Update README.md

Author: Sarfarazsfz

README.md

@@ -1,83 +1,67 @@
-JobMatch AI – AI-Powered Job Tracker with Smart Matching
+# JobMatch AI – AI-Powered Job Tracker with Smart Matching
 
 This project was built as part of an internship assignment and demonstrates end-to-end full-stack development, clean UI/UX design, and practical AI integration.
 
-An intelligent job tracking system that fetches jobs from external APIs, matches them with a user’s resume using AI, provides smart application tracking, and includes a conversational AI assistant to help users navigate their job search efficiently.
+An intelligent job tracking system that fetches jobs from external APIs, matches them with a user's resume using AI, provides smart application tracking, and includes a conversational AI assistant to help users navigate their job search efficiently.
 
-🔗 Live Demo
+---
 
-Frontend: https://smart-job-tracker-ochre.vercel.app
+## 🔗 Live Demo
 
-Backend API: https://smart-job-tracker-backend-r5wd.onrender.com
+- **Frontend**: https://smart-job-tracker-ochre.vercel.app
+- **Backend API**: https://smart-job-tracker-backend-r5wd.onrender.com
+- **GitHub Repository**: https://github.com/Sarfarazsfz/smart-job-tracker
 
-GitHub Repository: https://github.com/Sarfarazsfz/smart-job-tracker
+**Demo Note**: Upload a resume to enable AI-based job matching and personalized match scores.
 
-Demo Note: Upload a resume to enable AI-based job matching and personalized match scores.
+---
 
-✨ Key Features
-Core Functionality
+## ✨ Key Features
 
-Job feed powered by an external job API (India-focused listings)
+### Core Functionality
+- Job feed powered by an external job API (India-focused listings)
+- Resume upload (PDF/TXT) with automatic text extraction
+- AI-based job–resume matching with percentage scores
+- **Best Matches for You** section highlighting top matches
+- Smart application tracking with confirmation popup
+- Conversational AI assistant for job discovery and guidance
+- Fully responsive UI (mobile, tablet, desktop)
 
-Resume upload (PDF/TXT) with automatic text extraction
+### Filters (Assignment Requirement)
+- **Role / Job Title** search
+- **Skills** (multi-select: React, Node.js, Python, etc.)
+- **Date Posted** (Last 24 hours, Last week, Last month, Any time)
+- **Job Type** (Full-time, Part-time, Contract, Internship)
+- **Work Mode** (Remote, Hybrid, On-site)
+- **Location** (City / Region)
+- **Match Score** (High >70%, Medium 40–70%, All)
 
-AI-based job–resume matching with percentage scores
+---
 
-Best Matches for You section highlighting top matches
-
-Smart application tracking with confirmation popup
-
-Conversational AI assistant for job discovery and guidance
-
-Fully responsive UI (mobile, tablet, desktop)
-
-Filters (Assignment Requirement)
-
-Role / Job Title search
-
-Skills (multi-select: React, Node.js, Python, etc.)
-
-Date Posted (Last 24 hours, Last week, Last month, Any time)
-
-Job Type (Full-time, Part-time, Contract, Internship)
-
-Work Mode (Remote, Hybrid, On-site)
-
-Location (City / Region)
-
-Match Score (High >70%, Medium 40–70%, All)
-
-🏗️ Project Architecture
+## 🏗️ Project Architecture
 
 The application follows a clean three-tier architecture:
 
-1. Presentation Layer (Frontend)
-
-React + Vite
-
-Client-side filtering and ranking for instant UX
-
-State managed with React hooks
+### 1. Presentation Layer (Frontend)
+- React + Vite
+- Client-side filtering and ranking for instant UX
+- State managed with React hooks
+- Dark-mode focused, professional UI
 
-Dark-mode focused, professional UI
+### 2. Application Layer (Backend)
+- Node.js + Fastify
+- REST APIs
+- Job fetching, resume processing, AI orchestration
+- Cache-first design for performance
 
-2. Application Layer (Backend)
+### 3. Data & External Services
+- Adzuna API for job listings
+- Google Gemini for AI matching (with fallback logic)
+- Upstash Redis (or in-memory fallback) for caching
 
-Node.js + Fastify REST APIs
+### Architecture Diagram
 
-Job fetching, resume processing, AI orchestration
-
-Cache-first design for performance
-
-3. Data & External Services
-
-Adzuna API for job listings
-
-Google Gemini for AI matching (with fallback logic)
-
-Upstash Redis (or in-memory fallback) for caching
-
-Architecture Diagram
+```mermaid
 graph TB
     subgraph Frontend
         UI[Job Feed]
@@ -86,140 +70,123 @@ graph TB
         Chat[AI Assistant]
         Tracker[Application Tracker]
     end
-
+    
     subgraph Backend
         API[Fastify API]
         JobService[Job Service]
         AIService[AI Matching]
         ResumeService[Resume Service]
     end
-
+    
     subgraph External
         JobAPI[Adzuna API]
         AI[Google Gemini]
         Cache[(Redis / Memory)]
     end
-
+    
     UI --> API
     Filters --> API
     Resume --> API
     Chat --> API
     Tracker --> API
-
+    
     API --> JobService
     API --> AIService
     API --> ResumeService
-
+    
     JobService --> JobAPI
     JobService --> Cache
     AIService --> AI
+```
 
-🤖 AI Matching Logic
-Overview
-
-The system calculates a job–resume match score (0–100%) using a weighted approach:
-
-Skill Match: 45%
+---
 
