
+wheel=0.40.0=pyhd8ed1ab_0
+win_inet_pton=1.1.0=pyhd8ed1ab_6
+wordcloud=1.8.2.2=pypi_0
+wrapt=1.15.0=pypi_0
+x264=1!164.3095=h8ffe710_2
+x265=3.5=h2d74725_3
+xorg-libxau=1.0.9=hcd874cb_0
+xorg-libxdmcp=1.1.3=hcd874cb_0
+xz=5.2.6=h8d14728_0
+yarl=1.8.2=pypi_0
+zipp=3.15.0=pyhd8ed1ab_0
+zstd=1.5.2=h12be248_6

PythonServer/main.py

+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+
+# from fer import FER
+
+import routes.resume as resumes
+import routes.voice as voice
+import routes.facial as facial
+
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+app.include_router(resumes.router)
+app.include_router(voice.router)
+app.include_router(facial.router)
+
+@app.get("/")
+def read_root():
+    return {"status": "running"}
+
+

PythonServer/pip-requirements.txt

+absl-py==1.4.0
+aiohttp==3.8.4
+aiosignal==1.3.1
+alembic==1.10.2
+antlr4-python3-runtime==4.9.3
+anyio @ file:///home/conda/feedstock_root/build_artifacts/anyio_1666191106763/work/dist
+asteroid-filterbanks==0.4.0
+astunparse==1.6.3
+async-timeout==4.0.2
+attrs==22.2.0
+audioread==3.0.0
+backports.cached-property==1.0.2
+blis==0.7.9
+brotlipy @ file:///D:/bld/brotlipy_1666764815687/work
+cachetools==5.3.0
+catalogue==1.0.2
+certifi==2022.12.7
+cffi @ file:///D:/bld/cffi_1671179514672/work
+chardet==5.1.0
+charset-normalizer==3.1.0
+click @ file:///D:/bld/click_1666798499870/work
+cmaes==0.9.1
+colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1666700638685/work
+colorlog==6.7.0
+commonmark==0.9.1
+contourpy @ file:///D:/bld/contourpy_1673633852898/work
+cryptography @ file:///D:/bld/cryptography-split_1679811407000/work
+cycler @ file:///home/conda/feedstock_root/build_artifacts/cycler_1635519461629/work
+cymem==2.0.7
+decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1641555617451/work
+docopt==0.6.2
+docx2txt==0.8
+einops==0.3.2
+en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz
+fastapi @ file:///home/conda/feedstock_root/build_artifacts/fastapi_1679196090342/work
+filelock==3.10.7
+flatbuffers==23.3.3
+fonttools @ file:///D:/bld/fonttools_1680021390608/work
+frozenlist==1.3.3
+fsspec==2023.3.0
+future @ file:///home/conda/feedstock_root/build_artifacts/future_1673596611778/work
+gast==0.4.0
+gensim==3.8.1
+google-auth==2.17.0
+google-auth-oauthlib==0.4.6
+google-pasta==0.2.0
+greenlet==2.0.2
+grpcio==1.53.0
+h11 @ file:///home/conda/feedstock_root/build_artifacts/h11_1664132893548/work
+h5py==3.8.0
+hmmlearn==0.2.8
+huggingface-hub==0.13.3
+HyperPyYAML==1.1.0
+idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work
+imageio @ file:///home/conda/feedstock_root/build_artifacts/imageio_1679914882579/work
+imageio-ffmpeg @ file:///home/conda/feedstock_root/build_artifacts/imageio-ffmpeg_1673483481485/work
+importlib-metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1679167925176/work
+importlib-resources @ file:///home/conda/feedstock_root/build_artifacts/importlib_resources_1676919000169/work
+joblib @ file:///home/conda/feedstock_root/build_artifacts/joblib_1663332044897/work
+jsonschema==4.17.3
+julius==0.2.7
+keras==2.10.0
+Keras-Preprocessing==1.1.2
+kiwisolver @ file:///D:/bld/kiwisolver_1666805897768/work
+libclang==16.0.0
+librosa==0.9.2
+llvmlite==0.39.1
+Mako==1.2.4
+Markdown==3.4.3
+MarkupSafe==2.1.2
+matplotlib @ file:///D:/bld/matplotlib-suite_1678135799522/work
+moviepy @ file:///home/conda/feedstock_root/build_artifacts/moviepy_1665160419595/work
+mpmath==1.3.0
+multidict==6.0.4
+munkres==1.1.4
+murmurhash==1.0.9
+networkx==2.8.8
+nltk==3.8.1
+numba==0.56.4
+numpy==1.23.5
+oauthlib==3.2.2
+olefile==0.46
+omegaconf==2.3.0
+opt-einsum==3.3.0
+optuna==3.1.0
+packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1673482170163/work
+pandas @ file:///D:/bld/pandas_1674136542219/work
+pdfminer @ file:///home/conda/feedstock_root/build_artifacts/pdfminer_1613401440402/work
+pdfminer.six==20221105
+Pillow @ file:///D:/bld/pillow_1678273632076/work
+plac==1.1.3
+platformdirs==3.2.0
+plotly==5.13.1
+ply==3.11
+pooch==1.7.0
+preprocess==1.2.3
+preshed==3.0.8
+primePy==1.3
+proglog==0.1.9
+protobuf==3.19.6
+pyannote.audio==2.1.1
+pyannote.core==4.5
+pyannote.database==4.1.3
+pyannote.metrics==3.2.1
+pyannote.pipeline==2.3
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+PyAudio==0.2.13
+pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work
+pycryptodome==3.17
+pydantic @ file:///D:/bld/pydantic_1679565539355/work
+pyDeprecate==0.3.2
+Pygments==2.14.0
+pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1680037383858/work
+pyparsing @ file:///home/conda/feedstock_root/build_artifacts/pyparsing_1652235407899/work
+PyQt5==5.15.7
+PyQt5-sip @ file:///D:/bld/pyqt-split_1674666735227/work/pyqt_sip
+pyresparser==1.0.6
+pyrsistent==0.19.3
+PySocks @ file:///D:/bld/pysocks_1661604991356/work
+python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1626286286081/work
+python-speech-features==0.6
+pytorch-lightning==1.6.5
+pytorch-metric-learning==1.7.3
+pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1680088766131/work
+PyYAML==6.0
+regex==2023.3.23
+requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1673863902341/work
+requests-oauthlib==1.3.1
+resampy==0.4.2
+rich==12.6.0
+rsa==4.9
+ruamel.yaml==0.17.21
+ruamel.yaml.clib==0.2.7
+scikit-learn @ file:///C:/b/abs_e01rh8f1vi/croot/scikit-learn_1675454931501/work
+scipy==1.9.3
+semver==2.13.0
+sentencepiece==0.1.97
+shellingham==1.5.0.post1
+simplejson==3.18.4
+singledispatchmethod==1.0
+sip @ file:///D:/bld/sip_1675696791179/work
+six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work
+smart-open==6.3.0
+sniffio @ file:///home/conda/feedstock_root/build_artifacts/sniffio_1662051266223/work
+sortedcontainers==2.4.0
+SoundFile==0.10.3.post1
+spacy==2.3.9
+speechbrain==0.5.14
+SQLAlchemy==2.0.7
+srsly==1.0.6
+starlette @ file:///home/conda/feedstock_root/build_artifacts/starlette-recipe_1678817698143/work
+sympy==1.11.1
+tabulate==0.9.0
+tenacity==8.2.2
+tensorboard==2.10.1
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+tensorflow==2.10.1
+tensorflow-estimator==2.10.0
+tensorflow-io-gcs-filesystem==0.31.0
+termcolor==2.2.0
+texthero==1.1.0
+thinc==7.4.6
+threadpoolctl @ file:///home/conda/feedstock_root/build_artifacts/threadpoolctl_1643647933166/work
+toml @ file:///home/conda/feedstock_root/build_artifacts/toml_1604308577558/work
+torch==1.13.1
+torch-audiomentations==0.11.0
+torch-pitch-shift==1.2.2
+torchaudio==0.13.1
+torchmetrics==0.11.4
+tornado @ file:///D:/bld/tornado_1666788767305/work
+tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1677948868469/work
+typer==0.7.0
+typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1678559861143/work
+unicodedata2 @ file:///D:/bld/unicodedata2_1667240049903/work
+Unidecode==1.3.6
+urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1678635778344/work
+uvicorn @ file:///D:/bld/uvicorn-split_1678984112139/work
+wasabi==0.10.1
+Werkzeug==2.2.3
+win-inet-pton @ file:///D:/bld/win_inet_pton_1667051142467/work
+wordcloud==1.8.2.2
+wrapt==1.15.0
+yarl==1.8.2
+zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1677313463193/work

