This article will provide a quick introduction to create & deploy a Machine Learning Model and to integrate with a React application.
Here is an overview of technologies we will be using

Hugging Face is a platform where we can host Machine Learning Models and Create Spaces to use them with any of the models available.

Gradio is a platform which enables to build user interfaces for interacting with or demoing our Machine Learning models.

Create a Space in Hugging Face

Let's start by creating a space of hugging face here. Choose a name for the space and under Spaces SDK choose Gradio

You can leave the other options as default and proceed to create space. Clone the created spaces to your machine and create app.py with the following contents

import gradio as gr  def greet(name):     return "Hello " + name + "!!"  demo = gr.Interface(fn=greet, inputs="text", outputs="text") demo.launch() 

Once these are are done, commit and push your changes to your space. Hugging face should build this automatically and shows the gradio interface of our app like this

Now we have built a gradio app with basic interface and hosted it in hugging face spaces. Let's look into extending the functionality our app in the next section.

Note: You need to create a write token here in order to push your changes to hugging face spaces

Create a Vision Classifier Model

We are going to use fast.ai to create a simple classification which predicts whether a given image is a cat or not.

First, let's get path of the training images dataset. fast.ai provides lot of datasets already, we'll use one from that. Here are the steps to train & export the model

# Read the dataset from fastai  path = untar_data(URLs.PETS)/'images'  dls = ImageDataLoaders.from_name_func(         path,get_image_files(path), valid_pct=0.2, seed=42,         label_func=is_cat, item_tfms=Resize(224))  # Train the model with vision_learner  learn = vision_learner(dls, resnet34, metrics=error_rate)  learn.fine_tune(1)  # Export the model  learn.path = Path('.')  learn.export(  'cats_classifier.pkl')   

After you have run this, there should be a model file generated in the same directory. You can then use this for predictions.

model = load_learner('cats_classifier.pkl')  def predict(image):      img = PILImage.create(image)      _,_,probs = model.predict(img)      return {'Not a Cat':float("{:.2f}".format(probs[0].item())),              'Cat':float("{:.2f}".format(probs[1].item()))}    

Then launch gradio using the predict method above

demo = gr.Interface(fn=predict, inputs=gr.Image(), outputs='label') demo.launch() 

To see your changes in action, push your changes to hugging face.

Note: Since the size of the generated model will be large, you need to use Git LFS to track the changes

Then you should able to upload any image and find if its a cat or not

Integrate with React application

One of the advantages of using gradio is that, it gives us an API to access our model. We will look into how to integrate it with React.

If you scroll to bottom in our deployed model in hugging face, you can see a button called use via Api where you will find the details to connect to the api. We will be using @gradio/client package to connect to api. Here is the sample code to connect to the model & get the results

import { Client } from "@gradio/client";  const response_0 = await fetch("https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png"); const exampleImage = await response_0.blob();  const client = await Client.connect("ganesh1410/basic-classifier"); const result = await client.predict("/predict", {                  image: exampleImage,  });  // This should have the label and confidences console.log(result.data); 

You can see it in action for our model here

Here are the links for all the code & demo

• Demo of Hugging Face with Gradio
• Code used for Hugging Face Model with Gradio
• Demo and Code of React app with our trained model