How to Use Google Cloud Function with Python ?
Google Cloud Functions provides a way to run small pieces of code in response to cloud events without managing servers. If you're a developer looking to automate tasks, process data or build APIs, Python is a great language for working with Google Cloud Functions.
In this article, we will look into how to use the Google Cloud Function with python on any website.
Use of Google Cloud Functions
Here’s why Google Cloud Functions can be useful:
- No Server Management – Deploy code without worrying about infrastructure.
- Event-driven – Runs automatically when an event occurs, such as an HTTP request or a file upload.
- Scales Automatically – Handles increased workload without manual intervention.
- Cost-Effective – You only pay for the execution time and resources used.
- Seamless Google Cloud Integration – Works well with services like Firebase, BigQuery, and Cloud Storage.
What you need before getting started?
Before deploying your Python function, make sure you have:
- A Google Cloud Platform (GCP) account.
- The gcloud CLI installed on your system.
- Python 3.8 or later installed.
- A Google Cloud project with billing enabled.
To do so follow the below steps:
Step 1: Let's first head to the functions manager site on Google Cloud Platform (GCP). It should look like below:
Step 2: Now let's create our function. Click the "CREATE FUNCTION" on the top.
Step 3: A window like this one should appear next. Here in the function name, give any name. And if you are not going to use any authentication, choose "Allow unauthenticated invocations". For this particular example at least, we will choose "Allow unauthenticated invocations". Other options are not required to be changed. Now hit the "NEXT" button.
Step 4: Now you will get a Trigger URL. Copy this URL somewhere as this is the URL we will use to invoke our function. Then hit "NEXT".
Step 5: Now under "Runtime", select a Python version. We have selected "Python 3.8". Now here comes the main part, the SOURCE CODE of the function. Right now if you deploy the function, it most probably won't work because, in the source code, there is no "Access Control" header. This means when you will invoke this function from a browser like Firefox or Chrome, it won't work. So let's use our own source code.
The request_json = request.get_json() extracts the JSON object sent by the client. The next code specifies the access scope of the user. The next code line checks whether there is a "Message" header in the headers. If yes, then send its value and the retrieved JSON, as a response to the client. If not, then send the entire header content along with any JSON retrieved back to the client. That's it for the source code.
But before we test the function, here are a few things. The "Entry point" beside the "Runtime" shows the method which will be invoked when a request has been received. Under the source code section, you will also see the requirements.txt file. This file is used to specify additional dependencies for your function. For additional queries regarding requirements.txt, you may refer to this documentation.
Step 6: Now let's hit "DEPLOY" to deploy the function. Now you will again be redirected to the functions manager site. It should take about 1-2 minutes for the function to be deployed.
Now, let's test our function.
Step 7: Let's create a test.html file from where we can call our function using JavaScript.
In the "Your trigger URL" on line 14, paste the trigger URL you copied before. We set a header "Content-Type" to tell our function, what kind of data we have sent. In our case, it is JSON. Next, we set a header named "Message" just for testing purposes. Then we specify what to do when the response has been received. And at last, send some dummy data as JSON. Now let's run the below given HTML file in a browser.
<!DOCTYPE html>
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type">
<meta content="utf-8" http-equiv="encoding">
<title>GCP Function</title>
</head>
<body>
<p id="p">Processing...</p>
<script>
function httpGet()
{
var xmlHttp = new XMLHttpRequest();
xmlHttp.open("POST", "Your trigger URL");
xmlHttp.setRequestHeader("Content-Type", "application/json;charset=UTF-8");
xmlHttp.setRequestHeader("message", "Header: This is head");
xmlHttp.onload = function () {
document.getElementById("p").innerHTML=xmlHttp.responseText;
};
xmlHttp.send(JSON.stringify({"Body": "This is body"}));
}
httpGet();
</script>
</body>
</html>
And bravo! Here is the response! Everything is up and running. But what will happen if you code wrong in the source code of the function? Google is there to the rescue.
Click the function you created. In the next window, select the "LOGS" tab to see the logs of your function.
Advanced Use Cases
- Cloud Storage Integration – Automatically process uploaded files.
- Firestore Triggers – Execute functions when database entries are modified.
- Building APIs – Use Flask or FastAPI to create lightweight APIs.
Conclusion
Google Cloud Functions with Python is a powerful tool for automating tasks and building cloud applications. This guide walked you through the setup and deployment process in a simple, easy-to-follow manner.