PythonServer/requirements.txt

+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: win-64
+_tflow_select=2.3.0=mkl
+absl-py=1.3.0=py39haa95532_0
+aiohttp=3.8.3=py39h2bbff1b_0
+aiosignal=1.2.0=pyhd3eb1b0_0
+alembic=1.10.3=pypi_0
+antlr4-python3-runtime=4.9.3=pypi_0
+appdirs=1.4.4=pyhd3eb1b0_0
+asteroid-filterbanks=0.4.0=pypi_0
+astunparse=1.6.3=py_0
+async-timeout=4.0.2=py39haa95532_0
+attrs=22.1.0=py39haa95532_0
+audioread=3.0.0=pypi_0
+backports-cached-property=1.0.2=pypi_0
+blas=1.0=mkl
+blinker=1.4=py39haa95532_0
+blis=0.7.9=pypi_0
+bottleneck=1.3.5=py39h080aedc_0
+brotlipy=0.7.0=py39h2bbff1b_1003
+ca-certificates=2023.01.10=haa95532_0
+cachetools=4.2.2=pyhd3eb1b0_0
+catalogue=1.0.2=pypi_0
+certifi=2022.12.7=py39haa95532_0
+cffi=1.15.1=py39h2bbff1b_3
+chardet=5.1.0=pypi_0
+charset-normalizer=2.0.4=pyhd3eb1b0_0
+click=8.0.4=py39haa95532_0
+cmaes=0.9.1=pypi_0
+colorama=0.4.6=py39haa95532_0
+colorlog=6.7.0=pypi_0
+commonmark=0.9.1=pypi_0
+contourpy=1.0.7=pypi_0
+cryptography=39.0.1=py39h21b164f_0
+cycler=0.11.0=pypi_0
+cymem=2.0.7=pypi_0
+decorator=5.1.1=pypi_0
+docopt=0.6.2=pypi_0
+docx2txt=0.8=pypi_0
+einops=0.3.2=pypi_0
+fer=22.4.0=pypi_0
+filelock=3.11.0=pypi_0
+flatbuffers=2.0.0=h6c2663c_0
+flit-core=3.8.0=py39haa95532_0
+fonttools=4.39.3=pypi_0
+frozenlist=1.3.3=py39h2bbff1b_0
+fsspec=2023.4.0=pypi_0
+gast=0.4.0=pyhd3eb1b0_0
+gensim=3.8.1=pypi_0
+giflib=5.2.1=h8cc25b3_3
+google-auth=2.6.0=pyhd3eb1b0_0
+google-auth-oauthlib=0.4.4=pyhd3eb1b0_0
+google-pasta=0.2.0=pyhd3eb1b0_0
+greenlet=2.0.2=pypi_0
+grpcio=1.42.0=py39hc60d5dd_0
+h11=0.12.0=pyhd3eb1b0_0
+h5py=3.7.0=py39h3de5c98_0
+hdf5=1.10.6=h1756f20_1
+hmmlearn=0.2.8=pypi_0
+huggingface-hub=0.13.4=pypi_0
+hyperpyyaml=1.2.0=pypi_0
+icc_rt=2022.1.0=h6049295_2
+icu=58.2=ha925a31_3
+idna=3.4=py39haa95532_0
+importlib-metadata=6.0.0=py39haa95532_0
+importlib-resources=5.12.0=pypi_0
+importlib_metadata=6.0.0=hd3eb1b0_0
+intel-openmp=2021.4.0=haa95532_3556
+joblib=1.2.0=pypi_0
+jpeg=9e=h2bbff1b_1
+jsonschema=4.17.3=pypi_0
+julius=0.2.7=pypi_0
+keras=2.10.0=py39haa95532_0
+keras-preprocessing=1.1.2=pyhd3eb1b0_0
+kiwisolver=1.4.4=pypi_0
+libclang=16.0.0=pypi_0
+libcurl=7.88.1=h86230a5_0
+libpng=1.6.39=h8cc25b3_0
+libprotobuf=3.20.3=h23ce68f_0
+librosa=0.9.2=pypi_0
+libssh2=1.10.0=hcd4344a_0
+llvmlite=0.39.1=pypi_0
+mako=1.2.4=pypi_0
+markdown=3.4.1=py39haa95532_0
+markupsafe=2.1.1=py39h2bbff1b_0
+matplotlib=3.7.1=pypi_0
+mkl=2021.4.0=haa95532_640
+mkl-service=2.4.0=py39h2bbff1b_0
+mkl_fft=1.3.1=py39h277e83a_0
+mkl_random=1.2.2=py39hf11a4ad_0
+mpmath=1.3.0=pypi_0
+mtcnn=0.1.1=pypi_0
+multidict=6.0.2=py39h2bbff1b_0
+murmurhash=1.0.9=pypi_0
+networkx=2.8.8=pypi_0
+nltk=3.8.1=pypi_0
+numba=0.56.4=pypi_0
+numexpr=2.8.4=py39h5b0cc5e_0
+numpy=1.23.5=py39h3b20f71_0
+numpy-base=1.23.5=py39h4da318b_0
+oauthlib=3.2.2=py39haa95532_0
+omegaconf=2.3.0=pypi_0
+opencv-contrib-python=4.7.0.72=pypi_0
+opencv-python=4.7.0.72=pypi_0
+openssl=1.1.1t=h2bbff1b_0
