Creating a web application powered by a fastai model

Aakanksha NS
USF-Data Science
Published in
4 min readAug 18, 2019


using React, Flask,, and Firebase

Developing an end-to-end Machine Learning based web-app can appear quite daunting. However, with libraries like fastai, training models has become a lot easier compared to the past. Deployment takes less than ten minutes with sites like and Firebase.

This article describes a simple, step by step approach to building a simple flower classification app. It takes an image input and returns the predicted flower category along with the probabilities of each of the classes. Here’s a live demo of the application I’ve built:


I’ve used the flower recognition dataset from Kaggle:

It consists of 700–1000 images each of five types of flowers: daisy, dandelion, rose, sunflower, and tulip. If you wish to construct your own dataset using Google images follow the steps mentioned in the following article:

Let’s begin building our web app!

Step 1: Train your model

I’ve explained in detail how to train a model using fastai through transfer learning in the following article:

After training, the accuracy of my model was 94.4%

Step 2: Export trained model

Once we have trained our model, to put our model in production we export the minimal state of our Learner. A PKL file is created using pickle, a Python module that enables objects to be serialized to files on disk and deserialized back into the program at runtime.


Once exported, download the trained_model.pkl file.

Step 3: Create a REST API using Flask

Flask is a popular Python web framework. To load our model, we use the load_learner function from fastai.basic_train module to load the .pkl file we exported in step 2. We open our image obtained from the request using open_image from module and then call the predict function over the image to get our prediction. We then return this result in the form of a JSON object. The following code goes into our file:

Additionally for our server to know what dependencies to install we would need a requirements.txt file that consists of:


Step 4: Deploy the API on

For this step, we’ll require our, requiements.txt and trained_model.pkl files to be on a Github repo. Once we have pushed our code into a GitHub repo (let’s call it flower_classifier), we do the following:

  1. Go to and login/sign up.
  2. Go to services -> New web service.
  3. Connect the flower_classifier GitHub repo to Render
  4. Use the following values during creation:

Make sure you verify that the service is functioning the way it is supposed to once it’s deployed. I’ve used Postman to verify this:

Step 5: Build UI

I’ve used ReactJs to build my frontend and a fetch API call to get my prediction. You can check out my code here:

Step 6: Deploy web-app on Firebase

We’re almost done! The final and simplest step is to deploy our web-app on Firebase. To do this we first create a production build using:

npm run build


  1. Go to -> login/sign up
  2. Go to Console->Add Project
  3. Enter the following commands on the command line:
$ npm install -g firebase-tools 
$ firebase login
$ firebase init

4. Select ‘Hosting’ when prompted

5. Deploy using:

$ firebase deploy

And we’re done!

Few issues that I ran into:

1)I tried deploying the API using Heroku instead of Render (because it’s free), however because of the dependencies being too large I faced the following error:

Compiled slug size: 997.6M is too large (max is 500M)

2)I also came across a CORS related issue while trying to call the API from my local machine while developing the UI. I handled it using flask_cors.

No ‘Access-Control-Allow-Origin’ header is present on the requested resource



Aakanksha NS
USF-Data Science

ML Engineer @ Snap Inc. | MSDS University of San Francisco | CSE NIT Calicut