metadata

title: GlycoAI - AI-Powered Glucose Insights
emoji: 🩺
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: apache-2.0
tags:
  - agent-demo-track
  - diabetes
  - glucose-monitoring
  - healthcare-ai
  - medical-analysis
  - dexcom-api
  - mistral-ai
  - gradio
  - demo

GlycoAI 🩺 - AI-Powered Glucose Insights

Transform your glucose data into actionable health insights with intelligent AI analysis

🌟 Overview

GlycoAI is an advanced AI-powered application that analyzes continuous glucose monitoring (CGM) data to provide personalized diabetes management insights. Using state-of-the-art AI agents powered by Mistral AI, GlycoAI transforms complex glucose patterns into clear, actionable recommendations for better diabetes control. 🎯 Video demo: deleted

🎯 Key Features

🤖 Intelligent AI Agent: Conversational AI that understands glucose patterns and provides personalized insights
📊 Comprehensive Analysis: 14-day glucose trend analysis with clinical metrics (Time in Range, GMI, CV)
🎭 Demo Users: Four realistic patient profiles showcasing different glucose management scenarios
🔐 Dexcom Integration: OAuth-authenticated connection to Dexcom Sandbox API
📈 Interactive Visualizations: Color-coded glucose charts with target range overlays
⚠️ Smart Notifications: Real-time alerts for concerning glucose patterns
🏥 Clinical Focus: Evidence-based recommendations aligned with diabetes care standards

🚀 Live Demo

Try GlycoAI now: https://huggingface.co/spaces/your-username/glycoai

🎭 Demo Users Available

Sarah Thompson - G7 Mobile - ⚠️ Unstable Control (Demonstrates crisis management)
Marcus Rodriguez - ONE+ Mobile - Type 2 Diabetes with Dawn Phenomenon
Jennifer Chen - G6 Mobile - Athletic lifestyle with excellent control
Robert Williams - G6 Receiver - Experienced user with good management

🛠️ Technology Stack

Frontend: Gradio 4.44.0 with custom CSS styling
AI Engine: Mistral AI for intelligent glucose pattern analysis
Data Processing: Pandas, NumPy for glucose data analysis
Visualization: Plotly for interactive glucose charts
API Integration: Dexcom API with OAuth 2.0 authentication
Deployment: Hugging Face Spaces

🏥 Clinical Significance

Metrics Analyzed

Time in Range (TIR): Target >70% (70-180 mg/dL)
Time Below Range (TBR): Target <4% (<70 mg/dL)
Time Above Range (TAR): Target <25% (>180 mg/dL)
Glucose Management Indicator (GMI): Estimated A1C
Coefficient of Variation (CV): Target <36% (glucose variability)

AI Capabilities

Pattern Recognition: Identifies dawn phenomenon, post-meal spikes, nocturnal hypoglycemia
Safety Prioritization: Emphasizes hypoglycemia prevention and severe glucose excursions
Personalized Recommendations: Tailored advice based on individual glucose patterns
Clinical Context: Provides education on diabetes management principles

🔧 Installation & Setup

For Local Development

# Clone the repository
git clone https://github.com/your-username/glycoai.git
cd glycoai

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your API keys:
# MISTRAL_API_KEY=your_mistral_api_key_here
# DEXCOM_CLIENT_ID=your_dexcom_client_id (optional)
# DEXCOM_CLIENT_SECRET=your_dexcom_client_secret (optional)

# Run the application
python app.py

Environment Variables

Variable	Description	Required
`MISTRAL_API_KEY`	Mistral AI API key for chat functionality	✅ Yes
`DEXCOM_CLIENT_ID`	Dexcom developer client ID	❌ Optional
`DEXCOM_CLIENT_SECRET`	Dexcom developer client secret	❌ Optional

📖 Usage Guide

1. Select Data Source

Choose from 4 demo users for instant testing
Or connect via Dexcom Sandbox OAuth (requires developer credentials)

2. Load Glucose Data

Click "Load 14-Day Glucose Data" button
Watch for notification indicating data quality and patterns

3. Analyze with AI

Navigate to "Chat with AI" tab
Click on suggested prompts or ask custom questions
Get personalized insights about glucose patterns

4. Explore Visualizations

View interactive 14-day glucose trends
Examine detailed statistics and clinical metrics
Understand time-in-range analysis

🎯 Use Cases

For Healthcare Providers

Patient Education: Explain glucose patterns in accessible language
Treatment Planning: Identify areas for intervention
Progress Monitoring: Track improvement over time
Clinical Documentation: Generate insights for medical records