+opt_einsum=3.3.0=pyhd3eb1b0_1
+optuna=3.1.1=pypi_0
+packaging=23.0=py39haa95532_0
+pandas=2.0.0=pypi_0
+pdfminer-six=20221105=pypi_0
+pillow=9.5.0=pypi_0
+pip=23.0.1=py39haa95532_0
+plac=1.1.3=pypi_0
+plotly=5.14.1=pypi_0
+pooch=1.4.0=pyhd3eb1b0_0
+preshed=3.0.8=pypi_0
+primepy=1.3=pypi_0
+protobuf=3.19.6=pypi_0
+pyannote-audio=2.1.1=pypi_0
+pyannote-core=4.5=pypi_0
+pyannote-database=4.1.3=pypi_0
+pyannote-metrics=3.2.1=pypi_0
+pyannote-pipeline=2.3=pypi_0
+pyasn1=0.4.8=pyhd3eb1b0_0
+pyasn1-modules=0.2.8=py_0
+pyaudio=0.2.13=pypi_0
+pycparser=2.21=pyhd3eb1b0_0
+pycryptodome=3.17=pypi_0
+pydeprecate=0.3.2=pypi_0
+pygments=2.14.0=pypi_0
+pyjwt=2.4.0=py39haa95532_0
+pyopenssl=23.0.0=py39haa95532_0
+pyparsing=3.0.9=pypi_0
+pyresparser=1.0.6=pypi_0
+pyrsistent=0.19.3=pypi_0
+pysocks=1.7.1=py39haa95532_0
+python=3.9.16=h6244533_2
+python-dateutil=2.8.2=pyhd3eb1b0_0
+python-flatbuffers=2.0=pyhd3eb1b0_0
+python-speech-features=0.6=pypi_0
+pytorch-lightning=1.6.5=pypi_0
+pytorch-metric-learning=1.7.3=pypi_0
+pytz=2023.3=pypi_0
+pyyaml=6.0=pypi_0
+regex=2023.3.23=pypi_0
+requests=2.28.1=py39haa95532_1
+requests-oauthlib=1.3.0=py_0
+resampy=0.4.2=pypi_0
+rich=12.6.0=pypi_0
+rsa=4.7.2=pyhd3eb1b0_1
+ruamel-yaml=0.17.21=pypi_0
+ruamel-yaml-clib=0.2.7=pypi_0
+scikit-learn=1.2.2=pypi_0
+scipy=1.9.3=py39h321e85e_1
+semver=2.13.0=pypi_0
+sentencepiece=0.1.97=pypi_0
+setuptools=65.6.3=py39haa95532_0
+shellingham=1.5.0.post1=pypi_0
+simplejson=3.19.1=pypi_0
+singledispatchmethod=1.0=pypi_0
+six=1.16.0=pyhd3eb1b0_1
+smart-open=6.3.0=pypi_0
+snappy=1.1.9=h6c2663c_0
+sortedcontainers=2.4.0=pypi_0
+soundfile=0.10.3.post1=pypi_0
+spacy=2.3.9=pypi_0
+speechbrain=0.5.14=pypi_0
+sqlalchemy=2.0.9=pypi_0
+sqlite=3.41.1=h2bbff1b_0
+sqlite3=3.8.6=0
+srsly=1.0.6=pypi_0
+sympy=1.11.1=pypi_0
+tabulate=0.9.0=pypi_0
+tenacity=8.2.2=pypi_0
+tensorboard=2.10.0=py39haa95532_0
+tensorboard-data-server=0.6.1=py39haa95532_0
+tensorboard-plugin-wit=1.8.1=py39haa95532_0
+tensorflow=2.10.0=mkl_py39ha510bab_0
+tensorflow-base=2.10.0=mkl_py39h6a7f48e_0
+tensorflow-estimator=2.10.0=py39haa95532_0
+tensorflow-io-gcs-filesystem=0.31.0=pypi_0
+termcolor=2.1.0=py39haa95532_0
+texthero=1.1.0=pypi_0
+thinc=7.4.6=pypi_0
+threadpoolctl=3.1.0=pypi_0
+torch=1.13.1=pypi_0
+torch-audiomentations=0.11.0=pypi_0
+torch-pitch-shift=1.2.3=pypi_0
+torchaudio=0.13.1=pypi_0
+torchmetrics=0.11.4=pypi_0
+tqdm=4.65.0=py39hd4e2768_0
+typer=0.7.0=pypi_0
+typing_extensions=4.4.0=py39haa95532_0
+tzdata=2023.3=pypi_0
+unidecode=1.3.6=pypi_0
+urllib3=1.26.15=py39haa95532_0
+uvicorn=0.20.0=py39haa95532_0
+vc=14.2=h21ff451_1
+vs2015_runtime=14.27.29016=h5e58377_2
+wasabi=0.10.1=pypi_0
+websockets=10.4=py39h2bbff1b_1
+werkzeug=2.2.3=py39haa95532_0
+wheel=0.38.4=py39haa95532_0
+win_inet_pton=1.1.0=py39haa95532_0
+wincertstore=0.2=py39haa95532_2
+wordcloud=1.8.2.2=pypi_0
+wrapt=1.14.1=py39h2bbff1b_0
+yarl=1.8.1=py39h2bbff1b_0
+zipp=3.11.0=py39haa95532_0
+zlib=1.2.13=h8cc25b3_0

