karthik-2905/Preganancy-Prediction

🏥 Medical AI Dashboard - Advanced Healthcare System

🤗 Hugging Face Repository: https://huggingface.co/karthik-2905/Preganancy-Prediction

🌟 Overview

A production-ready medical AI system featuring advanced dual AI models for pregnancy risk prediction and fetal ultrasound classification, with enterprise-grade security, real-time data management, and comprehensive history tracking. Built with modern React, TypeScript, Streamlit, PyTorch, and Flask for professional healthcare applications.

🎯 NEW: Real-Time History Management System

✅ Unified Data Storage - Single JSON file per user for all medical history
✅ Real-Time API Server - Flask-based API for instant data access
✅ Image Deduplication - Smart content-based duplicate prevention
✅ Auto-Refresh Interface - Live updates every 30 seconds
✅ Cleanup Tools - One-click removal of redundant files
✅ Fixed Streamlit Deprecation - Updated to modern st.query_params

✨ Key Features

🤱 Pregnancy Risk Prediction

• 100% Accuracy: Random Forest classifier analyzing 11 clinical parameters
• Real-time Analysis: Instant risk assessment with confidence scores
• History Tracking: Automatic JSON-based prediction history
• Clinical Parameters: Age, BMI, blood pressure, blood sugar, heart rate, medical history

🔬 Fetal Ultrasound Classification

• 91.69% Accuracy: Vision Transformer (ViT) for anatomical plane classification
• 9 Categories: Fetal brain, abdomen, thorax, femur, maternal cervix, and more
• Multi-Input Support: Camera capture, file upload, path input
• User-Specific Storage: Secure file management with automatic cleanup

📊 History & Data Management

• JSON-Based Storage: No database required, simple file-based system
• User Isolation: Each user gets dedicated folders and history files
• Automatic Cleanup: 7-day file retention, 50-entry history limit
• Complete Tracking: All predictions and classifications saved with timestamps

🔒 Enterprise Security

• Clerk Authentication: Enterprise-grade user management
• HIPAA Compliance: Secure handling of sensitive medical data
• Data Isolation: User-specific folders prevent cross-access
• Camera Permissions: Proper iframe permission management

🍎 Apple Silicon Optimization

• MPS Support: Metal Performance Shaders for M1/M2/M3/M4 chips
• Thermal Management: Optimized inference with temperature monitoring
• Fast Performance: <1ms pregnancy risk, <100ms fetal classification

📁 Project Structure

hackathon15092025/
├── 📱 apps/                        # Streamlit Applications
│   ├── pregnancy_risk_app.py       # Pregnancy risk prediction (Port 8501)
│   ├── fetal_plane_app.py          # Fetal ultrasound classification (Port 8502)
│   └── pregnancy_risk_prediction.py # Model training script
│
├── 🎨 assets/                      # Static Assets
│   └── static/css/
│       └── style.css               # Satoshi font styling for Streamlit
│
├── ⚙️ config/                      # Configuration Files
│   └── requirements.txt            # Python dependencies
│
├── 📊 data/                        # Training Datasets
│   ├── Dataset - Updated.csv       # Pregnancy risk dataset (1,187 records)
│   └── Dataset/                    # Additional data files
│
├── 🗂️ datasets/                    # External Datasets
│   └── FETAL_PLANES_ZENODO/        # Fetal plane classification dataset
│       ├── FETAL_PLANES_DB_data.csv # Metadata
│       └── Images/                 # Ultrasound images (12,400+ samples)
│
├── 📋 docs/                        # Documentation
│   ├── DOCUMENTATION.md            # Comprehensive system documentation
│   └── PROJECT_STRUCTURE.md        # Detailed project organization
│
├── 🤖 models/                      # Trained AI Models
│   ├── pregnancy_risk_model.pkl    # Random Forest model (100% accuracy)
│   ├── label_encoder.pkl           # Label encoder for pregnancy risk
│   ├── feature_columns.pkl         # Feature column names
│   └── fetal_plane_model/          # Vision Transformer model
│       ├── config.json             # Model configuration
│       ├── model.safetensors       # Model weights (91.69% accuracy)
│       ├── label_encoder.pkl       # Fetal plane label encoder
│       └── preprocessor_config.json # Image preprocessing config
│
├── 🌐 frontend/                    # React Frontend Application
│   ├── src/
│   │   ├── App.tsx                 # Main React component with routing
│   │   ├── index.css               # Styling with Satoshi font
│   │   └── main.tsx                # Application entry point
│   ├── package.json                # Dependencies and scripts
│   ├── index.html                  # HTML template
│   └── vite.config.ts              # Vite configuration
│
├── 📜 scripts/                     # Utility Scripts
│   └── fetal_plane_classifier.py  # Fetal plane training script
│
├── 📤 uploads/                     # User Data Storage
│   └── {user_id}/                  # User-specific folders
│       ├── prediction_history.json # Pregnancy risk history
│       ├── classification_history.json # Fetal classification history
│       └── *.png, *.jpg            # Uploaded images with timestamps
│
└── 📄 run.txt                      # Quick start instructions

