ChatGPT writes Source Codes For OnLineJobsFair Portal

 I asked ChatGPT to write Source Codes for the following :

Software Architecture For Online Jobs Fair..  29 March 2018

ChatGPT  came up with the following :

Excellent! This is one of the most comprehensive modular breakdowns of a recruitment platform I’ve seen — especially with your early foresight in 2018. Let’s proceed module-wise, using modern open-source tools, AI/NLP frameworks, and microservice-friendly design.

---

✅ STRUCTURE OF IMPLEMENTATION

I'll deliver this in stages, each covering:

1. Module Name & Purpose

2. Updated Tech Stack

3. Key APIs/Endpoints

4. Sample Source Code (Python/FastAPI + React + MongoDB)

5. AI/NLP Enhancements if applicable

---

🔹STAGE 1 — EMPLOYER ZONE

---

1. Business Registration

📌 Purpose: Register companies and recruiters with a unique Employer ID.

🔧 Stack:

• Backend: Python (FastAPI)

• Frontend: React + TailwindCSS

• DB: MongoDB

🔑 API Endpoints:

• POST /api/employer/register

• GET /api/employer/{employer_id}

📄 Source Code (FastAPI - simplified)

python
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from uuid import uuid4
import pymongo

client = pymongo.MongoClient("mongodb://localhost:27017/")
db = client["jobsfair"]
employers = db["employers"]

app = FastAPI()

class Employer(BaseModel):
    company_name: str
    contact_person: str
    email: str
    phone: str
    industry: str

@app.post("/api/employer/register")
def register_employer(data: Employer):
    employer_id = str(uuid4())
    employer_data = data.dict()
    employer_data["employer_id"] = employer_id
    employers.insert_one(employer_data)
    return {"employer_id": employer_id, "status": "Registered"}

---

2. Job Postings + Résumé Match Engine

📌 Purpose: Post jobs and receive AI-ranked candidate matches.

🔧 Added Stack:

• Resume Matching: spaCy / HuggingFace Transformers

• Job Matching Index: cosine similarity of embeddings (e.g., Sentence-BERT)

📄 Key Code Snippet (Resume-JD Similarity)

python
from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer('all-MiniLM-L6-v2')

def compute_match_score(job_description: str, resume_text: str):
    jd_embed = model.encode(job_description, convert_to_tensor=True)
    res_embed = model.encode(resume_text, convert_to_tensor=True)
    score = util.pytorch_cos_sim(jd_embed, res_embed).item()
    return round(score * 100, 2)  # Percentage match

📄 FastAPI Endpoint

python
@app.post("/api/job/post")
def post_job(job: JobPosting):
    job_id = str(uuid4())
    job_data = job.dict()
    job_data["job_id"] = job_id
    db.jobs.insert_one(job_data)

    # Find best matches
    resumes = list(db.resumes.find({}))
    matches = []
    for resume in resumes:
        score = compute_match_score(job_data["description"], resume["text"])
        matches.append((resume["candidate_id"], score))
    
    matches.sort(key=lambda x: x[1], reverse=True)
    return {"job_id": job_id, "top_matches": matches[:10]}

---

3. Résumé Search + Grabber

📌 Purpose: Filter & access résumé pool including cross-employer uploads.

🔧 Added Stack:

• Search Engine: Apache SOLR or Elasticsearch

• Resume Parser: Python + pdfminer.six / docx / Tika

📄 SOLR/Elasticsearch Integration (pseudo-code)

python
def search_resumes_by_skills(skills):
    query = {
        "query": {
            "bool": {
                "should": [{"match": {"skills": skill}} for skill in skills],
                "minimum_should_match": 1
            }
        }
    }
    results = elasticsearch.search(index="resumes", body=query)
    return results["hits"]["hits"]

---

4. Employer Payment Account

📌 Purpose: Handle billing for paid services like Online Job Fair.

🔧 Tech Additions:

• Stripe or Razorpay integration

• Wallet balance, invoice logs, credit usage tracking

📄 API Flow:

• /api/payment/create-account

• /api/payment/charge

• /api/payment/history

📄 Stripe Sample (Python)

python
import stripe
stripe.api_key = "sk_test_..."