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/facial.py

+from fastapi import APIRouter, WebSocket, WebSocketDisconnect
+from fer import FER
+import cv2
+import imutils 
+from scripts.blink_detection import count_blinks
+from baseModels.payloads import CountBlinks
+from scripts.web_socket import ConnectionManager
+import json
+
+manager = ConnectionManager()
+router = APIRouter(prefix='/facial')
+
+@router.post("/eye-blinks")
+def countBlinks(payload:CountBlinks):
+    video_filename = 'videos/interviews/'+payload.application_id+'.mp4'
+    count = count_blinks(video_filename, payload.start, payload.end)
+    return count
+
+@router.websocket("/ws/eye-blinks")
+async def countBlinks(websocket: WebSocket,  application_id: str, start:str, end:str):
+    await manager.connect(websocket)
+    try:
+        while True:
+            video_filename = 'videos/interviews/'+application_id+'.mp4'
+            count = count_blinks(video_filename, int(start), int(end))
+            await manager.send_private(json.dumps({"count":count, "end":True}), websocket)
+            await manager.disconnect(websocket)
+            
+    except WebSocketDisconnect:
+        await manager.send_private(json.dumps({"end":True}), websocket)
+        await manager.disconnect(websocket)
+    return
+
+@router.websocket("/ws/emotions")
+async def emotions(websocket: WebSocket, application_id: str):
+    await manager.connect(websocket)
+    video_filename = 'videos/interviews/'+application_id+'.mp4'
+    face_detector = FER(mtcnn=True)
+    cap = cv2.VideoCapture(video_filename)
+    try:
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                await manager.send_private(json.dumps({"data":[], "end":True}), websocket)
+                await manager.disconnect(websocket)
+                break
+            
+            frame = imutils.resize(frame, width=800)
+            emotions = face_detector.detect_emotions(frame)
+            print(emotions)
+            await manager.send_private(json.dumps({"data":emotions, "end":False}), websocket)
+            
+    except WebSocketDisconnect:
+        await manager.send_private(json.dumps({"data":[], "end":True, "status":"Offline"}), websocket)
+        await manager.disconnect(websocket)
+
+

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/routes/voice.py

+from fastapi import APIRouter
+import moviepy.editor
+import pickle
+import os 
+import urllib
+import numpy as np
+from baseModels.payloads import EnrollVoice, AnalyseVoice, VerifyVoice
+from scipy.spatial.distance import euclidean
+from scripts.processing import extract_input_feature
+from keras.models import load_model
+from scripts.parameters import MODEL_FILE, MAX_SEC
+from scripts.voice_feature_extraction import get_embedding
+from scripts.api import SingletonAiohttp
+
+router = APIRouter(prefix='/voice')
+
+@router.post("/enroll")
+def enroll(payload:EnrollVoice):
+    video_filename = 'voices/'+payload.user_id+'.mp4'
+    urllib.request.urlretrieve(payload.video_url, video_filename) 
+
+    # Download video and save audio
+    video = moviepy.editor.VideoFileClip(video_filename)
+    audio = video.audio
+    audio_filename = 'voices/auth/temp'+payload.user_id+'.wav'
+    audio.write_audiofile(audio_filename, codec='pcm_s16le', bitrate='50k')
+
+    # Extract and entrol audio features
+    model = load_model(MODEL_FILE)
+    enroll_result = get_embedding(model, audio_filename, MAX_SEC)
+    enroll_embs = np.array(enroll_result.tolist()) 
+    np.save("voices/auth/embed/"+ payload.user_id+".npy", enroll_embs)
+    
+    try:
+        os.remove(video_filename)
+    except:
+        print('')
+
+    try:
+        os.remove(audio_filename)
+    except:
+        print('')
+
+    return 'SUCCESS'
+
+@router.post("/verify")
+async def verify(payload:VerifyVoice):
+    video_filename = 'videos/interviews/'+payload.application_id+'.mp4'
+    urllib.request.urlretrieve(payload.video_url, video_filename)
+
+    # Download video and save audio
+    video = moviepy.editor.VideoFileClip(video_filename)
+    audio = video.audio
+    audio_filename = 'voices/interviews/'+payload.application_id+'.wav'
+    audio.write_audiofile(audio_filename, codec='pcm_s16le', bitrate='50k')
+
+    model = load_model(MODEL_FILE) 
+    test_result = get_embedding(model, audio_filename, MAX_SEC)
+    test_embs = np.array(test_result.tolist())
+    enroll_embs = np.load("voices/auth/embed/"+ payload.user_id+".npy") 
+    distance = euclidean(test_embs, enroll_embs)
+    url = "http://localhost:5000/applications/update/voice-verification"
+    payload = {'update':round(1-distance, 5), 'applicationId':payload.application_id }
+
+    await SingletonAiohttp.put_url(url, payload)
+
+    return 'success'
+
+@router.post("/analyse")
+def analys(payload:AnalyseVoice):
+    model = pickle.load(open("models/voice_classifier.model", "rb"))
+    filename = 'voices/interviews/'+payload.application_id+'.wav'
+    features = extract_input_feature(filename, mfcc=True, chroma=True, mel=True, start=float(payload.start), end=float(payload.end)).reshape(1, -1)
+    result = model.predict(features)[0]
+    return result
\ No newline at end of file

PythonServer/scripts/api.py

+import aiohttp
+from socket import AF_INET
+from typing import List, Optional, Any, Dict
+
+SIZE_POOL_AIOHTTP = 100
+
+
+class SingletonAiohttp:
+    aiohttp_client: Optional[aiohttp.ClientSession] = None
+
+    @classmethod
+    def get_aiohttp_client(cls) -> aiohttp.ClientSession:
+        if cls.aiohttp_client is None:
+            timeout = aiohttp.ClientTimeout(total=2)
+            connector = aiohttp.TCPConnector(family=AF_INET, limit_per_host=SIZE_POOL_AIOHTTP)
+            cls.aiohttp_client = aiohttp.ClientSession(timeout=timeout, connector=connector)
+
+        return cls.aiohttp_client
+
+    @classmethod
+    async def close_aiohttp_client(cls) -> None:
+        if cls.aiohttp_client:
+            await cls.aiohttp_client.close()
+            cls.aiohttp_client = None
+
+    @classmethod
+    async def post_url(cls, url: str) -> Any:
+        client = cls.get_aiohttp_client()
+
+        try:
+            async with client.post(url) as response:
+                if response.status != 200:
+                    return {"ERROR OCCURED" + str(await response.text())}
+
+                json_result = await response.json()
+        except Exception as e:
+            return {"ERROR": e}
+
+        return json_result
+    
+    @classmethod
+    async def put_url(cls, url: str, payload) -> Any:
+        client = cls.get_aiohttp_client()
+
+        try:
+            async with client.put(url, data=payload) as response:
+                if response.status != 200:
+                    return {"ERROR OCCURED" + str(await response.text())}
+
+                json_result = await response.json()
+        except Exception as e:
+            return {"ERROR": e}
+
+        return json_result
\ No newline at end of file

