Build an Interactive Iris Classifier App with Python and Amber

Build an Interactive Iris Classifier App with Python and Amber

Table of Contents

1. Introduction
2. Building the User Interface
3. Writing Client-Side Python Code
4. Connecting the App to the Notebook
5. Deploying the App
6. Hosting the Model Online
7. Conclusion

Introduction 🌟 Building an Interactive Iris Classifier App with Python and Amber

The field of data science offers various opportunities to solve real-world problems using machine learning algorithms. One such problem is classifying iris flowers based on their measurements. In this Tutorial, we will go through the process of building an interactive web app, entirely in Python, using Amber. This app will allow users to classify iris flowers themselves, making predictions without any need for HTML, CSS, JavaScript, or web hosting knowledge. Let's get started!

Building the User Interface ✨ Creating the User Interface in Amber

To classify the species of an iris flower, we need to Collect several measurements from the user. In the first step, we will design the user interface for collecting this data. We'll use the Amber editor to drag and drop components from the toolbox and construct the UI. This includes adding cards, labels, and text boxes for each parameter we need. With a user-friendly interface in place, users can easily input the sepal length, sepal width, Petal length, and petal width of the iris flower they want to classify.

Writing Client-Side Python Code 🚀 Dictating User Interface Behavior with Client-Side Python Code

To make the user interface interactive, we'll write client-side Python code. This code will dictate the behavior of our user interface components. In this step, we will focus on the categorize button component. When the user clicks this button, we want to trigger a Python function that sends the iris measurements to our Collab notebook for prediction. By utilizing the click event and the Amber server.callable decorator, we can create a function that will be called each time the button is clicked. This function will be responsible for calling the predict Iris function in our collab notebook and displaying the results.

Connecting the App to the Notebook 🔗 Establishing Connection between the App and the Notebook

To connect our web app to our collab notebook, we will use the Anvil Uplink. This powerful tool allows us to connect our app to Python code anywhere on the web. By enabling the server Uplink and obtaining the Uplink key, we can establish a connection between our app and the notebook. This key will be used in our collab notebook to establish the connection. With the connection established, we can proceed to write the predict Iris function in our notebook, which will take the iris measurements collected from the app and return the predicted species.

Deploying the App 🚀 Deploying the Web App in Two Clicks

In this step, we will deploy our app to make it accessible to others. Anvil makes it incredibly easy to deploy our app with just two clicks. By selecting the publish button and choosing the option to publish the app, we can obtain a public URL for our app. This enables anyone to access and use our iris classifier web app. With a few simple clicks, our app is live and ready to go!

Hosting the Model Online 🌐 Hosting Our Model Online with Anvil

While our app is now accessible online, there is still one problem. Collab notebooks have time limitations and may shut down after a certain period. To solve this issue and ensure continuous availability of our app, we can host our model online with Anvil. By uploading our model as a data file and configuring the server environment, we can ensure that our model is always accessible. This step may require a paid account, but it simplifies the process of hosting both our app and our model online.

Conclusion 💡 Wrapping Up and Next Steps

In this tutorial, we have learned how to build an interactive iris classifier web app using Python and Amber. We started by designing the user interface, then wrote client-side Python code to dictate the app's behavior. Using the Anvil Uplink, we established a connection between our app and the collab notebook. After deploying the app, we explored options for hosting the model online to ensure continuous availability. This tutorial provides a solid foundation for building similar web apps and exploring the vast possibilities offered by Python and machine learning.

Highlights:

• Build an interactive web app entirely in Python
• Collect iris flower measurements from users
• Use the Anvil Uplink to connect the app to a collab notebook
• Deploy the app with just two clicks
• Host the model online with Anvil for continuous availability

FAQ: Q: Can I build the app without any knowledge of HTML, CSS, or JavaScript? A: Yes, with Amber, you can build the entire web app using just Python, without any need for HTML, CSS, or JavaScript.

Q: What do I need to know to follow this tutorial? A: You should have a basic understanding of Python and how to use Google collab notebooks. Familiarity with machine learning concepts would also be helpful.

Q: Can I deploy my app and model for free? A: Deploying the app with Anvil may require a paid account, but you can follow the tutorial to host the model on your own computer for free.

Q: Are there any resources for further learning? A: Yes, the tutorial includes links to additional resources for learning more about machine learning and the iris classification problem.