In this tutorial, we’ll build BodyMetrics Pro v2.0, a desktop BMI Risk Prediction System using:

🐍 Python

🖥️ Tkinter + ttkbootstrap (modern UI)

🤖 Scikit-Learn (Logistic Regression)

📊 Matplotlib (visual BMI chart)

By the end, you’ll have a fully working AI-powered health app.

🔗 Project Repository

Clone the full project here:

https://github.com/rogers-cyber/python-tiny-tools/tree/main/BMI-prediction-system

1️⃣ What This App Does

The app:

Calculates BMI

Uses Machine Learning to predict health risk

Considers Age and Gender

Displays results visually with a BMI chart

Risk levels:

✅ Low Risk

⚠️ Moderate Risk

❌ High Risk

2️⃣ Install Required Libraries

Before coding, install dependencies:

pip install numpy scikit-learn matplotlib ttkbootstrap

3️⃣ Import Required Modules

We start by importing all necessary libraries.

import tkinter as tk
from tkinter import messagebox
import ttkbootstrap as tb
from ttkbootstrap.constants import *

Why?

tkinter → GUI framework

ttkbootstrap → modern UI themes

messagebox → error alerts

Machine Learning & Math Imports

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler

numpy → numerical operations

LogisticRegression → ML classifier

StandardScaler → feature normalization

Charting Library

from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure

This lets us embed charts inside Tkinter.

4️⃣ Building the Machine Learning Model

We encapsulate ML logic inside a class.

class BMIRiskModel:
def __init__(self):
self.scaler = StandardScaler()
self.model = LogisticRegression()
self._train()

Why a class?

Keeps ML logic clean

Easy to reuse and maintain

Training the Model

def _train(self):
X = np.array([
[18, 18, 0], [22, 25, 1], [24, 30, 0],
[27, 40, 1], [29, 45, 0], [32, 50, 1],
[35, 55, 0], [38, 60, 1], [42, 65, 0]
])

Each row contains:

[BMI, Age, Gender]

Gender:

Male = 1

Female = 0

Risk Labels

y = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2])

0 → Low Risk

1 → Moderate Risk

2 → High Risk

Scaling & Training

X_scaled = self.scaler.fit_transform(X)
self.model.fit(X_scaled, y)

Scaling ensures fair ML predictions.

Making Predictions

def predict(self, bmi, age, gender):
X = np.array([[bmi, age, gender]])
X_scaled = self.scaler.transform(X)
return self.model.predict(X_scaled)[0]

5️⃣ Utility Functions BMI Calculation

def calculate_bmi(weight, height):
return round(weight / (height ** 2), 2)

Risk Label Mapping

def risk_label(code):
return {
0: ("Low Risk", "✅"),
1: ("Moderate Risk", "⚠️"),
2: ("High Risk", "❌")
}[code]

6️⃣ Creating the Main Application Window

class BMIPredictorApp:
APP_NAME = "BodyMetrics Pro"
APP_VERSION = "2.0"

Initialize the App

def __init__(self):
self.root = tb.Window(themename="darkly")
self.root.title("BodyMetrics Pro v2.0")
self.root.minsize(720, 620)

self.model = BMIRiskModel()
self._build_ui()

7️⃣ Building the User Interface App Title

tb.Label(
main,
text="🧠 BodyMetrics Pro",
font=("Segoe UI", 22, "bold")
).pack()

Input Fields

self.weight = tb.Entry(form)
self.height = tb.Entry(form)
self.age = tb.Entry(form)

Each input collects user data.

Gender Selection

self.gender = tk.StringVar(value="Male")

tb.Combobox(
form,
values=["Male", "Female"],
textvariable=self.gender
)

8️⃣ Analyze Button & Progress Bar

tb.Button(
main,
text="🚀 Analyze Health Risk",
bootstyle=SUCCESS,
command=self.analyze
).pack()

self.progress = tb.Progressbar(
main,
maximum=100
)

Gives visual feedback during analysis.

9️⃣ BMI Chart Visualization

self.figure = Figure(figsize=(5, 3))
self.ax = self.figure.add_subplot(111)

Updating the Chart

def _update_chart(self, bmi):
self.ax.clear()
self.ax.bar(["Your BMI"], [bmi])
self.ax.axhline(18.5, linestyle="--")
self.ax.axhline(25, linestyle="--")
self.ax.axhline(30, linestyle="--")

These lines show BMI classification zones.

🔟 Processing User Input

def analyze(self):
w = float(self.weight.get())
h = float(self.height.get())
age = int(self.age.get())

Predicting Risk

bmi = calculate_bmi(w, h)
risk_code = self.model.predict(bmi, age, gender)
label, icon = risk_label(risk_code)

Display Result
self.result.config(
text=f"{icon} BMI: {bmi} | {label}",
font=("Segoe UI", 16, "bold")
)

🔚 Running the App

if __name__ == "__main__":
app = BMIPredictorApp()
app.run()

🎉 Final Result

You now have:

✅ A modern desktop GUI

🤖 An ML-based BMI risk predictor

📊 Interactive BMI visualization

🧩 Clean, modular Python code