PythonServer/scripts/blink_detection.py

+import cv2
+import dlib
+import imutils
+import numpy as np
+from imutils import face_utils
+from scipy.spatial import distance as dist
+from scripts.parameters import EYE_AR_THRESH, EYE_AR_CONSEC_FRAMES
+
+
+def eye_aspect_ratio(eye):
+    A = dist.euclidean(eye[1], eye[5])
+    B = dist.euclidean(eye[2], eye[4])
+    C = dist.euclidean(eye[0], eye[3])
+    ear = (A + B) / (2.0 * C)
+    return ear
+
+ 
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor("models/shape_predictor_68_face_landmarks.dat")
+
+
+(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
+(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
+
+def count_blinks(filename, start=0, end=0): 
+    COUNTER = 0
+    TOTAL = 0
+    vs = cv2.VideoCapture(filename)
+    fps = vs.get(cv2.CAP_PROP_FPS)
+
+    start_frame = start * fps
+    end_frame = end * fps
+    current_frame = start_frame
+
+    vs.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
+
+    while (current_frame<=end_frame):
+        ret, frame = vs.read()
+
+        if ret == True:
+            frame = imutils.resize(frame, width=800)
+            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            rects = detector(gray, 0)
+            current_frame = current_frame + 1
+            print('FRAME : '+str(current_frame) + ' Blinks :'+ str(TOTAL))
+
+            for rect in rects:
+
+                shape = predictor(gray, rect)
+                shape = face_utils.shape_to_np(shape)
+
+                leftEye = shape[lStart:lEnd]
+                rightEye = shape[rStart:rEnd]
+                leftEAR = eye_aspect_ratio(leftEye)
+                rightEAR = eye_aspect_ratio(rightEye)
+
+                ear = (leftEAR + rightEAR) / 2.0 
+
+                if ear < EYE_AR_THRESH:
+                    COUNTER += 1
+                else:
+                    if COUNTER >= EYE_AR_CONSEC_FRAMES:
+                        TOTAL += 1
+                    COUNTER = 0
+        else:
+            break
+                
+    vs.release()
+    return TOTAL
\ No newline at end of file

PythonServer/scripts/parameters.py

+from pyaudio import paInt16
+
+# Signal processing
+SAMPLE_RATE = 16000
+PREEMPHASIS_ALPHA = 0.97
+FRAME_LEN = 0.025
+FRAME_STEP = 0.01
+NUM_FFT = 512
+BUCKET_STEP = 1
+MAX_SEC = 10
+
+# Model
+MODEL_FILE = "models/voice_auth_model_cnn"
+COST_METRIC = "cosine"  # euclidean or cosine
+INPUT_SHAPE=(NUM_FFT,None,1)
+
+# IO
+EMBED_LIST_FILE = "voices/auth/embed"
+
+# Recognition
+THRESHOLD = 0.2
+
+EMOTIONS = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
+
+EYE_AR_THRESH = 0.2
+EYE_AR_CONSEC_FRAMES = 3
\ No newline at end of file

PythonServer/scripts/processing.py

+import pandas as pd
+import texthero as hero
+import numpy as np
+import librosa
+
+from pyresparser.utils import extract_text
+
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+from pyannote.audio import Audio
+from pyannote.core import Segment
+
+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)}
+
+
+def extract_input_feature(file_name, **kwargs):
+    mfcc = kwargs.get("mfcc")
+    chroma = kwargs.get("chroma")
+    mel = kwargs.get("mel")
+    contrast = kwargs.get("contrast")
+    tonnetz = kwargs.get("tonnetz")
+    start = kwargs.get("start")
+    end = kwargs.get("end")
+    sample_rate = 16000
+
+    audio = Audio(sample_rate=sample_rate, mono=True)
+    segment = Segment(start, end)
+    sound, sample_rate = audio.crop(file_name, segment)
+    X = sound[0].numpy()  
+      
+    if chroma or contrast:
+        stft = np.abs(librosa.stft(X))
+    result = np.array([])
+    if mfcc:
+        mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)
+        result = np.hstack((result, mfccs))
+    if chroma:
+        chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)
+        result = np.hstack((result, chroma))
+    if mel:
+        mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)
+        result = np.hstack((result, mel))
+    if contrast:
+        contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)
+        result = np.hstack((result, contrast))
+    if tonnetz:
+        tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0)
+        result = np.hstack((result, tonnetz))
+    return result

PythonServer/scripts/train.py

+import glob
+import os
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import accuracy_score
+import librosa
+import soundfile
+import pickle
+
+EMOTIONS = {
+    "01": "neutral",
+    "02": "calm",
+    "03": "happy",
+    "04": "sad",
+    "05": "angry",
+    "06": "fearful",
+    "07": "disgust",
+    "08": "surprised"
+}
+
+
+AVAILABLE_EMOTIONS = {
+    "angry",
+    "sad",
+    "neutral",
+    "happy"
+}
+
+def extract_feature(file_name, **kwargs):
+    mfcc = kwargs.get("mfcc")
+    chroma = kwargs.get("chroma")
+    mel = kwargs.get("mel")
+    contrast = kwargs.get("contrast")
+    tonnetz = kwargs.get("tonnetz")
+    sample_rate = 16000
+
+    with soundfile.SoundFile(file_name) as sound_file:
+        X = sound_file.read(dtype="float32") 
+        if chroma or contrast:
+            stft = np.abs(librosa.stft(X))
+        result = np.array([])
+        if mfcc:
+            mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)
+            result = np.hstack((result, mfccs))
+        if chroma:
+            chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)
+            result = np.hstack((result, chroma))
+        if mel:
+            mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)
+            result = np.hstack((result, mel))
+        if contrast:
+            contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)
+            result = np.hstack((result, contrast))
+        if tonnetz:
+            tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0)
+            result = np.hstack((result, tonnetz))
+    return result
+
+
+# update random_state=9
+def load_data(test_size=0.2, random_state=7):
+    X, y = [], []
+    for file in glob.glob("../data/voice/Actor_*/*.wav"):
+        basename = os.path.basename(file)
+        emotion = EMOTIONS[basename.split("-")[2]]
+
+        if emotion not in AVAILABLE_EMOTIONS:
+            continue
+
+        features = extract_feature(file, mfcc=True, chroma=True, mel=True)
+        X.append(features)
+        y.append(emotion)
+
+    return train_test_split(np.array(X), y, test_size=test_size, random_state=random_state)
+
+def train_voice():
+    X_train, X_test, y_train, y_test = load_data(test_size=0.25)
+    model=MLPClassifier(alpha=0.01, batch_size=256, epsilon=1e-08, hidden_layer_sizes=(300,), learning_rate='adaptive', max_iter=500)
+    model.fit(X_train, y_train)
+    y_pred = model.predict(X_test)
+    accuracy = accuracy_score(y_true=y_test, y_pred=y_pred)
+    print("Accuracy: {:.2f}%".format(accuracy*100))
+
+    if not os.path.isdir("result"):
+        os.mkdir("result")
+
+    pickle.dump(model, open("../result/mlp_classifier.model", "wb"))
+
+train_voice()
\ No newline at end of file

