Diabetes Predictor with FastAPI and Scikit-Learn
30 min readDec 31, 2020
As COVID-19 continues to rage on, there is a continuous need for the application of Machine Learning in Health. Health is a crucial part of our lives that can be significantly improved with the application of machine learning. Some application areas in health include;
- Disease Diagnosis
2. Drug Discovery and Manufacturing
3. Personalized Treatment
4. Smart health recording
5. Outbreak prediction and containment
and many others.
I built a model a while ago that predicts Diabetes using an open-source dataset and so I decided to create a web app based on this model. The web app will take in the user’s Age, Glucose level, and BMI to predict the possibility of being diagnosed with Diabetes. Please note this is not meant to be a professional medical diagnosis but can certainly be improved to do so. It is just for learning purposes only.
Setup
Let’s begin our app by creating a virtual environment. I will use conda from Anaconda since it is easy to setup. Run the following command on your terminal if you have conda or anaconda installed.
# create env
conda create --name diabetes
# activate it
conda activate diabetesThen install the dependencies from the requirements.txt file:
pip install -r requirements.textapp.py
Next, we will create our app. Our project folder structure will look like the following;
.
├── app.py
├── diabetes.sav
├── README.md
├── requirements.txt
└── template
└── index.htmlYou will realize that I also added a diabetes.sav file which is my saved model from scikit-learn . This file and the code for this project can be found here.
The code for the app.py file will look like the following:
import pickle
from fastapi import FastAPI
from starlette.requests import Request
from fastapi.templating import Jinja2Templates
import numpy as np
app = FastAPI()
templates = Jinja2Templates(directory="template")
# the filename of the saved model
filename = 'diabetes.sav'
# load the saved model
loaded_model = pickle.load(open(filename, 'rb'))
@app.get("/")
def home(request: Request):
return templates.TemplateResponse("index.html", {"request": request})
@app.post('/predict')
async def predict(request: Request):
result = {}
if request.method == "POST":
# get the features to predict
form = await request.form()
# form data
age = form["Age"]
glucose = form["Glucose"]
bmi = form["BMI"]
# create the features list for prediction
features_list = [int(glucose), int(bmi), int(age)]
# get the prediction class
prediction = loaded_model.predict([features_list])
# get the prediction probabilities
confidence = loaded_model.predict_proba([features_list])
# formulate the response to return to client
result['prediction'] = int(prediction[0])
result['confidence'] = str(round(np.amax(confidence[0]) * 100 ,2))
return templates.TemplateResponse("index.html", {"request": request, "result": result})In the file above, we first import our libraries and then create an instance of FasAPI, the web framework we are using to build our app. We then creates a templates variable that points to our template file. After that, we create a variable(filename) that points to our saved model file.
We then create two routes; a get route that serves as our home routes and then displays our form that will take the user's inputs. The post route is where our form processing happen. In this route, we first create an empty dictionary that will contain our prediction. We then make sure that our request method is post which is required for form data. Then we extract the inputs into a list we can feed to our model by calling the .predict method on it. We also get the prediction probability by using the .predict_prob method.
Finally, we render the result in our index.html file.
index.html
This file will look like the following:
<!DOCTYPE html>
<html>
<head>
<!--Import Google Icon Font-->
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<meta http-equiv="X-UA-Compatible" content="ie=edge" />
<!-- Compiled and minified CSS -->
<link
href="https://unpkg.com/tailwindcss@^2/dist/tailwind.min.css"
rel="stylesheet"
/>
<title>Diabetes Predictor</title>
<style>
body {
background-color: #dfdbe5;
