Deploying a Python Flask Web Application to App Engine Flexible

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Deploying a Python Flask Web Application to App Engine Flexible

1 hour 1 Credit


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In this lab you will learn how to deploy a Python Flask web application to the App Engine Flexible environment. The example application allows a user to upload a photo of a person's face and learn how likely it is that the person is happy. The application uses Google Cloud APIs for Vision, Storage, and Datastore.

About App Engine

Google App Engine applications are easy to create, easy to maintain, and easy to scale as your traffic and data storage needs change. With App Engine, there are no servers to maintain. You simply upload your application and it's ready to go.

App Engine applications automatically scale based on incoming traffic. Load balancing, microservices, authorization, SQL and NoSQL databases, traffic splitting, logging, search, versioning, roll out and roll backs, and security scanning are all supported natively and are highly customizable.

App Engine's Flexible Environment supports a host of programming languages, including Java, Python, PHP, NodeJS, Ruby, and Go. App Engine's Standard Environment is an additional option for certain languages including Python. The two environments give users maximum flexibility in how their application behaves since each environment has certain strengths. Read Choosing an App Engine Environment for more information.

What you'll learn

  • How to deploy a simple web application to the App Engine Flexible Environment
  • How to access the Google Cloud client libraries for Vision, Storage, and Datastore
  • How to use the Cloud Shell


  • Familiarity with Python
  • Familiarity with standard Linux text editors such as vim, emacs, or nano
  • Access to an image with a face

Setup and Requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

  • Access to a standard internet browser (Chrome browser recommended).
Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.
  • Time to complete the lab---remember, once you start, you cannot pause a lab.
Note: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

How to start your lab and sign in to the Google Cloud Console

  1. Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

    • The Open Google Console button
    • Time remaining
    • The temporary credentials that you must use for this lab
    • Other information, if needed, to step through this lab
  2. Click Open Google Console. The lab spins up resources, and then opens another tab that shows the Sign in page.

    Tip: Arrange the tabs in separate windows, side-by-side.

    Note: If you see the Choose an account dialog, click Use Another Account.
  3. If necessary, copy the Username from the Lab Details panel and paste it into the Sign in dialog. Click Next.

  4. Copy the Password from the Lab Details panel and paste it into the Welcome dialog. Click Next.

    Important: You must use the credentials from the left panel. Do not use your Google Cloud Skills Boost credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.
  5. Click through the subsequent pages:

    • Accept the terms and conditions.
    • Do not add recovery options or two-factor authentication (because this is a temporary account).
    • Do not sign up for free trials.

After a few moments, the Cloud Console opens in this tab.

Note: You can view the menu with a list of Google Cloud Products and Services by clicking the Navigation menu at the top-left. Navigation menu icon

Activate Cloud Shell

Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.

  1. In the Cloud Console, in the top right toolbar, click the Activate Cloud Shell button.

Cloud Shell icon

  1. Click Continue.

It takes a few moments to provision and connect to the environment. When you are connected, you are already authenticated, and the project is set to your PROJECT_ID. The output contains a line that declares the PROJECT_ID for this session:

Your Cloud Platform project in this session is set to YOUR_PROJECT_ID

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

  1. (Optional) You can list the active account name with this command:

gcloud auth list


ACTIVE: * ACCOUNT: To set the active account, run: $ gcloud config set account `ACCOUNT`
  1. (Optional) You can list the project ID with this command:

gcloud config list project


[core] project = <project_ID>

(Example output)

[core] project = qwiklabs-gcp-44776a13dea667a6 For full documentation of gcloud, in Google Cloud, Cloud SDK documentation, see the gcloud command-line tool overview.

Get the sample code

In Cloud Shell on the command-line, run the following command to clone the Github repository:

git clone

Change directory into python-docs-samples/codelabs/flex_and_vision:

cd python-docs-samples/codelabs/flex_and_vision

Authenticate API Requests

The Datastore, Storage, and Vision APIs are automatically enabled for you in this lab. In order to make requests to the APIs, you will need service account credentials. You can generate credentials from your project using gcloud in Cloud Shell. Your Project ID can be found on the Qwiklabs tab, where you started the lab.

Set an environment variable for [YOUR_PROJECT_ID], replacing [YOUR_PROJECT_ID] with your own project ID:


Create a Service Account to access the Google Cloud APIs when testing locally:

gcloud iam service-accounts create qwiklab \ --display-name "My Qwiklab Service Account"

Give your newly created Service Account appropriate permissions:

gcloud projects add-iam-policy-binding ${PROJECT_ID} \ --member serviceAccount:qwiklab@${PROJECT_ID} \ --role roles/owner

After creating your Service Account, create a Service Account key:

gcloud iam service-accounts keys create ~/key.json \ --iam-account qwiklab@${PROJECT_ID}

This command generates a service account key stored in a JSON file named key.json in your home directory.

Using the absolute path of the generated key, set an environment variable for your service account key:


You can read more about authenticating the Vision API.

Click Check my progress below to check your lab progress.

Authenticate API Requests

Testing the Application Locally

Starting your virtual environment and installing dependencies

Create an isolated Python 3 environment named env with virtualenv:

virtualenv -p python3 env

Enter your newly created virtualenv named env:

source env/bin/activate

Use pip to install dependencies for your project from the requirements.txt file:

pip install -r requirements.txt

The requirements.txt file is a list of package dependencies you need for your project. The above command downloaded all of these listed package dependencies to the virtualenv.