PythonServer/scripts/voice_feature_extraction.py

+import os
+import numpy as np
+import pandas as pd
+# from scipy.spatial.distance import cdist, euclidean, cosine
+
+from scripts.voice_preprocess import get_fft_spectrum
+from scripts.parameters import BUCKET_STEP,FRAME_STEP,MAX_SEC
+
+
+def buckets(max_time, steptime, frameskip):
+    buckets = {}
+    frames_per_sec = int(1/frameskip)
+    end_frame = int(max_time*frames_per_sec)
+    step_frame = int(steptime*frames_per_sec)
+    for i in range(0, end_frame+1, step_frame):
+        s = i
+        s = np.floor((s-7+2)/2) + 1  # for first conv layer
+        s = np.floor((s-3)/2) + 1    # for first maxpool layer
+        s = np.floor((s-5+2)/2) + 1  # for second conv layer
+        s = np.floor((s-3)/2) + 1    # for second maxpool layer
+        s = np.floor((s-3+2)/1) + 1  # for third conv layer
+        s = np.floor((s-3+2)/1) + 1  # for fourth conv layer
+        s = np.floor((s-3+2)/1) + 1  # for fifth conv layer
+        s = np.floor((s-3)/2) + 1    # for fifth maxpool layer
+        s = np.floor((s-1)/1) + 1    # for sixth fully connected layer
+        if s > 0:
+            buckets[i] = int(s)
+    return buckets
+
+
+def get_embedding(model, wav_file, max_time):
+    buckets_var = buckets(MAX_SEC, BUCKET_STEP, FRAME_STEP)
+    signal = get_fft_spectrum(wav_file, buckets_var)
+    embedding = np.squeeze(model.predict(signal.reshape(1,*signal.shape,1)))
+    return embedding
+
+
+def get_embedding_batch(model, wav_files, max_time):
+    return [ get_embedding(model, wav_file, max_time) for wav_file in wav_files ]
+
+
+def get_embeddings_from_list_file(model, list_file, max_time):
+    buckets_var = buckets(MAX_SEC, BUCKET_STEP, FRAME_STEP)
+    result = pd.read_csv(list_file, delimiter=",")
+    result['features'] = result['filename'].apply(lambda x: get_fft_spectrum(x, buckets_var))
+    result['embedding'] = result['features'].apply(lambda x: np.squeeze(model.predict(x.reshape(1,*x.shape,1))))
+    return result[['filename','speaker','embedding']]

PythonServer/scripts/voice_preprocess.py

+import librosa
+import numpy as np
+from scipy.signal import lfilter, butter
+from python_speech_features import sigproc
+from scripts.parameters import SAMPLE_RATE, PREEMPHASIS_ALPHA, FRAME_LEN, FRAME_STEP, NUM_FFT
+
+def load(filename, sample_rate):
+    audio, sr = librosa.load(filename, sr=sample_rate, mono=True)
+    audio = audio.flatten()
+    return audio
+
+
+def normalize_frames(m,epsilon=1e-12):
+    return np.array([(v - np.mean(v)) / max(np.std(v),epsilon) for v in m])
+
+
+# Valuable dc and dither removal function implemented 
+# https://github.com/christianvazquez7/ivector/blob/master/MSRIT/rm_dc_n_dither.m
+def remove_dc_and_dither(sin, sample_rate):
+    if sample_rate == 16e3:
+        alpha = 0.99
+    elif sample_rate == 8e3:
+        alpha = 0.999
+    else:
+        print("Sample rate must be 16kHz or 8kHz only")
+        exit(1)
+    sin = lfilter([1,-1], [1,-alpha], sin)
+    dither = np.random.random_sample(len(sin)) + np.random.random_sample(len(sin)) - 1
+    spow = np.std(dither)
+    sout = sin + 1e-6 * spow * dither
+    return sout
+
+
+def get_fft_spectrum(filename, buckets):
+    signal = load(filename, SAMPLE_RATE)
+    signal *= 2**15
+
+    # get FFT spectrum
+    signal = remove_dc_and_dither(signal, SAMPLE_RATE)
+    signal = sigproc.preemphasis(signal, coeff=PREEMPHASIS_ALPHA)
+    frames = sigproc.framesig(signal, frame_len=FRAME_LEN*SAMPLE_RATE, frame_step=FRAME_STEP*SAMPLE_RATE, winfunc=np.hamming)
+    fft = abs(np.fft.fft(frames,n=NUM_FFT))
+    fft_norm = normalize_frames(fft.T)
+
+    # truncate to max bucket sizes
+    rsize = max(k for k in buckets if k <= fft_norm.shape[1])
+    rstart = int((fft_norm.shape[1]-rsize)/2)
+    out = fft_norm[:,rstart:rstart+rsize]
+
+    return out

PythonServer/scripts/web_socket.py

+from typing import List
+from fastapi import WebSocket
+
+class ConnectionManager:
+
+    def __init__(self) -> None:
+        self.active_connections: List[WebSocket] = []
+
+    async def connect(self, websocket: WebSocket):
+        await websocket.accept()
+        self.active_connections.append(websocket)
+
+    def disconnect(self, websocket: WebSocket):
+        self.active_connections.remove(websocket)
+
+    async def send_private(self, message: str, websocket: WebSocket):
+        await websocket.send_text(message)
+
+    async def broadcast(self, message: str):
+        for connection in self.active_connections:
+            await connection.send_text(message)
\ No newline at end of file

