Team 25 – Washington State University

Project Lead: Khushi Panchal
Team Member: Steven Bennett
Client: Trevor Kingsley
Mentor: Parteek Kumar

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Project Overview

The International Student Scoring System (IARA) supports WSU's admissions office by scoring international applicants accurately and efficiently.

Demo Video

Sprint 5 Demo – Direct link: Youtube.com

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Installation

Prerequisites

• Python 3.11+
• Git
• pip

Additional Requirements

• Flask, ReportLab, OpenPyXL
• Transformers, Torch, Sentence-Transformers
• Tesseract OCR, Poppler / PDFPlumber
• Pandas, NumPy, Pillow, OpenCV, PyMuPDF

Setup

git clone https://github.com/Stevieb253/CPTS421_International_Rating_Algorithm.git
cd CPTS421_International_Rating_Algorithm
python -m venv .venv
source .venv/bin/activate   # Linux/Mac
.venv\Scripts\activate      # Windows
pip install -r requirements.txt

Set your HuggingFace token (recommended):

export HF_TOKEN=your_token_here        # Linux/Mac
set HF_TOKEN=your_token_here           # Windows

Run the app:

cd code
python app.py

Access at http://localhost:5000
Default login: admin / admin123

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Usage

Single Applicant Analysis

• Log in and navigate to the home page
• Fill in student profile or load a sample (High / Medium / High Risk)
• Click Analyze Student to generate scores and essay analysis
• Add staff comments and download PDF or plain text report

Batch Analysis

• Navigate to Batch Process
• Upload a CSV file with columns: studentId, country, gpa, curriculum, travelHistory, essayText, negFactors
• Download the template from the page if needed
• Review results and export as CSV or Excel

Fraud Screening

• Navigate to Financial Docs or Transcripts
• Upload one or more PDFs
• Review per-page risk levels (LOW / MEDIUM / HIGH), OCR preview, and fraud signals
• Add reviewer notes and export a combined PDF or TXT report

Admin

• Log in as admin and navigate to /admin/users
• Create new staff accounts with Reviewer or Admin role
• Deactivate or delete existing accounts

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Project Structure

code/
├── app.py                  # Main Flask application
├── db/
│   ├── database.py         # SQLite database layer
│   ├── report_generator.py # PDF report generation
│   └── student_scoring.db  # SQLite database file
├── services/
│   ├── student_analyzer.py       # Core scoring engine
│   ├── nlp_service.py            # Sentiment & similarity
│   ├── financial_fraud_detector.py
│   └── transcript_fraud_detector.py
└── templates/              # All 11 HTML templates

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Contribution

git checkout -b my-new-feature
git commit -am 'Add feature'
git push origin my-new-feature

Submit a pull request.