Rapid Test Analyzer 🧪

🌐 Live Demo: https://rapid-test-analyzer.netlify.app

Overview

Advanced AI-powered medical test analysis system with professional-grade reporting capabilities. Supports pH strip analysis, Fecal Occult Blood (FOB) testing, and Urinalysis with computer vision and machine learning.

Features

• AI-Powered Analysis: Computer vision algorithms for accurate test interpretation
• Medical-Grade Reports: Professional diagnostic reports with clinical styling
• Multiple Test Types:
  • ✅ pH Analysis (Vaginal pH Testing)
  • ✅ FOB Detection (Fecal Occult Blood)
  • ✅ Urinalysis (10-parameter comprehensive urine analysis)
• User-Friendly Interface: Modern, responsive design with accessibility features
• Real-time Processing: Fast analysis with progress tracking
• Professional Styling: Medical-themed interface with glass morphism effects
• Confidence Scoring: KNN-based confidence levels for each result
• Abnormal Detection: Automatic flagging of abnormal test results

Urinalysis Features

• 10 Parameters Analyzed:
  • Blood (Hemoglobin/Non-Hemoglobin detection)
  • Bilirubin
  • Urobilinogen
  • Ketones
  • Protein
  • Nitrites
  • Glucose
  • pH Level
  • Specific Gravity
  • Leukocytes
• Automatic Pad Detection: Detects and analyzes all 10 test pads
• Missing Pad Reconstruction: Intelligently fills in missing pads
• KNN Classification: K-Nearest Neighbors algorithm for accurate results
• Detailed Results Table: Professional medical report format

Technologies Used

• Backend: Python, Flask, OpenCV, scikit-learn, NumPy
• Frontend: HTML5, CSS3 (TailwindCSS), Vanilla JavaScript
• AI/ML: Computer vision algorithms, image processing
• Styling: Glass morphism, responsive design, accessibility features

Quick Start

Prerequisites

• Python 3.8+
• Modern web browser

Installation

1. Clone the repository:

git clone https://github.com/yourusername/rapid-test-analyzer.git
cd rapid-test-analyzer

2. Install dependencies:

pip install -r requirements.txt

3. Run the application:

python app.py

4. Open your browser and navigate to http://localhost:5000

Usage

1. Select Test Type: Choose from pH Analysis or FOB Detection
2. Upload Image: Drag & drop or select your test image
3. Analyze: Click analyze to process your image
4. View Results: Get professional medical-style reports

Project Structure

├── app.py                        # Flask backend server
├── fob_analyzer.py               # FOB test analysis logic
├── ph_strip_analyzer.py          # pH strip analysis logic
├── urinalysis_strip_analyzer.py  # Urinalysis analysis logic (KNN-based)
├── frontend/
│   ├── index.html                # Main application interface
│   ├── result.html               # Results display page
│   ├── analyze.js                # Frontend JavaScript logic
│   └── result.js                 # Results page logic
├── templates/                    # Flask templates
├── static/                       # Static assets
├── uploads/                      # Temporary image storage
├── result_images/                # Analysis result images
├── requirements.txt              # Python dependencies
├── Procfile                      # Deployment configuration
├── runtime.txt                   # Python version
└── render.yaml                   # Render.com deployment config

API Endpoints

• GET / - Main application interface
• POST /analyze - Image analysis endpoint
• GET /result - Results display page

Supported File Types

• PNG, JPG, JPEG, GIF, BMP
• Maximum file size: 10MB

Development Roadmap

• pH Strip Analysis
• FOB Detection
• Urinalysis (10-parameter analysis)
• Professional Medical Reports
• Responsive Design
• KNN-based Classification
• Confidence Scoring
• Abnormal Result Detection
• User Authentication
• Test History
• Multi-language Support
• Export to PDF
• API Documentation

Deployment

Deploy to Render.com (Recommended - Free)

Deployment

Deploy to Render.com (Recommended - Free)

1. Push your code to GitHub (if not already done):

   git add .
   git commit -m "Ready for deployment"
   git push origin main

2. Sign up/Login to Render.com:

   • Go to render.com
   • Sign up with your GitHub account

3. Create a New Web Service:

   • Click "New +" → "Web Service"
   • Connect your GitHub repository
   • Select the rapid-test-analyzer repository

4. Configure the service:

   • Name: rapid-test-analyzer (or your preferred name)
   • Runtime: Python 3
   • Build Command: pip install -r requirements.txt
   • Start Command: gunicorn app:app --bind 0.0.0.0:$PORT --workers 1 --timeout 60
   • Plan: Free

5. Deploy:

   • Click "Create Web Service"
   • Wait for the build to complete (5-10 minutes)
   • Your app will be live at https://your-app-name.onrender.com

Deploy to Railway.app

1. Push to GitHub (if not already done)

2. Deploy on Railway:

   • Go to railway.app
   • Click "Start a New Project"
   • Select "Deploy from GitHub repo"
   • Choose your repository
   • Railway will auto-detect Flask and deploy

Environment Variables (if needed)

No environment variables required for basic deployment. The app works out of the box!

Post-Deployment

• The first request may take 30-60 seconds as the server spins up (free tier)
• Subsequent requests will be faster
• Upload limit: 16MB (configurable in app.py)

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

Medical Disclaimer

This system is for informational purposes only. Results should be verified by qualified healthcare professionals. Not intended to replace professional medical diagnosis.

Contact

For questions or support, please open an issue on GitHub.