RESTART Career Platform

RESTART is a web-based platform that helps former inmates rebuild their career pathway through structured learning, career assessment powered by a machine learning model, and an AI chat assistant for career guidance.

This repository contains three separate services:

• frontend/ — React + Vite web client (port 5173)
• backend/ — Express REST API server (port 5000)
• ai-service/ — FastAPI ML prediction + RAG service (port 8000)

Tech Stack

Layer	Technology
Frontend	React, Vite, Tailwind CSS
Backend	Node.js, Express, mysql2, bcryptjs, jsonwebtoken
Database	MySQL (restart_db)
AI Service	Python, FastAPI, TensorFlow / Keras
Chat AI	Google Gemini 2.5 Flash (via Gemini API)
RAG	ChromaDB, sentence-transformers

Repository Structure

restart-career-platform/
├── backend/          # Express API application
├── frontend/         # React + Vite application
├── ai-service/       # FastAPI ML prediction + RAG service
│   ├── model/            # Keras DNN model + artifacts (in git)
│   ├── models/           # Embedding model — downloaded separately (gitignored)
│   ├── knowledge/        # Career knowledge base for RAG (in git)
│   ├── main.py
│   ├── rag.py
│   ├── download_model.py # One-time embedding model downloader
│   └── requirements.txt
├── data-science/     # Data exploration, Notebooks, and Streamlit Dashboard
│   ├── dataset/      # Master_Data_RESTART.csv
│   └── dashboard/    # Streamlit app files
└── package.json      # Root scripts for running backend and frontend

For service-specific details, see:

• Backend README
• Frontend README

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Data & Analytics

The analytics and raw data processing pipelines are located in the data-science/ directory. The career recommendation model is trained on a specially curated dataset derived from the O*NET (Occupational Information Network) standards, tailored specifically for the SME and digital creative ecosystems in Indonesia.

• Dataset: Master_Data_RESTART.csv contains 16 essential features (10 cross-sectoral competencies and 6 Holland Code/RIASEC personality dimensions).
• Dashboard: Exploratory Data Analysis (EDA) visualizations can be accessed via our interactive Streamlit app.

Running the Streamlit Dashboard Locally

cd data-science/dashboard
pip install -r requirements.txt
streamlit run app.py

Getting Started

1. Install dependencies

# Backend
cd backend && npm install

# Frontend
cd ../frontend && npm install

2. Configure environment

cp backend/.env.example backend/.env

Fill in backend/.env:

DB_HOST=localhost
DB_USER=root
DB_PASS=
DB_NAME=restart_db
JWT_SECRET=your_secret_here
AI_SERVICE_URL=http://localhost:8000
GEMINI_API_KEY=your_gemini_api_key_here

Getting a Gemini API key (free) Go to https://aistudio.google.com, sign in with a Google account, and click Get API key. The free tier is sufficient for development.

3. Set up the database

mysql -u root restart_db < backend/migrations/001_create_users_table.sql
mysql -u root restart_db < backend/migrations/002_add_phone_education_to_users.sql

4. Set up the AI service

cd ai-service
python -m venv venv
venv\Scripts\activate        # Windows
# source venv/bin/activate   # macOS / Linux
pip install -r requirements.txt

4a. Download the embedding model (one time only)

The Keras career model (ai-service/model/) is already included in this repository.

The RAG embedding model (paraphrase-multilingual-MiniLM-L12-v2, ~449 MB) is not included because of its size. Download it once using the provided script:

python download_model.py

The model is saved to ai-service/models/ (gitignored) and will be loaded from there on every subsequent startup — no internet connection needed after the first download.

4b. Start the AI service

uvicorn main:app --reload --port 8000

On startup you will see:

Knowledge base loaded: 72 chunks from 12 professions.

This confirms the RAG knowledge base is indexed and the service is ready.

5. Start backend and frontend

From the project root:

npm run start           # runs backend + frontend concurrently

Or separately:

npm run backend:dev     # port 5000
npm run frontend:dev    # port 5173

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Available Root Scripts

npm run backend:dev
npm run backend:start
npm run frontend:dev
npm run frontend:build
npm run frontend:lint
npm run start           # runs backend + frontend concurrently

Service Ports

Service	URL
Frontend	http://localhost:5173
Backend	http://localhost:5000
AI Service	http://localhost:8000

---

Request Flows

Assessment (ML-based career recommendation)

React page
  → POST /api/assessment/submit
  → assessmentController.js
  → POST http://localhost:8000/predict   (AI service)
  → Keras DNN → top-3 professions + confidence scores
  → save to MySQL
  → AssessmentResultPage.jsx

If the AI service is unreachable, the backend falls back to rule-based recommendations automatically.

Chat AI (career guidance)

DashboardPage.jsx
  → POST /api/chat  { message, context }
  → chatController.js
  → POST http://localhost:8000/retrieve  (RAG: find relevant career docs)
  → Gemini Flash API  (system prompt + RAG docs + user context)
  → reply text → chat window

User context sent with every message: assessment result, top professions, skill scores, enrolled courses, and learning progress.

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Models & Artifacts

File	Location	In Git	Description
profession_recommendation_model.keras	ai-service/model/	Yes	Keras DNN career classifier
feature_scaler.json	ai-service/model/	Yes	Input normalization (mean/scale)
feature_names.json	ai-service/model/	Yes	Order of 16 input features
profession_classes.json	ai-service/model/	Yes	12 output profession labels
paraphrase-multilingual-MiniLM-L12-v2	ai-service/models/	No	Sentence embedding model for RAG (449 MB)

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Data Dictionary

The Master_Data_RESTART.csv dataset consists of 2,400 rows and 17 columns. Below is the breakdown of the features used to train the recommendation system:

Target Label (1 Column)

• Target_Profesi (Categorical): The target profession (15 job classes in the SME, operational, and digital creative sectors).

Cross-Sectoral Competency Features (10 Columns, Scale 1.0 - 5.0)

• Stamina: The physical ability to exert oneself over long periods without getting winded or out of breath.
• Static Strength: The ability to exert maximum muscle force to lift, push, pull, or carry objects.
• Manual Dexterity: The ability to quickly move your hand, your hand together with your arm, or your two hands to grasp or assemble objects.
• Spatial Orientation: The ability to know your location in relation to the environment or to know where other objects are in relation to you.
• Active Listening: Giving full attention to what other people are saying, taking time to understand the points being made.
• Speaking: Talking to others to convey information effectively and clearly.
• Service Orientation: Actively looking for ways to help people or customers.
• Coordination: Adjusting actions in relation to others' actions (teamwork).
• Troubleshooting: Determining causes of operating errors and deciding what to do about it.
• Time Management: Managing one's own time and the time of others efficiently.

Holland Code / RIASEC Personality Profiles (6 Columns, Scale 1.0 - 7.0)

• Realistic (R): Preference for practical, hands-on problems and working with tools, machinery, or plants.
• Investigative (I): Preference for working with ideas, searching for facts, and figuring out problems mentally.
• Artistic (A): Preference for creative exploration, visual design, and self-expression.
• Social (S): Preference for working with, communicating with, and teaching or helping people.
• Enterprising (E): Preference for starting up and carrying out projects, especially leading people and making business decisions.
• Conventional (C): Preference for following set procedures and routines, working with data and details cleanly.