For Patients & Caregivers

Self-Management: Understand personal glucose patterns
Medication Timing: Optimize treatment schedules
Lifestyle Adjustments: Learn about food and exercise impacts
Safety Awareness: Recognize dangerous patterns

For Researchers & Developers

Algorithm Development: Study glucose pattern recognition
AI Applications: Explore conversational health AI
Data Analysis: Understand CGM data processing
Clinical Decision Support: Build evidence-based tools

🔬 Technical Details

Data Processing Pipeline

Data Ingestion: Accepts Dexcom API format or generates realistic mock data
Preprocessing: Validates timestamps, handles missing values, calculates trends
Statistical Analysis: Computes clinical metrics using standardized formulas
Pattern Recognition: Identifies glucose variability, meal responses, and anomalies
AI Context Building: Structures data for intelligent conversation

AI Agent Architecture

Context Awareness: Maintains conversation state with glucose data context
Clinical Knowledge: Trained on diabetes management best practices
Safety Focus: Prioritizes urgent recommendations for dangerous patterns
Personalization: Adapts advice to individual glucose characteristics

📊 Demo Scenarios

Sarah Thompson - Crisis Management

Scenario: Highly unstable glucose with frequent dangerous excursions
TIR: ~45% (concerning)
CV: ~52% (very high variability)
AI Response: Urgent safety recommendations and healthcare provider consultation

Marcus Rodriguez - Dawn Phenomenon

Scenario: Type 2 diabetes with morning glucose elevation
Pattern: Consistent 6-8 AM glucose rises
AI Response: Medication timing optimization and morning routine adjustments

Jennifer Chen - Athletic Lifestyle

Scenario: Active individual with exercise-related glucose variations
Pattern: Exercise-induced lows and recovery patterns
AI Response: Pre/post-workout glucose management strategies

Robert Williams - Experienced Management

Scenario: Long-term diabetes with good overall control
Focus: Fine-tuning and maintaining excellent management
AI Response: Advanced optimization strategies and pattern maintenance

🛡️ Privacy & Security

Data Processing: All analysis performed in real-time, no permanent storage
API Security: OAuth 2.0 authentication for Dexcom integration
Privacy by Design: No personal health information retained between sessions
Compliance: Designed with HIPAA principles in mind
Transparency: Open-source approach for algorithm audibility

⚠️ Medical Disclaimer

IMPORTANT: GlycoAI is for informational and educational purposes only. This application:

IS NOT a medical device or diagnostic tool
DOES NOT replace professional medical advice
SHOULD NOT be used for treatment decisions without healthcare provider consultation
REQUIRES users to always consult their healthcare team before making management changes

Always follow your healthcare provider's guidance for diabetes management.

🤝 Contributing

We welcome contributions from the healthcare AI, diabetes technology, and open-source communities!

Ways to Contribute

🐛 Bug Reports: Submit issues with detailed reproduction steps
💡 Feature Requests: Suggest new capabilities or improvements
🔧 Code Contributions: Submit pull requests with enhancements
📚 Documentation: Improve guides, examples, and explanations
🧪 Testing: Help validate algorithms with diverse glucose patterns

Development Guidelines

Follow clinical evidence-based recommendations
Prioritize patient safety in all features
Maintain code quality with comprehensive testing
Document clinical rationale for algorithm decisions

📜 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Copyright 2024 GlycoAI Contributors

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

🙏 Acknowledgments

Mistral AI for providing the intelligent conversation capabilities
Dexcom for continuous glucose monitoring technology and API access
Diabetes Community for inspiration and clinical insights
Open Source Community for tools and frameworks that make this possible
Healthcare Providers who guide evidence-based diabetes management

📞 Support & Contact

Issues: GitHub Issues
Discussions: GitHub Discussions
Documentation: Project Wiki
Email: your-email@example.com

🚀 Roadmap

Upcoming Features

Multi-language Support: Expand accessibility globally
Advanced Pattern Recognition: Machine learning-based anomaly detection
Integration Expansion: Support for additional CGM devices
Clinical Decision Support: Enhanced recommendations for healthcare providers
Mobile Optimization: Improved mobile device experience
API Development: RESTful API for third-party integrations

Research Directions

Federated Learning: Privacy-preserving model improvements
Predictive Analytics: Glucose forecasting capabilities
Behavioral Analysis: Lifestyle factor correlation
Population Health: Aggregate insights for public health

Made with ❤️ for the diabetes community

Empowering better glucose management through intelligent AI analysis