Creating an App Engine App

Next, create an App Engine instance by using:

gcloud app create

A prompt will display a list of regions. Select a Region that supports App Engine Flexible for Python then press Enter. You can read more about Regions and Zones here.

Creating a Storage Bucket

First, set the environment variable CLOUD_STORAGE_BUCKET equal to the name of your PROJECT_ID. (It is generally recommended to name your bucket the same as your PROJECT_ID for convenience purposes).


Now run the following command to create a bucket with the same name as your PROJECT_ID.

gsutil mb gs://${PROJECT_ID}

Click Check my progress below to check your lab progress.

Create an App Engine App and Storage Bucket

Running the Application

Execute the following command to start your application:


Once the application starts, click on the Web Preview icon in the Cloud Shell toolbar and choose "Preview on port 8080."


A tab in your browser opens and connects to the server you just started. You should see something like this:


Now things will get interesting! Click the Choose File button, find an image from your computer that has a human face, and then click Submit.

After uploading a photo, you should see something like this:


Click Check my progress below to check your lab progress.

Run the Application

Exploring the Code

templates/ homepage.html /* HTML template that uses Jinja2 */ app.yaml /* App Engine application configuration file */ /* Python Flask web application */ requirements.txt /* List of dependencies for the project */ from import datastore from import storage from import vision @app.route('/') def homepage(): # Create a Cloud Datastore client. datastore_client = datastore.Client() # Use the Cloud Datastore client to fetch information from Datastore about # each photo. query = datastore_client.query(kind='Faces') image_entities = list(query.fetch()) # Return a Jinja2 HTML template and pass in image_entities as a parameter. return render_template('homepage.html', image_entities=image_entities) # Create a Cloud Datastore client. datastore_client = datastore.Client() # Fetch the current date / time. current_datetime = # The kind for the new entity. kind = 'Faces' # The name/ID for the new entity. name = # Create the Cloud Datastore key for the new entity. key = datastore_client.key(kind, name) # Construct the new entity using the key. Set dictionary values for entity # keys blob_name, storage_public_url, timestamp, and joy. entity = datastore.Entity(key) entity['blob_name'] = entity['image_public_url'] = blob.public_url entity['timestamp'] = current_datetime entity['joy'] = face_joy # Save the new entity to Datastore. datastore_client.put(entity)


<h1>Google Cloud Platform - Face Detection Sample</h1> <p>This Python Flask application demonstrates App Engine Flexible, Google Cloud Storage, Datastore, and the Cloud Vision API.</p> <br> <html> <body> <form action="upload_photo" method="POST" enctype="multipart/form-data"> Upload File: <input type="file" name="file"><br> <input type="submit" name="submit" value="Submit"> </form> {% for image_entity in image_entities %} <img src="{{image_entity['image_public_url']}}" width=200 height=200> <p>{{image_entity['blob_name']}} was uploaded {{image_entity['timestamp']}}.</p> <p>Joy Likelihood for Face: {{image_entity['joy']}}</p> {% endfor %} </body> </html>

Deploying the App to App Engine Flexible

App Engine Flexible uses a file called app.yaml to describe an application's deployment configuration. If this file is not present, App Engine will try to guess the deployment configuration. However, it is a good idea to provide this file.

Next, you will modify app.yaml using an editor of your choice vim, nano, or emacs. We will use the nano editor:

nano app.yaml

Once you have app.yaml open, replace <your-cloud-storage-bucket> with the name of your Cloud Storage bucket. (If you forgot the name of your Cloud Storage bucket, copy the Project ID from the Qwiklabs tab). The env_variables section sets up environment variables that will be used in once the application is deployed.

Next, set your app to use manual scaling by adding this at the end of the file:

manual_scaling: instances: 1

Your file should look like this:

runtime: python env: flex entrypoint: gunicorn -b :$PORT main:app runtime_config: python_version: 3 env_variables: CLOUD_STORAGE_BUCKET: manual_scaling: instances: 1

This is the basic configuration needed to deploy a Python 3 App Engine Flex application. You can learn more about configuring App Engine here.

You can now save and close the file in nano by using (Ctrl + x), which will prompt:


Type a letter Y and then press the ENTER key one more time to confirm the filename for the following prompt:


Update your Cloud Build timeout:

gcloud config set app/cloud_build_timeout 1000

Deploy your app on App Engine by using gcloud:

gcloud app deploy

If asked, Do you want to continue (Y/n), press Y and then Enter.

Watch in Cloud Shell as the application gets built. This will take up to 10 minutes. The App Engine Flexible environment is automatically provisioning a Compute Engine virtual machine for you behind the scenes, and then installing the application, then starting it.


After the application is deployed, open the app in your web browser with the following URL:


Click Check my progress below to check your lab progress.

Deploy the App


You learned how to write and deploy a Python web application to the App Engine Flexible environment!

Finish Your Quest


This self-paced lab is part of the Deploying Applications Quest. A Quest is a series of related labs that form a learning path. Completing this Quest earns you the badge above, to recognize your achievement. You can make your badge (or badges) public and link to them in your online resume or social media account. Enroll in this Quest and get immediate completion credit if you've taken this lab. See other available Quests.

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Manual Last Updated October 20, 2021
Lab Last Tested October 20, 2021

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