ADAPTY Body Composition
A FastAPI and Streamlit MVP that turns repeated body-composition assessments into goal-specific progress summaries. It tracks measurements over time and generates separate reports for coaches and clients using synthetic demonstration data.
The product, shown without personal or sensitive data
What is implemented today
- Follow repeated body-composition measurements against an individual baseline.
- Interpret progress according to fat-loss, muscle-gain or recomposition goals.
- Generate technical coach reports and plain-language client reports.
Context
Repeated assessments often remain in separate reports, making it difficult to follow progress toward a specific goal.
Problem
A change in weight or body composition needs a baseline, a goal and enough follow-up to support interpretation.
My role
Domain framing, API and dashboard development, progress rules and reporting.
What I built
Client, measurement, goal and plan workflows; first-to-latest progress comparisons; goal-aware classifications for fat loss, muscle gain and recomposition; technical coach reports and plain-language client reports.
Data or evidence used
Public application code, synthetic seed records, progress logic, tests and API documentation in the GitHub repository.
Limitations
This public MVP does not include a live deployment, clinical validation, population benchmarks or a trained prediction model. Fewer than two records produces an insufficient-data result. Real-data deployment requires controls beyond the public demonstration.
Next validation step
Test the interpretation of progress summaries with practitioners, document measurement uncertainty and validate the rules before expanding decision support.