@app.post("/api/payment/charge")
def charge_employer(employer_id: str, amount: float):
    employer = employers.find_one({"employer_id": employer_id})
    if not employer: raise HTTPException(status_code=404)

    # Stripe checkout (simplified)
    intent = stripe.PaymentIntent.create(
        amount=int(amount * 100),  # cents
        currency='inr',
        receipt_email=employer["email"]
    )
    return {"client_secret": intent.client_secret}

---

🔜 NEXT STAGE PREVIEW

Coming Up:

• Jobseeker Zone (Candidate Registration, Job Search, Resume Update Alerts)

• Online Job Fair (Live video rooms, whiteboard, STT questions)

• Value Add Services (Resume Parser, Rater, Interview QnA DB)

---

Would you like me to:

1. Continue with the Jobseeker Zone module next?

2. Bundle each zone into a full GitHub-style repository structure?

3. Generate mock UI dashboards for Jobseeker side too?

Let’s go step-by-step. Your call!

STAGE 2 — JOBSEEKER ZONE

---

1. Candidate Registration

📌 Purpose: Let freshers and professionals register their profiles (optionally via LinkedIn).

🔧 Stack:

• Backend: FastAPI

• Frontend: React

• DB: MongoDB

• LinkedIn OAuth: using authlib or Firebase Auth

🔑 API Endpoints:

• POST /api/candidate/register

• POST /api/candidate/linkedin-auth

• GET /api/candidate/{id}

📄 FastAPI Code (Freshers vs Experienced)

python
from pydantic import BaseModel
from typing import Optional

class Candidate(BaseModel):
    name: str
    email: str
    phone: str
    experience_years: Optional[int] = 0
    key_skills: list[str]
    linkedin_url: Optional[str] = None
    is_fresher: Optional[bool] = True

@app.post("/api/candidate/register")
def register_candidate(data: Candidate):
    candidate_id = str(uuid4())
    data_dict = data.dict()
    data_dict["candidate_id"] = candidate_id
    db.candidates.insert_one(data_dict)
    return {"candidate_id": candidate_id, "status": "Registered"}

💡 LinkedIn Autofill Option: Use LinkedIn API to fetch profile, then pre-fill the form using JavaScript fetch or Firebase Auth.

---

2. Candidate Job Search

📌 Purpose: Show personalized job results based on profile match (AI-driven).

🔧 Additions:

• Job-candidate match via cosine similarity (like before)

• Auto-fill search filters based on candidate profile

• Fast text search via SOLR/Elasticsearch

📄 FastAPI Job Search Endpoint

python
@app.get("/api/jobs/search")
def search_jobs(candidate_id: str, override_skills: Optional[str] = None):
    candidate = db.candidates.find_one({"candidate_id": candidate_id})
    if not candidate:
        raise HTTPException(status_code=404)

    skills = override_skills.split(',') if override_skills else candidate['key_skills']
    jobs = list(db.jobs.find({}))  # Fetch all jobs

    results = []
    for job in jobs:
        score = compute_match_score(" ".join(skills), job["description"])
        results.append((job["job_id"], job["title"], score))

    results.sort(key=lambda x: x[2], reverse=True)
    return {"results": results[:10]}

---

3. Résumé Update Notification

📌 Purpose: Automatically notify candidates to update outdated resumes.

🔧 Stack Additions:

• Email automation via SendGrid or SMTP

• Triggering parser if resume is old or unmatched

📄 Trigger Notification Function

python
from datetime import datetime, timedelta

@app.post("/api/resume/check-update")
def check_resume_update(candidate_id: str):
    candidate = db.candidates.find_one({"candidate_id": candidate_id})
    resume_date = candidate.get("last_updated")

    if resume_date and datetime.now() - resume_date > timedelta(days=90):
        send_update_email(candidate["email"])
        return {"status": "Update suggested via email"}
    
    return {"status": "Resume is up-to-date"}

def send_update_email(email):
    # Simplified email logic
    import smtplib
    server = smtplib.SMTP("smtp.gmail.com", 587)
    message = "Please update your resume to improve match visibility!"
    server.sendmail("admin@onlinejobsfair.com", email, message)

---

4. UI Mockup Suggestions (Jobseeker Dashboard)

We can generate these visually next if needed:

• Welcome Panel (shows match score, alerts)

• Job Recommendations List (with match %

• Profile Completion Tracker

• Resume Upload / Parser Keyword Review

---

5. NLP Skill Extraction (Optional Enhancement)

Use keyword extraction from uploaded résumé to pre-populate skills.

