GenJob — AI-assisted Job & Resume Toolkit

This repository contains two main components: an ai-apis microservice that provides AI-powered resume generation, salary prediction, and job analysis utilities; and GenJobMVC, an ASP.NET MVC web application that integrates those AI services into a web UI for employers and job seekers.

🔗 Quick Navigation

Section	Description
📁 Contents	Project structure overview
✨ Features	Key capabilities and functionality
🛠️ Tech Stack	Technologies and frameworks used
📂 Folder Structure	Detailed directory organization
🔄 Architecture Flow	System architecture diagram
🔌 API Endpoints	Available API endpoints and responses
🌐 External APIs	Third-party service integrations
🚀 Installation & Setup	Prerequisites and running instructions
👥 Contributors	Team members and roles
📝 Notes	Additional information and assumptions

Contents

• ai-apis/ — Python FastAPI microservice providing AI helpers (resume LaTeX generation, salary prediction, job analysis, PDF conversion helper).
• GenJobMVC/ — ASP.NET MVC web app (C#) that uses the microservice and provides UI pages (Dashboard, ATS, Resume generation, Salary prediction).

Project features

• AI-powered Resume Generation: Generate professional LaTeX-based resumes from structured candidate data and job descriptions using AI models (oss-120b).
• PDF Conversion: Convert generated LaTeX to downloadable PDF URLs via latexonline.cc.
• ATS Score Analysis: Extract text from PDF/DOC resumes using iTextSharp/OpenXML and calculate ATS compatibility scores.
• Salary Prediction: ML-based salary prediction for job roles by company, location, and employment status with Redis caching.
• Job Market Analytics: Generate graph data and visualizations for job market trends and analysis.
• User Authentication: Secure login/signup system using ASP.NET Identity with MySQL database.
• Job Search Integration: Search job postings by role, location, and company via Glassdoor API integration.
• Data Caching: Redis-based caching for improved performance and data storage.
• Resume Parsing: Extract and process resume content from various document formats.

Tech stack

Libaries/Frameworks

• Frontend: HTML, Tailwind-CSS, JavaScript, Razor views
• Backend: C#, .net core and it's packages
• AI/ML : python, fastapis

ai-apis (Python Backend):

• Python 3.10+
• FastAPI for REST API endpoints
• Pydantic for request/response validation
• Redis for caching and data storage
• scikit-learn for ML model inference
• Pre-trained ML models (salary prediction)
• oss-120b model for AI-powered content generation
• latexonline.cc for LaTeX → PDF compilation

GenJobMVC (C# Web Application):

• .NET 8.0 (ASP.NET Core MVC)
• Entity Framework Core for ORM and migrations
• MySQL database for persistent data storage
• ASP.NET Identity for authentication and user management
• iTextSharp for PDF text extraction
• OpenXML for document processing
• Redis.OM for Redis integration in .NET
• Razor views for server-side rendering
• Glassdoor API integration for job search

Key .NET Packages:

• Microsoft.EntityFrameworkCore
• Pomelo.EntityFrameworkCore.MySql
• Microsoft.AspNetCore.Identity.EntityFrameworkCore
• Redis.OM
• iTextSharp
• DocumentFormat.OpenXml

Folder structure (short)

ai-apis/

• .env — environment variables for the Python service
• api.py — FastAPI application and endpoints
• helpers/ — Python helper modules
  • gen_latex.py — LaTeX generation logic
  • job_analisys.py — functions to prepare graph/analytics data
  • salary_prediciton.py — salary prediction helpers (loads pre_trained models)
• data/ — CSV datasets used for training/analysis
• pre_trained/ — stored ML models and scalers (pickle files)
• notebooks/ — Jupyter notebooks for analysis and experimentation
• scripts/ — utility scripts (e.g. salary_model.py)

GenJobMVC/

• GenJobMVC.sln — Visual Studio solution
• GenJobMVC/Program.cs — app entry
• Controllers/ — MVC controllers (AccountController, ATSController, DashboardController, JobPostingController, etc.)
• Services/ — service classes (ATSScoringService, JobPostingService, ResumeParserService)
• Data/ — AppDbContext.cs (EF Core DB context)
• Models/ — C# model classes (JobPosting, CompanyPosting, AI_API, etc.)
• Views/ — Razor views for pages (Dashboard, ATS, Account, JobPosting)
• wwwroot/ — static assets (css, js, lib)

High-level flow

The following diagram shows the complete architecture and data flow between all system components including the web UI, ASP.NET MVC backend, FastAPI, databases, and external APIs.

ai-apis API endpoints and example return objects

Below are the primary endpoints exposed by ai-apis/api.py with example request/response shapes (inferred from code).