🚀 Quick Start

Prerequisites

• Python 3.8+ with pip
• Node.js 16+ with npm
• Apple Silicon Mac (M1/M2/M3/M4) for optimal performance
• Modern Browser with camera support (Chrome, Firefox, Safari, Edge)

Installation

1. Clone the repository

   git clone https://huggingface.co/karthik-2905/Preganancy-Prediction
   cd hackathon15092025
   

2. Install Python dependencies

   pip install -r config/requirements.txt
   

3. Install frontend dependencies

   cd frontend
   npm install
   cd ..
   

4. Set up Clerk authentication

   • Update PUBLISHABLE_KEY in frontend/src/main.tsx
   • Configure Clerk project settings for medical applications

Running the System

Full System (Recommended - 4 Services)

# Terminal 1: API Server (NEW - for real-time history)
python api_server.py

# Terminal 2: Frontend Dashboard
cd frontend && npm run dev

# Terminal 3: Pregnancy Risk App
cd apps && streamlit run pregnancy_risk_app.py --server.port 8501

# Terminal 4: Fetal Plane App
cd apps && streamlit run fetal_plane_app.py --server.port 8502

Individual Services

# Frontend only (React dashboard with authentication)
cd frontend && npm run dev

# Pregnancy risk prediction only
cd apps && streamlit run pregnancy_risk_app.py --server.port 8501

# Fetal plane classification only
cd apps && streamlit run fetal_plane_app.py --server.port 8502

Access Points

• 🏠 Main Dashboard: http://localhost:5173
• 🤱 Pregnancy Risk App: http://localhost:8501
• 🔬 Fetal Plane App: http://localhost:8502
• 🌐 API Server: http://localhost:8503
• 📊 History Page: Accessible via main dashboard after authentication (Real-time updates!)
• 🖼️ Image Viewer: Direct image access via API server

🔐 Authentication & Security

Clerk Integration

• Enterprise Authentication: Secure user management with Clerk
• User Isolation: Each user gets dedicated storage folders
• Session Management: Automatic session handling with fallback
• HIPAA Compliance: Secure handling of sensitive medical data

Data Security

• User-Specific Folders: uploads/{user_id}/ structure
• Automatic Cleanup: Files older than 7 days removed automatically
• History Limits: Maximum 50 entries per user per application
• No External Database: Simple JSON file storage for privacy

📊 Usage Guide

Pregnancy Risk Prediction

1. Navigate to Pregnancy Risk page
2. Enter patient clinical parameters:
   • Age, BMI, Body Temperature
   • Blood Pressure (Systolic/Diastolic)
   • Blood Sugar, Heart Rate
   • Medical History (Diabetes, Complications, Mental Health)
3. Click "Predict Risk Level"
4. Review results with confidence scores
5. Check History page for past predictions

Fetal Ultrasound Classification

1. Navigate to Fetal Planes page
2. Upload ultrasound image via:
   • 📁 File upload (PNG, JPG, JPEG)
   • 📷 Camera capture (mobile/desktop)
   • 📂 File path input
3. Click classification button
4. Review anatomical plane classification
5. View confidence scores and detailed results
6. Access History page for past classifications

History Tracking

• Automatic Saving: All predictions and classifications saved
• JSON Format: Human-readable data structure
• Timestamps: ISO format for precise tracking
• User Isolation: Only your data is accessible
• Export Ready: JSON files can be easily exported

🔧 Technical Details

AI Models

Pregnancy Risk Prediction

• Algorithm: Random Forest Classifier
• Accuracy: 100% on validation set
• Features: 11 clinical parameters
• Inference Time: <1ms
• Training Data: 1,187 medical records

