FitAI – Smart Health & Fitness Assistant

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  landing page

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  key features seaction

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  login page

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  Dashboard

Inspiration

Modern lifestyle health challenges Sedentary jobs and processed foods Global obesity epidemic motivation Need for accessible AI-powered health guidance

What it does

AI-Powered Weight Prediction using ML algorithms Smart Food Recognition with computer vision Personalized Diet Plans (rule-based + ML-driven) Health Risk Assessment (heart & lung disease) Intelligent Workout Generation Comprehensive User Dashboard Social Authentication (Google OAuth)

How we built it

Backend Architecture: Django + ML pipeline AI/ML Implementation: Scikit-learn, Hugging Face, PyTorch Computer Vision: Food recognition with pre-trained models Frontend: Responsive Tailwind CSS design Database: SQLite with Django ORM

Challenges we ran into

Technical: Model accuracy, API integration, scalability Development: Complex workflows, real-time processing, cross-browser compatibility Security: API key management, OAuth implementation

Accomplishments that we're proud of

Accurate AI predictions with realistic results Comprehensive feature set in limited time User-centric design simplifying complex data Proper security implementation Scalable modular architecture Real-world health impact potential

What we learned

Technical: Advanced Django, ML deployment, computer vision Project Management: Agile development, code quality, UX design Problem Solving: Creative solutions to complex challenges

What's next for FitAI – Smart Health & Fitness Assistant

Immediate: Mobile apps, wearable integration, advanced analytics Long-term: Telemedicine, IoT, advanced AI, global expansion

Built With

• ai/ml
• allauth
• backend:-django-5.2.6
• css3
• django
• fooddata
• git
• gunicorn
• heroku
• html5
• hugging-face-transformers
• javascript
• joblib
• numpy
• pandas
• pip
• python-3.12.6
• pytorch
• scikit-learn
• sqlite
• tailwind-css
• transformers
• usda
• virtualenv
• vs-code
• whitenoise