background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='360' height='360' viewBox='0 0 360 360'%3E%3Cpath fill='%239C92AC' fill-opacity='0.4' d='M0 85.02l4.62-4.27a49.09 49.09 0 0 0 7.33 3.74l-1.2 10.24 2.66.87 5.05-9c2.62.65 5.34 1.08 8.12 1.28L28.6 98h2.8l2.02-10.12c2.74-.2 5.46-.62 8.12-1.28l5.05 8.99 2.66-.86-1.2-10.24c2.55-1.03 5-2.29 7.33-3.74l7.58 7 2.26-1.65-4.3-9.38a48.3 48.3 0 0 0 5.8-5.8l9.38 4.3 1.65-2.26-7-7.58a49.09 49.09 0 0 0 3.74-7.33l10.24 1.2.87-2.66-9-5.05a48.07 48.07 0 0 0 1.28-8.12L88 41.4v-2.8l-10.12-2.02c-.2-2.74-.62-5.46-1.28-8.12l8.99-5.05-.86-2.66-10.24 1.2c-1.03-2.55-2.29-5-3.74-7.33l7-7.58-1.65-2.26-9.38 4.3a48.3 48.3 0 0 0-5.8-5.8L62.42 0h2.16l-1.25 2.72a50.31 50.31 0 0 1 3.95 3.95l9.5-4.36 3.52 4.85-7.08 7.68c.94 1.6 1.79 3.27 2.54 4.98l10.38-1.21 1.85 5.7-9.11 5.12c.39 1.8.68 3.65.87 5.52L90 37v6l-10.25 2.05a49.9 49.9 0 0 1-.87 5.52l9.11 5.12-1.85 5.7-10.38-1.21c-.75 1.7-1.6 3.37-2.54 4.98l7.08 7.68-3.52 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display: flex;
height: 100vh;
justify-content: center;
align-items: center;
}
</style>
</head>
<body>
<section class="ml-auto text-gray-700 body-font">
<div class="container flex flex-wrap items-center px-5 py-24 mx-auto">
<div class="pr-0 md:w-1/2 md:pr-16 lg:pr-0">
<h1
class="mt-24 text-5xl font-medium font-bold text-gray-600 lg:mt-0"
>
Diabetes Predictor
</h1>
<p class="mt-4 text-xl font-medium leading-relaxed text-gray-600">
Predict the likelihood of being diagnosed
with Diabetes by providing
your Age, BMI and Glucose level
</p>
<div class="mt-10 mr-4">
{% if result %}
<div class="">
{% if result.prediction == 1 %}
<p class="text-5xl font-bold text-gray-500">
You are <span class="text-indigo-500">Diabetic</span>
</p>
{% else %}
<p class="text-5xl font-bold text-gray-500">
You are <span class="text-pink-500">Not Diabetic</span>
</p>
{% endif %}
<p class="mt-4 text-lg font-bold text-gray-600">
The confidence Level is <span class="text-pink-500">{{result.confidence}}% </span>
</p>
</div>
{% endif %}
</div>
</div>
<form method="post" action="/predict">
<div
class="flex flex-col w-full p-8 mt-10 text-xl bg-gray-800 rounded-lg lg:w-5/6 md:w-1/2 md:ml-auto md:mt-0"
>
<div class="relative mb-4">
<label for="Age" class="text-sm font-bold leading-7 text-gray-100"
>Age(years)</label
>
<input
type="number"
id="Age"
name="Age"
required
class="w-full px-3 py-1 text-base leading-8 text-gray-700 transition-colors duration-200 ease-in-out bg-white border border-gray-300 rounded outline-none focus:border-indigo-500"
/>
</div>
<div class="relative mb-4">
<label
for="glucose"
class="text-sm font-bold leading-7 text-gray-100"
>Glucose Level(mg/dL)</label
>
<input
type="number"
id="Glucose"
name="Glucose"
required
class="w-full px-3 py-1 text-base leading-8 text-gray-700 transition-colors duration-200 ease-in-out bg-white border border-gray-300 rounded outline-none focus:border-indigo-500"
/>
</div>
<div class="relative mb-4">
<label for="BMI" class="text-sm font-bold leading-7 text-gray-100"
>BMI(lbs)</label
>
<input
type="number"
id="BMI"
name="BMI"
required
class="w-full px-3 py-1 text-base leading-8 text-gray-700 transition-colors duration-200 ease-in-out bg-white border border-gray-300 rounded outline-none focus:border-indigo-500"
/>
</div>
<input
class="px-8 py-2 text-lg text-white bg-pink-500 border-0 rounded focus:outline-none hover:bg-pink-600"
type="submit"
value="Predict"
/>
</div>
</form>
<br />
</div>
</section>
<script src="https://cdn.jsdelivr.net/npm/sweetalert2@10"></script>
<script>
Swal.fire(
"Please Note!",
"This is for learning purposes and is not meant to be a qualified medical diagnosis. The model is not super accurate!",
"error"
);
</script>
</body>
</html>
We are using Tailwindcss; a cool utility-first CSS library that makes building websites fast and we are also using Heropatterns for the cool background.
Our logic in the template is simple. We first check to see if there is a result and then we check to see if the result’s prediction is equal to 1. If it is equal to 1, we say the user is Diabetic. Otherwise, he is not. We are using Jinja2 template engine.
The line below used a SweetAlert2 to display a disclaimer about the app.
Swal.fire(
"Please Note!",
"This is for learning purposes and is not meant to be a qualified medical diagnosis. The model is not super accurate!",
"error"
);Let’s finally run the app by adding the following to the app.py
# app.py
# import uvicorn at the top of app.py
import uvicorn
# then add the following to the bottom of app.py
if __name__ == "__main__":
uvicorn.run("app:app", host="127.0.0.1", port=8000, reload=True)Then finally run the app with:
python app.pyIf all goes well, you should see the following;
Conclusion
Machine Learning has come to stay and has enormous benefits for us today. It can significantly save the time it takes to diagnose simple diseases like diabetes. This application has just shown you how to do that. There so many other areas in health we can apply machine learning. I hope you are inspired to build your ml app.