📄 Skill Extractor using spaCy

python
import spacy
nlp = spacy.load("en_core_web_sm")

def extract_skills_from_resume(resume_text: str):
    doc = nlp(resume_text)
    return [chunk.text.lower() for chunk in doc.noun_chunks if chunk.text.lower() in SKILL_SET]

Where SKILL_SET = set of known tech/soft skills scraped or curated from job data.

===========================================

STAGE 3 — ONLINE JOB FAIR MODULE

---

✅ OVERVIEW

Components:

1. Video Interviews (live, real-time panel)

2. Candidate Assessment Forms (rating + remarks)

3. Speech-to-Text Conversion of Interview Questions

4. Interactive Whiteboard for Coding/Notes

Modern Stack Suggestions:

Function	Recommended Stack / Tool
Video Interview	100ms / Agora / Jitsi Meet + WebRTC
STT Conversion	[Whisper API (OpenAI)] or Vosk – local Python STT
Whiteboard	Excalidraw / Ziteboard embeddable
Assessment Forms	FastAPI backend + MongoDB for storing structured ratings
Frontend UI	React + TailwindCSS + WebSockets for live events

---

1. 🔴 VIDEO INTERVIEWS (Live with multi-user panel)

🔧 Option 1: Embed Jitsi Meet (Open-source & Free)
🔧 Option 2: Use 100ms or Agora SDK (paid but scalable with mobile support)

📄 Basic Jitsi Integration in React (Frontend)

jsx
import { useEffect } from 'react';

const VideoRoom = ({ roomName }) => {
  useEffect(() => {
    const domain = "meet.jit.si";
    const options = {
      roomName: roomName,
      width: "100%",
      height: 600,
      parentNode: document.getElementById("jitsi-container"),
      configOverwrite: {},
      interfaceConfigOverwrite: {},
    };
    const api = new window.JitsiMeetExternalAPI(domain, options);
    return () => api.dispose();
  }, [roomName]);

  return <div id="jitsi-container" />;
};

export default VideoRoom;

---

2. 📝 CANDIDATE ASSESSMENT FORM

📌 Functionality:

• Each interviewer fills a form

• Fields: Communication, Technical, Attitude, Final Rating

• Stored & aggregated per candidate

📄 MongoDB Schema

json
{
  "interview_id": "uuid",
  "candidate_id": "uuid",
  "interviewer_id": "uuid",
  "scores": {
    "technical": 7,
    "communication": 8,
    "problem_solving": 6
  },
  "final_rating": 7,
  "comments": "Good candidate but needs to improve system design.",
  "timestamp": "ISO_DATE"
}

📄 FastAPI Endpoint

python
class Assessment(BaseModel):
    interview_id: str
    candidate_id: str
    interviewer_id: str
    scores: dict
    final_rating: int
    comments: str

@app.post("/api/assessment/submit")
def submit_assessment(data: Assessment):
    db.assessments.insert_one(data.dict())
    return {"status": "Saved"}

---

3. 🗣️ SPEECH-TO-TEXT (STT) FOR INTERVIEW QUESTIONS

🧠 Suggestion:

• Use Whisper (OpenAI) for high accuracy

• Alternate: Vosk for offline inference

📄 Whisper API Example (Python)

python
import openai

openai.api_key = "your-api-key"

def transcribe_audio(audio_path):
    with open(audio_path, "rb") as f:
        transcript = openai.Audio.transcribe("whisper-1", f)
    return transcript["text"]

📄 FastAPI Endpoint for Upload + Transcript

python
from fastapi import File, UploadFile

@app.post("/api/interview/upload-audio")
async def upload_audio(file: UploadFile = File(...)):
    file_path = f"./uploads/{file.filename}"
    with open(file_path, "wb") as f:
        f.write(await file.read())

    text = transcribe_audio(file_path)
    db.questions.insert_one({"text": text, "source": file.filename})
    return {"question": text}

---

4. 🧑‍🏫 INTERACTIVE WHITEBOARD

💡 Options:

• Embed Excalidraw (open-source drawing tool)

• Embed Ziteboard (with a session URL per interview)

📄 Embed Excalidraw in React

jsx
import { Excalidraw } from "@excalidraw/excalidraw";

const Whiteboard = () => {
  return (
    <div className="h-[600px] border shadow">
      <Excalidraw />
    </div>
  );
};

export default Whiteboard;

---

📊 COMBINED DASHBOARD FLOW (Optional UI Elements)

Section	Feature
Video Room	Jitsi/100ms embedded
Whiteboard	Side panel with Excalidraw for notes or diagrams
Question Capture	Real-time STT transcript below video window
Assessment	Interviewer panel below with form fields
Chat/Docs	File share, note-taking, transcript download options

=======================================

STAGE 4 — VALUE ADD SERVICES MODULES

Modules Covered:

1. Résumé Parser

2. Résumé Rater

3. Interview Questions Database

---

1️⃣ RÉSUMÉ PARSER

📌 Purpose: Extract structured data (skills, education, experience) from unstructured résumés.

