AURORA - AI-powered Unified Review and Organization for Resourceful Academics

AURORA is a cutting-edge academic platform built using AI and NLP to revolutionize personalized learning and student productivity. It features resume analysis, note summarization, topic-specific chatbots, gamification modules, and AI-driven recommendations.

Key Features

• Personalized Note-Making – Organize, summarize, and download notes as PDFs.
• AI Chatbot Assistants – Get contextual academic help using Gemini-powered AI.
• Resume Analyzer – Extracts key fields and evaluates GenAI/ML exposure.
• AI Notes & Summarization – Generates summaries, flashcards, and concept maps.
• Interactive Learning – Quizzes, gamification, simulations, and case studies.
• Career & Skill Development – AI-guided internship/job matching and micro-certifications.
• External Integrations – Connect with LMS tools (Moodle, Canvas), Google Drive, and Notion.
• Accessibility & Inclusivity – Multi-language support and voice recognition features.
• AI Research Tools – Advanced research and resource curation capabilities.

Project Structure

AURORA/
├── main.py                          # Main application entry point
├── requirements.txt                 # Python dependencies
├── README.md                       # Project documentation
├── env_example.txt                 # Environment variables template
├── config/
│   └── settings.py                 # Configuration and settings
├── utils/
│   ├── ai_helpers.py              # AI utility functions
│   └── file_handlers.py           # File processing utilities
├── modules/
│   ├── resume_analyzer.py         # Resume analysis module
│   ├── ai_notes.py                # AI notes and summarization
│   ├── interactive_learning.py    # Interactive learning features
│   ├── community_features.py      # Community and collaboration tools
│   ├── research_tools.py          # AI research capabilities
│   ├── accessibility.py           # Accessibility features
│   ├── external_integrations.py   # LMS and app integrations
│   ├── career_development.py      # Career development tools
│   └── personalization.py         # AI personalization features
├── output/                         # Generated files (PDFs, Excel)
└── temp/                          # Temporary files

Setup Instructions

Prerequisites

• Python 3.8 or higher
• pip package manager

Installation

1. Clone the repository

   git clone https://github.com/PreethamNoelP/AURORA.git
   cd AURORA

2. Install dependencies

   pip install -r requirements.txt

3. Set up environment variables Copy env_example.txt to .env and add your API key:

   cp env_example.txt .env
   # Edit .env file and add your Gemini API key

4. Download spaCy model

   python -m spacy download en_core_web_sm

Configuration

1. API Keys: Add your Gemini API key to the .env file
2. File Paths: Update paths in config/settings.py if needed
3. Email Configuration: Configure email settings in config/settings.py

Running the Application

streamlit run main.py

The application will be available at http://localhost:8501

Usage

Getting Started

1. Navigate to the application in your browser
2. Use the sidebar to select different features
3. Follow the on-screen instructions for each module

Key Features

Resume Builder

• Upload PDF or DOCX resumes
• Get AI-powered analysis and insights
• Download results as Excel files

AI Notes

• Create and organize notes
• Generate AI-powered summaries
• Create flashcards and concept maps
• Download notes as PDF

Interactive Learning

• Participate in gamified learning
• Take quizzes and assessments
• Run virtual lab simulations
• Engage with case studies

Community Features

• Join discussion forums
• Collaborate on group projects
• Attend live expert Q&A sessions
• Generate email links for communication

Configuration

Environment Variables

• GEMINI_API_KEY: Your Google Gemini API key
• EMAIL_CONFIG: Email server configuration (optional)

Customization

• Modify config/settings.py to change default settings
• Update course recommendations in the settings file
• Customize quiz questions and learning paths

Dependencies

Core Dependencies

• streamlit: Web application framework
• google-generativeai: Gemini AI integration
• pandas: Data manipulation
• spacy: Natural language processing
• textblob: Sentiment analysis

File Processing

• pdfminer.six: PDF text extraction
• python-docx: DOCX file processing
• openpyxl: Excel file handling
• fpdf: PDF generation

AI and ML

• speech_recognition: Voice recognition
• deep-translator: Multi-language translation
• plotly: Data visualization

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For support and questions:

• Create an issue in the repository
• Check the documentation in each module
• Review the configuration settings

Future Enhancements

• Advanced analytics dashboard
• Real-time collaboration features
• Mobile app development
• Integration with more LMS platforms
• Advanced AI model support
• Multi-language interface
• Offline mode support

Performance Notes

• Large file uploads may take time to process
• AI responses depend on API availability
• Voice recognition requires microphone access
• PDF generation works best with smaller documents

Security

• API keys are stored in environment variables
• Temporary files are automatically cleaned up
• User data is processed locally when possible
• Secure file handling for uploaded documents