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sp103107/
NERA

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🎓 NORA - Northeastern Admissions Agent

AI-Powered Admissions Assistant for Northeastern University

NORA is an intelligent admissions evaluation system that uses Google Gemini AI to analyze student applications, score them across multiple criteria, and provide detailed rankings with explanations.

🚀 Live Demo

Try NORA online: Hugging Face Space

✨ Features

🤖 AI-Powered Analysis

Google Gemini Integration: Advanced AI analysis using Google's latest language model
Multi-Criteria Evaluation: Scores across Academics, Activities, Essays, and Recommendations
Intelligent Justifications: Detailed explanations for each scoring decision
Comprehensive Summaries: AI-generated applicant summaries

📊 Professional Interface

Streamlit Web UI: Modern, responsive web interface
Real-time Processing: Live progress tracking and status updates
Interactive Demos: Multiple demo modes for different use cases
Professional Styling: Northeastern University branding and modern design

🧪 Robust Testing

Comprehensive Test Suite: 80%+ test coverage
Unit Tests: Individual component testing
Integration Tests: End-to-end workflow validation
Performance Tests: Load handling and scalability testing

🎯 Demo-Ready

Multiple Demo Modes: Full Review, Quick Preview, Individual Review
Professional Presentations: Ready for stakeholder demonstrations
Interactive Elements: Audience participation capabilities
Detailed Documentation: Complete demo guides and scenarios

🏗️ Architecture

NORA/
├── agent/                 # Core AI agent logic
│   ├── admissions_agent.py    # Main agent class
│   ├── prompts.py            # AI prompt engineering
│   └── utils.py              # Utility functions
├── ui/                   # User interface
│   └── streamlit_app.py      # Streamlit web application
├── tests/                # Comprehensive test suite
│   ├── test_agent.py         # Agent unit tests
│   ├── test_integration.py   # Integration tests
│   ├── test_performance.py   # Performance tests
│   ├── test_ui.py           # UI component tests
│   └── mocks.py             # Mock data and responses
├── data/                  # Sample data
│   ├── sample_applications.json
│   └── rubric.json
└── docs/                  # Documentation
    ├── DEMO_README.md
    ├── UI_DEMO_GUIDE.md
    └── ROADMAP_SUMMARY.md

🚀 Quick Start

Prerequisites

Python 3.8+
Google Gemini API key

Installation

Clone the repository

bash
1git clone https://github.com/sp103107/NORA.git 2cd NORA
Install dependencies

pip install -r requirements.txt
Set up your API key

bash
1# Windows PowerShell 2$env:GOOGLE_API_KEY = "your-api-key-here" 3 4# Linux/Mac 5export GOOGLE_API_KEY="your-api-key-here"
Run the application

bash
1# CLI version 2python app.py 3 4# Web UI 5python -m streamlit run ui/streamlit_app.py

🎛️ Demo Modes

1. Full Review Mode 🚀

Complete demonstration with all 10 sample applicants

Real-time progress tracking
Professional leaderboard
Detailed applicant analysis

2. Quick Preview Mode ⚡

Instant demo without API calls

Sample data display
UI layout demonstration
No API key required

3. Individual Review Mode 👤

Focused analysis of specific applicants

Select individual applicants
Detailed scoring breakdown
Interactive analysis

🧪 Testing

Run All Tests

python run_tests.py

Quick Validation

python run_tests.py --quick

Individual Test Suites

bash

# Unit tests
python -m pytest tests/test_agent.py -v
python -m pytest tests/test_utils.py -v
python -m pytest tests/test_ui.py -v

# Integration tests
python -m pytest tests/test_integration.py -v

# Performance tests
python -m pytest tests/test_performance.py -v

📊 Test Results

Unit Tests: 27/28 passing (96.4%)
Integration Tests: 11/11 passing (100%)
Performance Tests: 10/10 passing (100%)
UI Tests: 11/11 passing (100%)
Overall Success Rate: 80%+

🎓 Demo Scenarios

Executive Presentation

Use Quick Preview for UI overview
Demonstrate Full Review for complete workflow
Highlight professional leaderboard
Show detailed applicant analysis

Technical Deep Dive

Start with Individual Review
Select different applicants to show variety
Expand AI analysis sections
Discuss scoring methodology

Interactive Demo

Let audience choose applicants
Show real-time AI processing
Discuss specific scoring decisions
Answer methodology questions

🔧 Configuration

Environment Variables

GOOGLE_API_KEY: Your Google Gemini API key (required)

API Configuration

Model: gemini-1.5-flash (default)
Temperature: 0.3 (controlled randomness)
Max Tokens: Auto-determined by Gemini

🏛️ Scoring Criteria

NORA evaluates applicants across four weighted criteria:

Academics (30%): GPA, SAT scores, academic achievements
Activities (25%): Extracurricular involvement, leadership roles
Essay (25%): Writing quality, personal insight, authenticity
Recommendations (20%): Teacher/counselor endorsements

🚀 Deployment

Hugging Face Spaces

The application is deployed on Hugging Face Spaces:

Space URL: https://huggingface.co/spaces/sp103107/NORA
Framework: Streamlit
Python Version: 3.9+

Local Deployment