PythonServer/server_env.yaml

+name: pyserver
+channels:
+  - blaze
+  - conda-forge
+  - defaults
+dependencies:
+  - _tflow_select=2.3.0=mkl
+  - absl-py=1.4.0=pyhd8ed1ab_0
+  - aiohttp=3.8.4=py39ha55989b_0
+  - aiosignal=1.3.1=pyhd8ed1ab_0
+  - anyio=3.6.2=pyhd8ed1ab_0
+  - aom=3.5.0=h63175ca_0
+  - astunparse=1.6.3=pyhd8ed1ab_0
+  - async-timeout=4.0.2=pyhd8ed1ab_0
+  - attrs=22.2.0=pyh71513ae_0
+  - blinker=1.5=pyhd8ed1ab_0
+  - brotlipy=0.7.0=py39ha55989b_1005
+  - bzip2=1.0.8=h8ffe710_4
+  - ca-certificates=2022.12.7=h5b45459_0
+  - cachetools=5.3.0=pyhd8ed1ab_0
+  - certifi=2022.12.7=pyhd8ed1ab_0
+  - cffi=1.15.1=py39h68f70e3_3
+  - charset-normalizer=2.1.1=pyhd8ed1ab_0
+  - click=8.1.3=win_pyhd8ed1ab_2
+  - colorama=0.4.6=pyhd8ed1ab_0
+  - cryptography=38.0.4=py39h58e9bdb_0
+  - decorator=5.1.1=pyhd8ed1ab_0
+  - expat=2.5.0=h63175ca_1
+  - fastapi=0.95.0=pyhd8ed1ab_0
+  - ffmpeg=5.1.2=gpl_h5b1d025_106
+  - flatbuffers=23.3.3=h63175ca_0
+  - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
+  - font-ttf-inconsolata=3.000=h77eed37_0
+  - font-ttf-source-code-pro=2.038=h77eed37_0
+  - font-ttf-ubuntu=0.83=hab24e00_0
+  - fontconfig=2.14.2=hbde0cde_0
+  - fonts-conda-ecosystem=1=0
+  - fonts-conda-forge=1=0
+  - freetype=2.12.1=h546665d_1
+  - frozenlist=1.3.3=py39ha55989b_0
+  - gast=0.4.0=pyh9f0ad1d_0
+  - giflib=5.2.1=h64bf75a_3
+  - google-auth=2.17.0=pyh1a96a4e_0
+  - google-auth-oauthlib=0.4.6=pyhd8ed1ab_0
+  - google-pasta=0.2.0=pyh8c360ce_0
+  - grpcio=1.42.0=py39hc60d5dd_0
+  - h11=0.14.0=pyhd8ed1ab_0
+  - h5py=3.7.0=py39h3de5c98_0
+  - hdf5=1.10.6=nompi_h5268f04_1114
+  - icu=58.2=ha925a31_3
+  - idna=3.4=pyhd8ed1ab_0
+  - imageio=2.27.0=pyh24c5eb1_0
+  - imageio-ffmpeg=0.4.8=pyhd8ed1ab_0
+  - importlib-metadata=6.1.0=pyha770c72_0
+  - importlib_metadata=6.1.0=hd8ed1ab_0
+  - intel-openmp=2023.0.0=h57928b3_25922
+  - jpeg=9e=hcfcfb64_3
+  - keras=2.10.0=py39haa95532_0
+  - keras-preprocessing=1.1.2=pyhd8ed1ab_0
+  - krb5=1.20.1=h6609f42_0
+  - lcms2=2.15=ha5c8aab_0
+  - lerc=4.0.0=h63175ca_0
+  - libblas=3.9.0=16_win64_mkl
+  - libcblas=3.9.0=16_win64_mkl
+  - libcurl=7.88.1=h68f0423_1
+  - libdeflate=1.17=hcfcfb64_0
+  - libexpat=2.5.0=h63175ca_1
+  - libffi=3.4.2=h8ffe710_5
+  - libhwloc=2.9.0=h51c2c0f_0
+  - libiconv=1.17=h8ffe710_0
+  - liblapack=3.9.0=16_win64_mkl
+  - libopus=1.3.1=h8ffe710_1
+  - libpng=1.6.39=h19919ed_0
+  - libprotobuf=3.20.2=h12be248_0
+  - libsqlite=3.40.0=hcfcfb64_0
+  - libssh2=1.10.0=h680486a_3
+  - libtiff=4.5.0=hf8721a0_2
+  - libwebp-base=1.3.0=hcfcfb64_0
+  - libxcb=1.13=hcd874cb_1004
+  - libxml2=2.10.3=hc3477c8_6
+  - libzlib=1.2.13=hcfcfb64_4
+  - m2w64-gcc-libgfortran=5.3.0=6
+  - m2w64-gcc-libs=5.3.0=7
+  - m2w64-gcc-libs-core=5.3.0=7
+  - m2w64-gmp=6.1.0=2
+  - m2w64-libwinpthread-git=5.0.0.4634.697f757=2
+  - markdown=3.4.3=pyhd8ed1ab_0
+  - markupsafe=2.1.2=py39ha55989b_0
+  - mkl=2022.1.0=h6a75c08_874
+  - moviepy=1.0.3=pyhd8ed1ab_1
+  - msys2-conda-epoch=20160418=1
+  - multidict=6.0.4=py39ha55989b_0
+  - oauthlib=3.2.2=pyhd8ed1ab_0
+  - openh264=2.3.1=h63175ca_2
+  - openjpeg=2.5.0=ha2aaf27_2
+  - openssl=1.1.1t=hcfcfb64_0
+  - opt_einsum=3.3.0=pyhd8ed1ab_1
+  - packaging=23.0=pyhd8ed1ab_0
+  - pandas=1.5.3=py39h2ba5b7c_1
+  - pillow=9.4.0=py39hcebd2be_1
+  - pip=23.0.1=pyhd8ed1ab_0
+  - platformdirs=3.2.0=pyhd8ed1ab_0
+  - pooch=1.7.0=pyha770c72_3
+  - proglog=0.1.9=py_0
+  - pthread-stubs=0.4=hcd874cb_1001
+  - pthreads-win32=2.9.1=hfa6e2cd_3
+  - pyasn1=0.4.8=py_0
+  - pyasn1-modules=0.2.7=py_0
+  - pycparser=2.21=pyhd8ed1ab_0
+  - pydantic=1.10.7=py39ha55989b_0
+  - pyjwt=2.6.0=pyhd8ed1ab_0
+  - pyopenssl=23.1.1=pyhd8ed1ab_0
+  - pysocks=1.7.1=pyh0701188_6
+  - pytesseract=0.3.10=pyhd8ed1ab_0
+  - python=3.9.16=h6244533_2
+  - python-dateutil=2.8.2=pyhd8ed1ab_0
+  - python-flatbuffers=23.1.21=pyhd8ed1ab_0
+  - python_abi=3.9=2_cp39
+  - pytz=2023.3=pyhd8ed1ab_0
+  - pyu2f=0.1.5=pyhd8ed1ab_0
+  - requests=2.28.2=pyhd8ed1ab_0
+  - requests-oauthlib=1.3.1=pyhd8ed1ab_0
+  - rsa=4.9=pyhd8ed1ab_0
+  - scipy=1.9.3=py39hfbf2dce_2
+  - setuptools=67.6.1=pyhd8ed1ab_0
+  - six=1.16.0=pyh6c4a22f_0