1. POST /json

• Purpose: Converts a payload containing a JSON string into an object.
• Request body: { "data": "" }
• Response: Parsed JSON object (dynamic)

Example Request body: { "data": "{"foo": "bar"}" }

Response: { "foo": "bar" }

2. POST /generate_resume

• Purpose: Generate LaTeX-based resume content and return a PDF URL.
• Request body shape: ResumeGenRequest
  • links: { name: url, ... }
  • candidate: { name, email, phone, linkedin, github, education: [{degree, university, year, cgpa?}], skills: [...], projects: [...], experience: [...], additional?: [...] }
  • job_description: { title, company, location?, requirements: [...] }
• Response: { "pdf_url": "https://latexonline.cc/compile?text=..." } or { "error": "Failed to generate LaTeX content" }

Example response object: { "pdf_url": "https://latexonline.cc/compile?text=" }

3. POST /string-process

• Purpose: Accepts raw LaTeX text and writes it to resume.tex, returning cleaned text.
• Request body: { "data": "" }
• Response: the cleaned LaTeX text (string)

4. GET /get-company-names

• Purpose: Return a list of company names used by the salary model/helpers.
• Response: { "company_list": ["Company A", "Company B", ...] }

5. POST /get-other-data

• Purpose: Return auxiliary data for a given company name.
• Request body: { "company_name": "ACME" }
• Response: { "data": } — shape depends on getOtherData implementation in helpers/salary_prediciton.py.
  6. POST /predict-salary
  • Purpose: Predict salary given company, job role, location, and employment status.
  • Request body: { "company_name": "ACME", "job_role": "Data Scientist", "location": "NYC", "status": "Full-time" }
  • Response: { "predicted_salary": 85000 } (value returned from getSalaryPrediction)
  7. GET /get-graph-data
  • Purpose: Return job analysis graph data for UI visualizations.
  • Response: object returned by getGraphData() — likely a serializable dict with nodes/edges or chart data.
  8. POST /test
  • Purpose: Echo the ResumeGenRequest payload for testing/validation.
  • Request body: ResumeGenRequest
  • Response: { "candidate": , "job": <job_description>, "links": }

  External API integrations

  Glassdoor API (via GenJobMVC):

  • Search jobs by role and location
  • Search jobs by company name
  • Retrieve job posting data including salary ranges, company info, and requirements

  oss-120b AI Model:

  • Used for intelligent resume generation and content optimization
  • Integrated through the ai-apis service

  latexonline.cc API:

  • Converts LaTeX markup to downloadable PDF URLs
  • No authentication required, public service

  How to run

  Prerequisites

  Install Redis Stack:

  # Windows (using Chocolatey)
  choco install redis-stack-server
  
  # Or download from: https://redis.io/docs/install/install-stack/windows/
  # Start Redis Stack: redis-stack-server

  Install MySQL:

  # Windows (using Chocolatey)
  choco install mysql
  
  # Or download MySQL Community Server from: https://dev.mysql.com/downloads/mysql/
  # Create database: CREATE DATABASE genjob_db;

  ai-apis (Python Backend)

  cd "d:\C-drive mini\GenJob\ai-apis"
  python -m venv .venv
  .\.venv\Scripts\Activate.ps1
  pip install -r requirements.txt
  uvicorn api:app --reload --port 8000

  GenJobMVC (ASP.NET)

  Required NuGet Packages:

  cd "d:\C-drive mini\GenJob\GenJobMVC\GenJobMVC"
  dotnet add package Microsoft.EntityFrameworkCore
  dotnet add package Pomelo.EntityFrameworkCore.MySql
  dotnet add package Microsoft.AspNetCore.Identity.EntityFrameworkCore
  dotnet add package Redis.OM
  dotnet add package iTextSharp
  dotnet add package DocumentFormat.OpenXml

  Run the application:

  # Option 1: Visual Studio
  # Open GenJobMVC/GenJobMVC.sln in Visual Studio and run
  
  # Option 2: Command line
  cd "d:\C-drive mini\GenJob\GenJobMVC\GenJobMVC"
  dotnet ef database update
  dotnet run

  Individual Contributors

  • Devan Chauhan (https://github.com/Devan019)
    • .net REST Apis
    • ML prediction model/data analysis
    • Redis caching
    • GenAI integration
    • LaTeX template design
    • UI/UX design
  • Moksh Desai (https://github.com/MokshDesai)
    • External package Resume parsing (iTextSharp/OpenXML)
    • MVC controllers and views
    • SharpAPI for ATS Score calculation
  • Nishant Dholakia (https://github.com/Nishant-Dholakia)
    • Database (MySQL) Design
    • Glassdoor API
    • Identity framework
    • EF Core integration

  Notes and assumptions

  • The README API docs are derived from ai-apis/api.py and helper names; exact response structures from helpers (e.g., getOtherData, getGraphData) depend on their implementations under ai-apis/helpers.
  • The project uses latexonline.cc for LaTeX compilation by building a URL with the encoded LaTeX content. For production usage consider a private LaTeX build service or a server-side container to compile PDFs more securely.