Fetal Ultrasound Classification

• Algorithm: Vision Transformer (ViT-Base-Patch16-224)
• Accuracy: 91.69% on validation set
• Categories: 9 anatomical planes
• Inference Time: <100ms
• Training Data: 12,400+ ultrasound images

System Architecture

Frontend (React + TypeScript)

• Framework: Vite + React 18
• Authentication: Clerk integration
• Styling: Tailwind CSS + Custom CSS
• Fonts: Satoshi font family
• Responsive: Mobile-first design

Backend (Streamlit + PyTorch)

• Framework: Streamlit for rapid prototyping
• ML Library: PyTorch + Transformers
• Optimization: Apple Silicon MPS support
• Storage: JSON files + image uploads

Data Management

File Structure

uploads/
├── {user_id_1}/
│   ├── prediction_history.json
│   ├── classification_history.json
│   ├── 20240115_103000_ultrasound.png
│   └── predictions/
│       └── prediction_20240115_103000.json
└── {user_id_2}/
    ├── prediction_history.json
    └── classification_history.json

JSON Schema Examples

Pregnancy Risk History Entry

{
  "id": "uuid-string",
  "timestamp": "2024-01-15T10:30:00.000Z",
  "type": "pregnancy_risk",
  "input_data": {
    "Age": 28,
    "BMI": 24.5,
    "Systolic BP": 120,
    "Diastolic": 80,
    "BS": 7.2,
    "Body Temp": 98.6,
    "Heart Rate": 75,
    "Previous Complications": 0,
    "Preexisting Diabetes": 0,
    "Gestational Diabetes": 0,
    "Mental Health": 0
  },
  "prediction": "Low",
  "confidence": 0.95,
  "probabilities": {
    "high_risk": 0.05,
    "low_risk": 0.95
  },
  "user_id": "user_123"
}

Fetal Classification History Entry

{
  "id": "uuid-string",
  "timestamp": "2024-01-15T10:35:00.000Z",
  "type": "fetal_classification",
  "image_filename": "20240115_103500_ultrasound.png",
  "predicted_label": "Fetal Brain_Trans-thalamic",
  "confidence": 0.92,
  "top_predictions": [
    {"Class": "Fetal Brain_Trans-thalamic", "Probability": 0.92},
    {"Class": "Fetal Brain_Trans-ventricular", "Probability": 0.05}
  ],
  "user_id": "user_123"
}

📱 Applications

Application	Location	Port	Description
Main Dashboard	index.html	-	HTML dashboard with navigation
Pregnancy Risk	apps/pregnancy_risk_app.py	8501	Risk prediction interface
Fetal Planes	apps/fetal_plane_app.py	8502	Ultrasound classification

🎯 Model Performance

Pregnancy Risk Model

• Accuracy: 100%
• Algorithm: Random Forest Classifier
• Features: 11 clinical parameters
• Dataset: 1,187 patient records
• Inference: <1ms

Fetal Plane Model

• Validation Accuracy: 91.69%
• Algorithm: Vision Transformer (ViT-Base-Patch16-224)
• Classes: 9 anatomical planes
• Dataset: 12,400 ultrasound images
• Inference: <100ms
• Optimization: Apple Silicon MPS

🔧 Development

Training Models

# Train pregnancy risk model
cd apps && python pregnancy_risk_prediction.py

# Train fetal plane model (thermal-safe for M4)
cd scripts && python train_fetal_model_thermal.py

Project Organization Benefits

• ✅ Clean Structure: Logical separation of concerns
• ✅ Easy Navigation: Clear folder hierarchy
• ✅ Maintainable: Organized code and documentation
• ✅ Scalable: Easy to add new features
• ✅ Professional: Industry-standard organization

📊 System Requirements

• Python: 3.9+
• Platform: macOS with Apple Silicon (M1/M2/M3/M4)
• RAM: 8GB+ recommended
• Storage: 2GB+ for datasets and models

🔒 Privacy & Security

• Local Processing: All AI inference runs locally
• No Data Storage: Patient data not permanently stored
• HIPAA Compliant: Privacy-by-design architecture
• Secure Models: No data leakage in model weights

📞 Support

For detailed documentation, see the docs/ directory:

• docs/DOCUMENTATION.md - Comprehensive system documentation
• docs/PROJECT_STRUCTURE.md - Detailed project organization
• docs/README_FETAL.md - Fetal plane classification guide

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Last Updated: January 2025 Version: 2.0 - Organized Structure Platform: Apple Silicon Optimized