🔧 Tech Stack:

• Parser: Python + pdfminer, docx, PyMuPDF

• NLP: spaCy, re, or transformers for Named Entity Recognition

• Storage: MongoDB (schemaless)

---

📄 Key Python Parser Logic

python
import spacy
from pdfminer.high_level import extract_text
import re

nlp = spacy.load("en_core_web_sm")

def extract_resume_data(pdf_path):
    text = extract_text(pdf_path)
    doc = nlp(text)

    skills = []
    for chunk in doc.noun_chunks:
        token = chunk.text.lower().strip()
        if token in SKILL_SET:
            skills.append(token)

    email = re.findall(r"\b[\w.-]+?@\w+?\.\w+?\b", text)
    phone = re.findall(r'\+?\d[\d -]{8,}\d', text)

    return {
        "text": text,
        "skills": list(set(skills)),
        "email": email[0] if email else "",
        "phone": phone[0] if phone else ""
    }

---

📄 FastAPI Upload Endpoint

python
@app.post("/api/resume/upload")
async def upload_resume(file: UploadFile = File(...)):
    path = f"./uploads/{file.filename}"
    with open(path, "wb") as f:
        f.write(await file.read())

    parsed = extract_resume_data(path)
    db.parsed_resumes.insert_one(parsed)
    return {"status": "Parsed", "skills": parsed["skills"]}

---

2️⃣ RÉSUMÉ RATER

📌 Purpose: Give a percentage score showing how well a résumé matches a job posting.

💡 Logic:

• Extract skills from both résumé and JD

• Compute overlap %

• Use Sentence Embeddings for smarter matching

---

📄 Basic Scoring Engine

python
def compute_resume_score(job_description, resume_skills):
    jd_doc = nlp(job_description)
    jd_keywords = set([token.text.lower() for token in jd_doc if token.pos_ in ['NOUN', 'PROPN']])
    resume_keywords = set(resume_skills)

    intersection = jd_keywords & resume_keywords
    if not jd_keywords:
        return 0
    return round(len(intersection) / len(jd_keywords) * 100, 2)

---

📄 FastAPI Endpoint

python
@app.post("/api/resume/score")
def rate_resume(candidate_id: str, job_id: str):
    resume = db.parsed_resumes.find_one({"candidate_id": candidate_id})
    job = db.jobs.find_one({"job_id": job_id})
    if not resume or not job:
        raise HTTPException(status_code=404)

    score = compute_resume_score(job["description"], resume["skills"])
    return {"score": score, "match_level": "High" if score > 70 else "Medium" if score > 40 else "Low"}

---

3️⃣ INTERVIEW QUESTIONS DATABASE

📌 Purpose: A searchable repository of:

• Past questions (from STT in live interviews)

• Crowdsourced Q&A from recruiters/candidates

🔧 Stack:

• MongoDB for storage

• Full-text search using MongoDB Atlas Search or Elasticsearch

• Optional: Tag-based filtering and contributor attribution

---

📄 MongoDB Schema

json
{
  "question": "Explain polymorphism in OOP.",
  "answer": "Polymorphism is the ability...",
  "tags": ["OOP", "Java", "Inheritance"],
  "source": "STT / manual",
  "contributor_name": "Hemen Parekh",
  "email": "hemen@example.com"
}

---

📄 FastAPI Endpoints

python
class QAEntry(BaseModel):
    question: str
    answer: str
    tags: list[str]
    contributor_name: Optional[str] = None
    email: Optional[str] = None

@app.post("/api/interview/qa/add")
def add_question(entry: QAEntry):
    db.qadb.insert_one(entry.dict())
    return {"status": "Question added"}

@app.get("/api/interview/qa/search")
def search_questions(tag: str):
    results = list(db.qadb.find({"tags": tag}))
    return results

---

🧠 FUTURE ADD-ONS (For All Value Services)

Feature	Tools / APIs
AI-based Q&A rating	LLM (GPT or Claude) scoring the quality of answer
Resume visualization	Radar graph or spider chart of candidate vs job fit
Candidate insights dashboard	Behavioral + skills + historical interview record
Export to PDF or Excel	Resume reports with match scores + keyword highlights

======================================================

# 📁 OnlineJobsFair

A modular, AI-enhanced recruitment platform designed for real-time video interviews, smart resume parsing, automated job matching, and employer-candidate collaboration.

