https://github.com/likhithsai2580/ssquare-self-study
Making Question for JEE for Everyone, Everywhere
https://github.com/likhithsai2580/ssquare-self-study
ai automation jee llm machine-learning mpc question-predictior
Last synced: 4 months ago
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Making Question for JEE for Everyone, Everywhere
- Host: GitHub
- URL: https://github.com/likhithsai2580/ssquare-self-study
- Owner: Likhithsai2580
- License: mit
- Created: 2024-09-09T11:51:46.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-11T18:46:55.000Z (over 1 year ago)
- Last Synced: 2025-10-10T08:14:36.109Z (9 months ago)
- Topics: ai, automation, jee, llm, machine-learning, mpc, question-predictior
- Language: Python
- Homepage:
- Size: 101 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
# SSquare Self Study
SSquare Self Study is a comprehensive web application designed to help students prepare for competitive exams like JEE Main and JEE Advanced. The platform combines AI-powered learning with traditional study methods to provide a personalized learning experience.
## System Overview
### Core Components
1. **Authentication System**
- Firebase-based authentication
- Secure user sessions with Flask-Login
- JWT token management for API security
- Role-based access control (student/admin)
2. **Database Architecture**
- SQLAlchemy ORM for data management
- Models for Users, Exams, Questions, Progress, etc.
- Real-time data synchronization
- Automated database migrations
3. **Real-time Features**
- WebSocket connections via Flask-SocketIO
- Live exam updates and notifications
- Real-time progress tracking
- Instant performance feedback
4. **AI/ML Integration**
- Question prediction engine using TensorFlow
- Performance analysis with scikit-learn
- Personalized study paths
- Adaptive difficulty adjustment
## How It Works
### 1. User Journey
1. **Registration & Onboarding**
- User creates account
- Completes initial assessment
- Sets study goals and preferences
- Receives personalized study plan
2. **Daily Learning Flow**
- Dashboard shows daily tasks
- AI recommends study materials
- Practice questions based on weak areas
- Real-time progress updates
3. **Exam Preparation**
- Scheduled mock tests
- Performance analysis
- Topic-wise improvement suggestions
- Predicted question patterns
### 2. AI Systems
#### Question Prediction Engine
- Uses historical exam data
- Analyzes question patterns
- Considers topic frequency
- Predicts likely questions
- Updates predictions based on new data
#### Study Recommendation System
- Analyzes user performance
- Identifies knowledge gaps
- Creates personalized study paths
- Adapts to learning speed
- Suggests revision schedules
#### Performance Analytics
- Real-time performance tracking
- Comparative analysis
- Progress visualization
- Strength/weakness identification
- Time management insights
### 3. Technical Implementation
#### Backend Architecture
```
app/
├── __init__.py # App initialization
├── models/ # Database models
│ ├── user.py # User model
│ ├── exam.py # Exam model
│ └── progress.py # Progress tracking
├── routes/ # API endpoints
│ ├── main.py # Core routes
│ ├── auth.py # Authentication
│ ├── exam.py # Exam handling
│ └── api.py # API endpoints
└── services/ # Business logic
├── ai/ # AI components
├── analytics/ # Analytics
└── notification/ # Notifications
```
#### Key Processes
1. **Exam Creation**
```python
# Example workflow
def create_exam():
questions = generate_questions()
schedule_exam()
notify_users()
```
2. **Study Recommendations**
```python
# Example workflow
def get_recommendations(user_id):
performance = analyze_performance(user_id)
gaps = identify_gaps(performance)
return generate_study_plan(gaps)
```
3. **Progress Tracking**
```python
# Example workflow
def track_progress(user_id, activity):
update_progress(user_id, activity)
analyze_performance()
adjust_recommendations()
```
### 4. External Integrations
#### Discord Integration
- Community engagement
- Instant notifications
- Study group coordination
- Doubt resolution
#### Firebase Integration
- Authentication
- Real-time database
- File storage
- Analytics
## Setup and Configuration
### 1. Environment Setup
```bash
# Create virtual environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
```
### 2. Configuration Files
```bash
# .env file structure
SECRET_KEY=your_secret_key
DATABASE_URL=sqlite:///ssquare.db
DISCORD_BOT_TOKEN=your_discord_bot_token
FIREBASE_CONFIG=your_firebase_config
```
### 3. Database Initialization
```bash
flask db init
flask db migrate
flask db upgrade
```
## Development Guidelines
### 1. Code Style
- Follow PEP 8 guidelines
- Use Black for formatting
- Implement type hints
- Write docstrings
### 2. Testing
```bash
# Run tests
pytest
# Coverage report
pytest --cov=app tests/
```
### 3. Git Workflow
```bash
# Feature development
git checkout -b feature/new-feature
git commit -m "Add new feature"
git push origin feature/new-feature
```
## Deployment
### Production Deployment
1. **Server Requirements**
- Python 3.8+
- PostgreSQL
- Redis (for caching)
- SSL certificate
2. **Deployment Steps**
```bash
# Set production configs
export FLASK_ENV=production
export DATABASE_URL=postgresql://...
# Run migrations
flask db upgrade
# Start application
gunicorn -w 4 -k gevent run:app
```
3. **Monitoring**
- Application logs
- Error tracking
- Performance metrics
- User analytics
## Security Measures
1. **Data Protection**
- Encrypted storage
- Secure sessions
- Rate limiting
- Input validation
2. **Access Control**
- Role-based permissions
- API authentication
- Session management
- IP blocking
## Maintenance
### Regular Tasks
1. Database backups
2. Log rotation
3. Security updates
4. Performance optimization
### Monitoring
1. Server health
2. Error rates
3. User engagement
4. System performance
## Support and Contact
For technical support or queries:
1. Open an issue on GitHub
2. Join our Discord community
3. Email: support@ssquare-study.com
## License
This project is licensed under the MIT License. See [LICENSE](LICENSE) file for details.