Deploying Python Web Apps with Streamlit Cloud

1. Preparing Your App for Deployment

Before you can deploy an app, you need to prepare a package that the hosting service can understand. This involves two key components: your Python script and a list of its dependencies.

The Python Script

For this exercise, we'll start with a simple "Hello World" Streamlit app. Create a file named app.py with the following code:

import streamlit as st

st.title('My First Deployed App')

st.write('Hello, world! This app is live on Streamlit Community Cloud.')




# A simple interactive element
name = st.text_input('What is your name?')

if name:
    st.write(f'Hello, {name}!')

This script creates a title, writes a line of text, and includes a simple text input box to demonstrate Streamlit's interactivity.

The Dependencies File (requirements.txt)

When Streamlit Cloud builds your app, it needs to know which Python libraries to install. You specify these in a requirements.txt file.

For our simple app, this file will contain only one line:

streamlit

You can create this file manually. If your app used other libraries like pandas or numpy, you would add them to this file, each on a new line.

2. The Deployment Workflow: From GitHub to Live App

Streamlit Community Cloud works by connecting directly to your GitHub account. This enables a powerful feature: continuous deployment. Every time you push an update to your GitHub repository, your live app updates automatically.

Step 1: Create a GitHub Repository

1. Go to your GitHub account and create a new public repository. You can name it something like streamlit-deployment-demo.
2. Add the app.py and requirements.txt files you just created to this repository. You can do this by uploading the files directly through the GitHub web interface or by using git from your terminal.

Your repository should now contain these two files.

Step 2: Deploy via Streamlit Community Cloud

Now for the main event. The following video from the official Streamlit team provides a perfect walkthrough of the entire process, from signing up to seeing your app go live.

For a text-based reference that you can keep open while you work, the official Streamlit documentation provides the same steps in a concise format.

By following these resources, you will have a live, public URL for your application. Congratulations!

3. Managing and Updating Your Deployed App

Your work doesn't stop once the app is live. You'll need to manage it, update it, and occasionally troubleshoot it.

• Updating Your App: As mentioned, any git push to your main branch on GitHub will automatically trigger a rebuild and update your app. Try it now: change the text in app.py, commit the change, and push it to GitHub. You'll see your live app update within a minute or two.

• Managing Settings: In the Streamlit Cloud dashboard (share.streamlit.io), you can manage your apps. As shown in the first video (at 10:02), you can click the settings for an app to give it a more memorable custom URL.

• Troubleshooting: If your app fails to deploy, the cause is almost always an issue in your requirements.txt file (e.g., a typo or a missing library). When viewing your app, click the "Manage app" button in the bottom-right corner to view the logs. This will show you the exact error message, which you can use to debug the problem. This process is very similar to the debugging workflows we covered in the last lesson.

The following resource provides excellent written guidance on these management and troubleshooting topics.

4. Putting It All Together: Your Turn

You now have the complete workflow to take a Python script and share it as a web app. Given your finance background and interest in building tools, imagine converting a financial calculation you might do in a spreadsheet into a shareable app.

As a challenge, try using Gemini to help you build and deploy a simple mortgage or investment calculator.

Example prompt for Gemini:

"I want to build a simple web app using Python and Streamlit. The app should calculate the future value of an investment.

It needs three input fields:

1. Principal amount (a number input)
2. Annual interest rate (a number input)
3. Investment duration in years (a slider from 1 to 30)

When the user changes any input, it should calculate and display the future value using the formula FV = P * (1 + r)^t.

Please generate the full Python code for the app.py file."

Once you have the code, create the corresponding requirements.txt, push it to a new GitHub repo, and deploy it. You'll have a functional, shareable financial tool in under 30 minutes.

Conclusion

You've reached the end of our rapid prototyping module and have successfully mastered the full development lifecycle: from planning and generating code with an AI to debugging and now, deploying a live application. This is a powerful combination of skills that is fundamental to leveraging AI for your startup ambitions and professional work.

Key Takeaways:

• Streamlit Community Cloud offers a fast, free, and straightforward way to deploy Python web apps.
• The deployment process hinges on a GitHub repository containing your app.py script and a requirements.txt file listing all dependencies.
• Deployment is continuous: git push your changes, and your live application updates automatically.
• Troubleshooting deployment issues typically involves checking the application logs for errors related to your code or dependencies.

Next Up:

With the ability to build and deploy AI-powered prototypes, the next logical step is to ensure your skills remain current in this rapidly evolving landscape. The final module of our course, "Building a System to Stay Current in AI," is dedicated to just that. In our next lesson, we will begin by learning how to set up a curated information system of newsletters, channels, and communities to effectively track AI tool developments.