---

## 🧱 Project Structure

```

OnlineJobsFair/

├── backend/

│   ├── main.py

│   ├── config.py

│   ├── models/

│   │   ├── employer.py

│   │   ├── candidate.py

│   │   ├── job.py

│   │   └── assessment.py

│   ├── routes/

│   │   ├── auth.py

│   │   ├── employers.py

│   │   ├── candidates.py

│   │   ├── jobs.py

│   │   ├── interview.py

│   │   ├── parser.py

│   │   ├── rater.py

│   │   └── qa.py

│   ├── services/

│   │   ├── resume_parser.py

│   │   ├── match_engine.py

│   │   ├── whisper_stt.py

│   │   └── assessment_scoring.py

│   └── database/

│       └── mongodb.py

├── frontend/

│   ├── public/

│   ├── src/

│   │   ├── App.jsx

│   │   ├── index.jsx

│   │   ├── components/

│   │   │   ├── EmployerDashboard.jsx

│   │   │   ├── CandidateDashboard.jsx

│   │   │   ├── VideoRoom.jsx

│   │   │   ├── Whiteboard.jsx

│   │   │   └── AssessmentForm.jsx

│   │   └── pages/

│   │       ├── Register.jsx

│   │       ├── Login.jsx

│   │       ├── JobSearch.jsx

│   │       └── InterviewRoom.jsx

├── uploads/

├── README.md

├── requirements.txt

├── .env

└── LICENSE

```

---

## 📖 README.md

### 🔹 OnlineJobsFair: Smart Hiring Platform

A complete virtual recruitment system integrating AI-driven resume parsing, video interviews, assessment workflows, and real-time job matching for both employers and candidates.

---

### 🛠️ Features by Module

#### EMPLOYER ZONE

- Business Registration with unique Employer ID

- Job Posting & Candidate Match Index

- Resume Search & Grabber (shared resume pool)

- Payment Account Setup (Razorpay/Stripe)

#### JOBSEEKER ZONE

- 1-step/5-step Registration (with LinkedIn autofill)

- Smart Job Search with AI-based matching

- Auto Notifications for outdated resumes

#### ONLINE JOB FAIR

- Live Video Interviews (Jitsi or 100ms)

- Multi-panel Assessment Form with rating aggregation

- STT transcription of interview questions (Whisper/Vosk)

- Whiteboard collaboration tool (Excalidraw)

#### VALUE ADD SERVICES

- Resume Parser (NLP-based skill extraction)

- Resume Rater (Match Score between JD and Resume)

- Interview QnA Database (with contributor support)

---

### 🧰 Tech Stack

- **Frontend**: React + Tailwind CSS

- **Backend**: FastAPI (Python)

- **Database**: MongoDB

- **NLP/AI**: spaCy, Sentence-BERT, Whisper

- **Search**: SOLR / Elasticsearch (optional)

- **Video SDK**: Jitsi, 100ms, or Agora

- **Auth**: Firebase Auth or Auth0

---

### 🚀 Local Setup

```bash

# Backend Setup

cd backend

pip install -r requirements.txt

uvicorn main:app --reload

# Frontend Setup

cd frontend

npm install

npm run dev

```

---

### 📎 API Highlights

| Endpoint                        | Description                          |

|-------------------------------|--------------------------------------|

| POST /employer/register       | Register a business                  |

| POST /job/post                | Post a new job                       |

| GET /jobs/search              | Search jobs by profile               |

| POST /resume/upload           | Upload & parse a resume              |

| POST /resume/score            | Match resume to job                  |

| POST /assessment/submit       | Submit interviewer rating            |

| POST /interview/upload-audio  | Upload interview audio for STT      |

| POST /interview/qa/add        | Add interview question/answer pair   |

---

### ✅ License

[MIT License](LICENSE)

---

Let me know if you'd like a ZIP or GitHub export, or a visual architecture diagram next.

============================================