🩺 GlucoSense — Diabetes Risk Analyzer

A modern, AI-powered web application that predicts diabetes risk from clinical parameters. Built with Streamlit and powered by a FastAPI machine learning backend.

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✨ Features

• Instant Predictions — Submit 8 biomarkers and get a result in ~1 second
• Dark Clinical UI — Custom CSS dark theme with teal accents and animated result cards
• Risk Visualization — Animated risk meter bar clearly communicates low vs high risk
• Input Summary — Expandable panel showing all submitted parameters after prediction
• Responsive Layout — Two-column card layout that works on desktop and mobile

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🖥️ Demo

Non-Diabetic Result	Diabetic Risk Detected
✅ Green result card + balloon animation	⚠️ Red/amber result card with high-risk meter

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🚀 Getting Started

Prerequisites

• Python 3.8+
• pip

Installation

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

# 2. Install dependencies
pip install streamlit requests

# 3. Run the app
streamlit run diabetes_app.py

The app will open automatically at http://localhost:8501

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🔌 API

The app connects to a hosted FastAPI prediction endpoint:

POST https://diabetes-prediction-api-ekdt.onrender.com/predict

Request Body

{
  "Pregnancies": 2,
  "Glucose": 120,
  "BloodPressure": 70,
  "SkinThickness": 20,
  "Insulin": 80,
  "BMI": 28.5,
  "DiabetesPedigreeFunction": 0.5,
  "Age": 33
}

Response

{
  "predicted category ": "Non Diabetic"
}

Possible values: "Non Diabetic" or "Diabetic"

Note: The API is hosted on Render's free tier and may take 30–60 seconds to wake up on the first request after a period of inactivity.

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📊 Input Parameters

Parameter	Unit	Range	Description
Pregnancies	count	0 – 20	Number of times pregnant
Glucose	mg/dL	1 – 300	Plasma glucose (2-hr OGTT)
Blood Pressure	mm Hg	1 – 200	Diastolic blood pressure
Skin Thickness	mm	0 – 100	Triceps skin fold thickness
Insulin	mu U/ml	0 – 900	2-hour serum insulin
BMI	kg/m²	0.1 – 49.9	Body mass index
Pedigree Function	score	0.0 – 2.5	Diabetes family history score
Age	years	1 – 119	Age of patient

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🗂️ Project Structure

glucosense/
│
├── diabetes_app.py     # Main Streamlit application
└── README.md           # Project documentation

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🛠️ Tech Stack

Layer	Technology
Frontend	Streamlit + custom CSS
Fonts	DM Serif Display, DM Sans (Google Fonts)
HTTP Client	Python requests
ML Backend	FastAPI (hosted on Render)

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⚠️ Disclaimer

This application is intended for educational and informational purposes only. It is not a substitute for professional medical advice, diagnosis, or treatment. Always consult a qualified healthcare professional regarding any medical concerns.

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📄 License

This project is open source and available under the MIT License.