metadata

title: Synthex
emoji: 🚀
colorFrom: red
colorTo: red
sdk: docker
app_port: 8000
tags:
  - fastapi
  - medical
  - ai
pinned: false

Synthex AI - Medical Text Generation Platform

A synthetic medical text generator that creates realistic medical records using AI models. The application provides both a FastAPI backend and a Streamlit interface.

Note: This Hugging Face Space runs the FastAPI version with HTML interface. For the Streamlit version, please run locally using streamlit run app.py.

Features

Generate various types of medical records:
- Clinical Notes
- Discharge Summaries
- Lab Reports
- Prescriptions
- Patient Intake Forms
Support for multiple AI models:
- Hugging Face models (default)
- Google Gemini (optional)
Two interfaces:
- FastAPI with HTML frontend (Hugging Face Space)
- Streamlit interface (Local development)

API Endpoints

GET /: HTML interface
GET /record-types: List available record types

POST /generate: Generate medical records

{
  "record_type": "clinical_note",
  "quantity": 1,
  "use_gemini": false,
  "include_metadata": true
}

Deployment

Local Development

Install dependencies:
```
pip install -r requirements.txt
```
Run FastAPI server:
```
uvicorn src.api.app:app --reload
```
Run Streamlit app (optional):
```
streamlit run app.py
```

Docker Deployment

Build the Docker image:

docker build -t synthex-medical-generator .

Run the container:

docker run -p 8000:8000 synthex-medical-generator

Hugging Face Spaces Deployment

This Space runs the FastAPI version with HTML interface. The application is automatically deployed when you push to the repository.

Environment Variables

GEMINI_API_KEY: Google Gemini API key (optional)

License

MIT License

🏢 Enterprise Solution

Synthex AI provides enterprise-grade synthetic medical data generation with:

HIPAA Compliance: All generated data is synthetic and compliant with healthcare regulations
Enterprise Security: SOC 2 Type II certified infrastructure
Custom Solutions: Tailored generation for specific medical domains
API Access: RESTful API for integration with existing systems
Dedicated Support: 24/7 enterprise support and SLAs

💼 Use Cases

Healthcare AI Development

Train and test AI models without real patient data
Generate diverse medical scenarios for model validation
Create synthetic datasets for research and development

Medical Software Testing

Test EHR systems with realistic synthetic data
Validate clinical decision support systems
QA medical software with diverse patient scenarios

Healthcare Research

Conduct research with privacy-compliant data
Generate synthetic datasets for medical studies
Test hypotheses without patient privacy concerns

🚀 Features

Core Features

Multiple medical record types:
- Clinical Notes
- Discharge Summaries
- Lab Reports
- Prescriptions
- Patient Intake Forms
Advanced generation methods:
- Hugging Face models (default)
- Google Gemini API (premium)
- Custom model integration (enterprise)
Enterprise-grade UI/UX
Multiple export formats (JSON, CSV, TXT)
Batch generation capabilities
API access (enterprise)

Enterprise Features

Custom model training
Domain-specific generation
Advanced data validation
Integration support
Dedicated infrastructure
Custom SLAs

💰 Pricing

Free Tier

Basic medical record generation
Limited to 100 records/month
Community support
Basic templates

Pro Plan ($99/month)

Up to 10,000 records/month
Advanced generation features
Priority support
API access
Custom templates

Enterprise Plan (Custom)

Unlimited generation
Custom model training
Dedicated support
Custom integrations
SLA guarantees
On-premise deployment

🛠️ Technical Details

Architecture

synthex/
├── app.py                 # Main Streamlit application
├── src/
│   ├── generation/        # Core generation logic
│   ├── api/              # REST API endpoints
│   ├── validation/       # Data validation
│   └── enterprise/       # Enterprise features
├── data/
│   └── generated/        # Generated records storage
├── tests/                # Test suite
├── Dockerfile           # Docker configuration
└── requirements.txt     # Python dependencies

API Reference

from synthex import SynthexClient

# Initialize client
client = SynthexClient(api_key="your_api_key")

# Generate records
records = client.generate_records(
    record_type="clinical_note",
    count=100,
    options={
        "include_metadata": True,
        "custom_fields": ["patient_demographics", "vital_signs"]
    }
)

# Export data
client.export_records(
    records,
    format="json",
    destination="s3://your-bucket/path"
)

🔒 Security & Compliance

HIPAA Compliance
SOC 2 Type II Certification
GDPR Compliance
Data Encryption at Rest and in Transit
Regular Security Audits
Access Control and Audit Logging

🤝 Enterprise Support

24/7 Technical Support
Dedicated Account Manager
Custom Integration Support
Training and Onboarding
Regular Updates and Maintenance
Custom Development Services

📞 Contact

Sales Inquiries

Email: [email protected]
Phone: +1 (555) 123-4567
Schedule a Demo

Technical Support

🌟 Why Choose Synthex AI?

Enterprise-Ready: Built for scale and security
Compliance-First: HIPAA and GDPR compliant
Customizable: Tailored to your needs
Support: Enterprise-grade support
Innovation: Cutting-edge AI technology

🚀 Getting Started

Quick Start

# Install Synthex CLI
pip install synthex

# Initialize client
synthex init

# Generate records
synthex generate --type clinical_note --count 10

Docker Deployment

# Pull image
docker pull synthex/synthex:latest

# Run container
docker run -p 8501:8501 synthex/synthex

📚 Documentation

🙏 Acknowledgments

Built with Streamlit
Powered by Hugging Face
Enterprise features by Google Cloud