AICTE OASIS Infobyte Internship Project Submission for Idea.2 by Annika Dubey

I've successfully completed the development of Idea 2 project under the AICTE OASIS Infobyte Internship for April 2026 — and honestly, this one pushed me further than I expected.

The project: a full-stack BMI Calculator. But not the kind where you plug in numbers and get a single output. I built BMI Pro — a Precision Wellness Tracker that runs a proper scientific analysis engine on the backend.

𝗪𝗵𝗮𝘁 𝗜 𝗯𝘂𝗶𝗹𝘁:

The app takes your weight, height, age, gender, activity level, and waist measurement — and runs them through a suite of clinically validated formulas:

→ BMI using the WHO standard (9 classification tiers, not just 4)
→ Body Fat % via 3 formulas — Deurenberg (1991), Jackson-Pollock (1978), and CUN-BAE — averaged into an ensemble with a 95% confidence interval
→ Ideal Body Weight across 4 clinical equations — Devine, Robinson, Miller, and Hamwi
→ BMR calculated 3 ways — Mifflin-St Jeor, Harris-Benedict, and Katch-McArdle (which uses lean body mass, not just total weight)
→ TDEE with 6 personalised calorie targets, from fat loss to muscle gain
→ Waist-to-Height Ratio for cardiometabolic risk — which research shows is actually a better heart disease predictor than BMI alone
→ Full statistical trend analysis using SciPy linear regression — slope, R², p-value, moving average, z-score, and a predicted next-entry BMI

𝗧𝗵𝗲 𝘀𝘁𝗮𝗰𝗸:
Python handles all computation via NumPy and SciPy. Flask serves a REST API with 7 endpoints. The frontend is a fully custom animated HTML/CSS interface — glassmorphism cards, live sliders, a canvas gauge, and Chart.js trend graphs. History is persisted to JSON and survives between sessions.

𝗪𝗵𝗮𝘁 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱:

Honestly, the biggest shift for me was understanding why you don't trust a single formula. Every equation was developed in a different decade, on a different population sample, for a different clinical purpose. Using an ensemble and reporting uncertainty (confidence intervals, standard deviation) is how real science works — and building that into an app made me think very differently about what "accuracy" means in software.

I also learned how to structure a full-stack Python app from scratch — Flask routing, CORS, REST design, and making the frontend talk cleanly to the backend. And debugging Windows path issues at midnight is apparently also part of the curriculum 😅

This internship has genuinely made me more confident in bridging data science and web development — two worlds that feel separate until you build something that needs both.

Thank you OASIS Infobyte and AICTE for the opportunity!

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