+  - snappy=1.1.10=hfb803bf_0
+  - sniffio=1.3.0=pyhd8ed1ab_0
+  - sqlite=3.41.1=h2bbff1b_0
+  - sqlite3=3.8.6=0
+  - starlette=0.26.1=pyhd8ed1ab_0
+  - svt-av1=1.4.1=h63175ca_0
+  - tbb=2021.8.0=h91493d7_0
+  - tensorboard=2.10.0=py39haa95532_0
+  - tensorboard-data-server=0.6.1=py39haa95532_0
+  - tensorboard-plugin-wit=1.8.1=pyhd8ed1ab_0
+  - tensorflow=2.10.0=mkl_py39ha510bab_0
+  - tensorflow-base=2.10.0=mkl_py39h6a7f48e_0
+  - tensorflow-estimator=2.10.0=py39haa95532_0
+  - termcolor=2.2.0=pyhd8ed1ab_0
+  - tk=8.6.12=h8ffe710_0
+  - tqdm=4.65.0=pyhd8ed1ab_1
+  - typing-extensions=4.5.0=hd8ed1ab_0
+  - typing_extensions=4.5.0=pyha770c72_0
+  - tzdata=2023c=h71feb2d_0
+  - ucrt=10.0.22621.0=h57928b3_0
+  - urllib3=1.26.15=pyhd8ed1ab_0
+  - uvicorn=0.21.1=py39hcbf5309_0
+  - vc=14.3=hb6edc58_10
+  - vs2015_runtime=14.34.31931=h4c5c07a_10
+  - werkzeug=2.2.3=pyhd8ed1ab_0
+  - wheel=0.40.0=pyhd8ed1ab_0
+  - win_inet_pton=1.1.0=pyhd8ed1ab_6
+  - wrapt=1.15.0=py39ha55989b_0
+  - x264=1!164.3095=h8ffe710_2
+  - x265=3.5=h2d74725_3
+  - xorg-libxau=1.0.9=hcd874cb_0
+  - xorg-libxdmcp=1.1.3=hcd874cb_0
+  - xz=5.2.6=h8d14728_0
+  - yarl=1.8.2=py39ha55989b_0
+  - zipp=3.15.0=pyhd8ed1ab_0
+  - zlib=1.2.13=hcfcfb64_4
+  - zstd=1.5.2=h12be248_6
+  - pip:
+      - alembic==1.10.2
+      - antlr4-python3-runtime==4.9.3
+      - asteroid-filterbanks==0.4.0
+      - audioread==3.0.0
+      - backports-cached-property==1.0.2
+      - blis==0.7.9
+      - catalogue==1.0.2
+      - chardet==5.1.0
+      - cmaes==0.9.1
+      - colorlog==6.7.0
+      - commonmark==0.9.1
+      - contourpy==1.0.7
+      - cycler==0.11.0
+      - cymem==2.0.7
+      - dlib==19.24.0
+      - docopt==0.6.2
+      - docx2txt==0.8
+      - einops==0.3.2
+      - en-core-web-sm==2.3.1
+      - fer==22.4.0
+      - filelock==3.10.7
+      - fonttools==4.39.3
+      - fsspec==2023.3.0
+      - gensim==3.8.1
+      - greenlet==2.0.2
+      - hmmlearn==0.2.8
+      - huggingface-hub==0.13.3
+      - hyperpyyaml==1.1.0
+      - importlib-resources==5.12.0
+      - imutils==0.5.4
+      - joblib==1.2.0
+      - jsonschema==4.17.3
+      - julius==0.2.7
+      - kiwisolver==1.4.4
+      - librosa==0.9.2
+      - llvmlite==0.39.1
+      - mako==1.2.4
+      - matplotlib==3.7.1
+      - mpmath==1.3.0
+      - mtcnn==0.1.1
+      - murmurhash==1.0.9
+      - networkx==2.8.8
+      - nltk==3.8.1
+      - numba==0.56.4
+      - numpy==1.23.5
+      - omegaconf==2.3.0
+      - opencv-contrib-python==4.7.0.72
+      - opencv-python==4.7.0.72
+      - optuna==3.1.0
+      - pdfminer-six==20221105
+      - plac==1.1.3
+      - plotly==5.14.0
+      - preshed==3.0.8
+      - primepy==1.3
+      - protobuf==3.19.6
+      - pyannote-audio==2.1.1
+      - pyannote-core==4.5
+      - pyannote-database==4.1.3
+      - pyannote-metrics==3.2.1
+      - pyannote-pipeline==2.3
+      - pyaudio==0.2.13
+      - pycryptodome==3.17
+      - pydeprecate==0.3.2
+      - pygments==2.14.0
+      - pyparsing==3.0.9
+      - pyresparser==1.0.6
+      - pyrsistent==0.19.3
+      - python-speech-features==0.6
+      - pytorch-lightning==1.6.5
+      - pytorch-metric-learning==1.7.3
+      - pyyaml==6.0
+      - regex==2023.3.23
+      - resampy==0.4.2
+      - rich==12.6.0
+      - ruamel-yaml==0.17.21
+      - ruamel-yaml-clib==0.2.7
+      - scikit-learn==1.2.2
+      - semver==2.13.0
+      - sentencepiece==0.1.97
+      - shellingham==1.5.0.post1
+      - simplejson==3.18.4
+      - singledispatchmethod==1.0
+      - smart-open==6.3.0
+      - sortedcontainers==2.4.0
+      - soundfile==0.10.3.post1
+      - spacy==2.3.9
+      - speechbrain==0.5.14
+      - sqlalchemy==2.0.7
+      - srsly==1.0.6
+      - sympy==1.11.1
+      - tabulate==0.9.0
+      - tenacity==8.2.2
+      - texthero==1.1.0
+      - thinc==7.4.6
+      - threadpoolctl==3.1.0
+      - torch==1.13.1
+      - torch-audiomentations==0.11.0
+      - torch-pitch-shift==1.2.3
+      - torchaudio==0.13.1
+      - torchmetrics==0.11.4
+      - typer==0.7.0
+      - unidecode==1.3.6
+      - wasabi==0.10.1
+      - wordcloud==1.8.2.2
+prefix: C:\Users\User\miniconda3\envs\pyserver