-Experience Level: 30%
+## 🤖 AI Matching Logic
 
-Title Relevance: 25%
-
-How It Works
-
-Extract skills and keywords from resume and job description
-
-Calculate skill overlap
-
-Detect experience level alignment
-
-Match job title relevance
-
-Combine weighted scores into a final percentage
-
-Match Categories
-
-High (70–100%) – Strong match
-
-Medium (40–69%) – Moderate match
-
-Low (<40%) – Weak match
+### Overview
+The system calculates a job–resume match score (0–100%) using a weighted approach:
+- **Skill Match**: 45%
+- **Experience Level**: 30%
+- **Title Relevance**: 25%
+
+### How It Works
+1. Extract skills and keywords from resume and job description
+2. Calculate skill overlap
+3. Detect experience level alignment
+4. Match job title relevance
+5. Combine weighted scores into a final percentage
+
+### Match Categories
+- **High (70–100%)** – Strong match
+- **Medium (40–69%)** – Moderate match
+- **Low (<40%)** – Weak match
 
 Match scores are calculated once after resume upload and reused across filters.
 
-🎯 Smart Application Popup (Critical Thinking)
+---
 
-Problem: Applications happen on external job portals (LinkedIn, company sites).
-Constraint: Browser security prevents tracking actions on external websites.
+## 🎯 Smart Application Popup (Critical Thinking)
 
-Solution:
-When a user returns to the app tab after clicking Apply, a popup asks:
+**Problem**: Applications happen on external job portals (LinkedIn, company sites).
 
-Yes, Applied
+**Constraint**: Browser security prevents tracking actions on external websites.
 
-Applied Earlier
-
-No, Just Browsing
+**Solution**: When a user returns to the app tab after clicking Apply, a popup asks:
+- Yes, Applied
+- Applied Earlier
+- No, Just Browsing
 
 This approach ensures accurate application tracking without violating user privacy.
 
-⚡ Caching & Performance
-
-Jobs fetched in batches of 50
-
-Cache-first strategy with 1-hour TTL
+---
 
-Jobs fetched only when cache expires
+## ⚡ Caching & Performance
 
-Filters and search applied client-side
+- Jobs fetched in batches of 50
+- Cache-first strategy with 1-hour TTL
+- Jobs fetched only when cache expires
+- Filters and search applied client-side
+- Reduces API usage while keeping listings fresh
 
-Reduces API usage while keeping listings fresh
+---
 
-📈 Scalability Considerations
+## 📈 Scalability Considerations
 
-Handles 100 jobs efficiently using parallel processing
+- Handles 100 jobs efficiently using parallel processing
+- Architecture supports scaling to 10,000 users
+- Stateless backend design
+- Redis used for fast access and caching
+- Clear migration path to PostgreSQL + background job queues
 
-Architecture supports scaling to 10,000 users
+---
 
-Stateless backend design
+## ⚖️ Tradeoffs & Limitations
 
-Redis used for fast access and caching
+### Current Limitations
+- No authentication (single-user demo model)
+- Redis / in-memory storage (not a permanent database)
+- AI scoring latency depends on external APIs
+- Single job data source
 
-Clear migration path to PostgreSQL + background job queues
+### Future Improvements
+- User authentication (OAuth)
+- Persistent database (PostgreSQL)
+- Background job queue for AI scoring
+- Multi-source job aggregation
+- Email alerts and saved searches
 
-⚖️ Tradeoffs & Limitations
-Current Limitations
+---
 
-No authentication (single-user demo model)
+## 🛠️ Setup Instructions
 
-Redis / in-memory storage (not a permanent database)
+### Prerequisites
+- Node.js 18+
+- npm or yarn
+- Optional API keys (Adzuna, Gemini, Redis)
 
-AI scoring latency depends on external APIs
+### Run Locally
 
-Single job data source
-
-Future Improvements
-
-User authentication (OAuth)
-
-Persistent database (PostgreSQL)
-
-Background job queue for AI scoring
-
-Multi-source job aggregation
-
-Email alerts and saved searches
-
-🛠️ Setup Instructions
-Prerequisites
-
-Node.js 18+
-
-npm or yarn
-
-Optional API keys (Adzuna, Gemini, Redis)
-
-Run Locally
+```bash
 git clone https://github.com/Sarfarazsfz/smart-job-tracker.git
 cd smart-job-tracker
 
@@ -233,28 +200,27 @@ npm run dev
 cd ../frontend
 npm install
 npm run dev
+```
 
+**Open**: http://localhost:5173
 
-Open: http://localhost:5173
+---
 
-📚 Documentation
+## 📚 Documentation
 
-Detailed references are available in the /docs folder:
+Detailed references are available in the `/docs` folder:
+- **API.md** – API endpoints and request/response details
+- **DEPLOYMENT.md** – Production deployment steps
 
-API.md – API endpoints and request/response details
+---
 
-DEPLOYMENT.md – Production deployment steps
-
-📝 License
+## 📝 License
 
 MIT License
 
-📧 Contact
-
-GitHub: https://github.com/Sarfarazsfz
-
-LinkedIn: https://www.linkedin.com/in/faraz4237/
+---
 
-GitHub: https://github.com/Sarfarazsfz
+## 📧 Contact
 
-LinkedIn: https://www.linkedin.com/in/sarfaraz-alam/
+- **GitHub**: https://github.com/Sarfarazsfz
+- **LinkedIn**: https://www.linkedin.com/in/faraz4237/