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🧠 Skill Gap Analyser – Backend (Pathfinder AI)
This repository contains the backend service for Pathfinder AI, responsible for analyzing a user’s skills against a chosen career and returning missing skills, recommendations, and learning guidance.
The backend is built using Flask and deployed on Render using Gunicorn.

🌐 Live API
πŸ”— Base URL:
https://skill-gap-analyser-ves3.onrender.com
πŸ”— Health Check:
GET /

πŸ› οΈ Tech Stack
🐍 Python 3
🌢️ Flask
πŸ”— Flask-CORS
πŸš€ Gunicorn (Production server)
☁️ Render (Deployment)

πŸ“ Project Structure
skill-gap-analyser/
β”‚
β”œβ”€β”€ app.py # Main Flask application
β”œβ”€β”€ requirements.txt # Python dependencies
β”œβ”€β”€ .gitignore
β”œβ”€β”€ README.md
β”‚
β”œβ”€β”€ venv/ # Virtual environment (ignored in git)
β”œβ”€β”€ pycache/ # Python cache (ignored in git)

πŸ”Œ API Endpoints
βœ… Health Check
GET /
Response
{
"status": "Backend is running"
}

🎯 Skill Gap Analysis
POST /api/skill-gap

Request Body
{
"career": "Data Scientist",
"skills": ["python", "statistics"]
}

Response
{
"career": "Data Scientist",
"required_skills": [
"python",
"statistics",
"machine learning",
"sql",
"data visualization"
],
"known_skills": ["python", "statistics"],
"missing_skills": [
"machine learning",
"sql",
"data visualization"
],
"completion_percentage": 40
}

🧠 Skill Analysis Logic
Career is mapped to a predefined skill set
User-provided skills are normalized
Missing skills are calculated
Completion percentage is computed
JSON response sent to frontend
⚠️ Currently rule-based (no paid AI APIs used), making it free, fast, and hackathon-friendly

πŸ” CORS Configuration
CORS is enabled to allow frontend access:
from flask_cors import CORS
CORS(app)

This allows requests from:
Vercel frontend
Localhost (development)

πŸ§ͺ Run Locally
1️⃣ Clone the repository
git clone https://github.com/as4aditis-cmd/skill-gap-analyser.git
cd skill-gap-analyser
2️⃣ Create virtual environment
python -m venv venv
source venv/bin/activate # Mac/Linux
venv\Scripts\activate # Windows
3️⃣ Install dependencies
pip install -r requirements.txt
4️⃣ Run Flask app
python app.py

Server will run on:
http://localhost:5000

πŸš€ Production Deployment (Render)
Start Command
gunicorn app:app
Instance Type
Free tier supported
No paid services required

πŸ“¦ requirements.txt
flask
flask-cors
gunicorn

🧩 Environment Variables
No required environment variables for MVP.
(Ready for future AI keys if needed)

🧠 Future Enhancements
πŸ€– AI-based skill recommendations (LLM integration)
πŸ“š Learning resource suggestions
πŸ§‘β€πŸŽ“ Personalized roadmap generation
πŸ” Authentication & user-based analysis
πŸ“Š Skill proficiency scoring

πŸ‘©β€πŸ’» Author
Aditi Sharma
Backend & Full Stack Developer
GitHub: https://github.com/as4aditis-cmd
Project: Pathfinder AI

⭐ Support
If you find this useful:
⭐ Star the repository
🧠 Share feedback
πŸš€ Fork & build on top of it
πŸš€ β€œIdentify your gaps. Build your skills. Shape your future.”

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Flask-based backend API for Pathfinder AI that analyzes user skills against career goals and returns missing skills and recommendations.

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