Gender Predictor using FastAPI and Tailwindcss
Published in
15 min readDec 8, 2020
Building a model without putting it in production is like standing in the dark with a pretty face. No one will see you enough to appreciate you. Besides, no one makes money or solve real-world problems with AI by just building models. That is why we must put models in production so people can use them to solve their problems.
There are, however, many ways of putting models in production but in this article, we will use FastAPI; a super cool, super fast Python web framework. FastAPI is easy to use just like Flask. It is production-ready and has support for a lot of things out-of-the-box like authentication, database, WebSocket, documentation, and many more. Trust me, you will love it. FastAPI is built on top Starllete; another super cool Python web framework.
We are going to use FastAPI, Scikit-learn, and Tailwindcss; a utility first UI library to build a gender predictor. I will not go into the details of building the model but will include a Jupyter notebook and a link to the saved model for your use. The notebook is available here. Now let’s get started.
Setup
Step 1. Create a virtualenv using anaconda (or any other you prefer) with the following commands;
conda create --name gender
conda activate gender # activate the virtualenv
# install dependencies
pip install sklearn
pip install fastapi[all]
pip install joblibStep 2. Create a directory where your project will be and add the following files and directory. Your folder should look like this. You can ignore __pycache__
.
├── app.py
├── gender_model.joblib
├── __pycache__
│ └── app.cpython-37.pyc
└── templates
└── index.html
2 directories, 4 files
You can find the model here
step 3. Let’s add content to the app.py file. This is the entry point to our app.
from fastapi import FastAPI
from starlette.requests import Request
from fastapi.templating import Jinja2Templates
from joblib import load
app = FastAPI()
templates = Jinja2Templates(directory="templates")
@app.get("/")
def home(request: Request):
return templates.TemplateResponse("index.html", {"request": request})
@app.post("/")
async def home(request: Request):
result = ""
if request.method == "POST":
form = await request.form()
if form["height"] and form["weight"]:
height = form["height"]
weight = form["weight"]
result = predict(int(height), int(weight))
return templates.TemplateResponse("index.html", {"request": request, "result":result})
def predict(a, b):
clf = load('gender_model.joblib')
prediction = clf.predict([(a, b)])
if prediction[0]:
result = "Male"
else:
result = "Female"
return result
So we started by importing our dependencies. Then we created an instance of FastAPI and add our template directory. Then we created two routes; a get and a post route. The first get route renders out the form and the second route preprocessing our form data and outputs our prediction. Note that we are using the Request module from Starllete. This uses the Python Request library for handling request and responses. Finally, created a function that takes in 2 parameters(height and weight), loads our model, makes a prediction, and returns the prediction label.
the result is sent to out post route and eventually sent to our index.html file. The content of 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>Gender Predictor</title>
<style>
body {
background-color: #e9e5ef;
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display: flex;
height: 100vh;
justify-content: center;
align-items: center;
}
</style>
</head>
<body>
<section class="text-gray-700 body-font">
<div class="container px-5 py-24 mx-auto flex flex-wrap items-center">
<div class="lg:w-2/5 md:w-1/2 md:pr-16 lg:pr-0 pr-0">
<h1
class="font-bold mt-24 lg:mt-0 font-medium text-5xl text-gray-600"
>
Gender Predictor
</h1>
<p class="leading-relaxed mt-4 text-xl text-gray-500">
Predict your gender by providing your Height and Weight.
</p>
</div>
<form method="post" action="">
<div
class="lg:w-5/6 md:w-1/2 bg-gray-800 rounded-lg p-8 flex flex-col text-xl md:ml-auto w-full mt-10 md:mt-0"
>
<div class="relative mb-4">
<label
for="height"
class="leading-7 font-bold text-sm text-gray-100"
>Height(Inches)</label
>
<input
type="number"
id="height"
name="height"
class="w-full bg-white rounded border border-gray-300 focus:border-indigo-500 text-base outline-none text-gray-700 py-1 px-3 leading-8 transition-colors duration-200 ease-in-out"
/>
</div>
<div class="relative mb-4">
<label
for="weight"
class="leading-7 text-sm font-bold text-gray-100"
>Weight(lbs)</label
>
<input
type="number"
id="weight"
name="weight"
class="w-full bg-white rounded border border-gray-300 focus:border-indigo-500 text-base outline-none text-gray-700 py-1 px-3 leading-8 transition-colors duration-200 ease-in-out"
/>
</div>
<input
class="text-white bg-indigo-500 border-0 py-2 px-8 focus:outline-none hover:bg-indigo-600 rounded text-lg"
type="submit"
value="Predict"
/>
</div>
</form>
<br />
<div class="mt-10 mr-4">
<div class="">
{% if result == "Male" %}
<p class="text-5xl text-gray-500 font-bold">
The person is a <span class="text-indigo-500">{{result}}</span>
</p>
{% else %}
<p class="text-5xl text-gray-500 font-bold">
The person is a <span class="text-pink-500">{{result}}</span>
</p>
{% endif %}
</div>
</div>
</div>
</section>
</body>
</html>
We are using Tailwindcss as the UI library of choice because it’s cool and there are a lot of components available on Tailblocks for Tailwindcss that you can use for your apps. Since FasAPI uses Jinja2; the same templating engine used by Flask; it’s easy to output our result and create conditions.
Step 4. We can run the app by running the following command on our terminal if we have uvicorn installed locally;
uvicorn app:app --reload # using the command line
# make sure to install uvicorn first
# you can also run the app by addingthe following to the app.py file
import uvicornif __name__=="__main__":
uvicorn.run("gender_model_fastapi:app", reload=True)
The output should be something like this;
That is how easy it to deploy a Scikit-learn model in production with FastAPI. I believe FastAPI is going to be an important framework for Data Scientist and Web Developers alike to use for deploying machine learning models and building full-stack application and APIs with Python.
