Flask ML API

Imagine that you work for a company that has a large collection of images and needs to automatically classify them into different categories. This task can be time-consuming and error-prone when done manually by human workers.

Your task is to develop a solution that can automatically classify images into over 1000 different categories using a Convolutional Neural Network (CNN) implemented in Tensorflow. Your solution will consist of a Web UI and a Python Flask API that serves the CNN.

The Web UI should allow users to upload an image and receive the predicted class for that image.

The Python Flask API should receive the uploaded image, preprocess it (e.g. resize, normalize), feed it into the CNN, and return the predicted class as a JSON object. The API should handle errors gracefully and provide informative error messages to the UI if something goes wrong.

You shouldn't worry for now about the TensorFlow CNN because we will use a pre-trained model for this problem. We provide you with a Jupyter notebook here which shows how to load and run this model and hope will serve as a motivation for you to learn in-depth how these amazing models work during the next sprint!

To develop this solution you will need to have a proper working environment setup in your machine consisting of:

• Docker
• docker-compose
• VS Code or any other IDE of your preference
• Optional: Linux subsystem for Windows (WSL2)

Please make sure to carefully read the ASSIGNMENT.md file which contains detailed instructions about the code you have to complete to make the project run correctly.

The technologies involved are:

• Python is the main programming language
• Flask framework for the API
• HTML for the web UI
• Redis for the communication between microservices
• Tensorflow for loading the pre-trained CNN model
• Locust for doing the stress testing of your solution

To run the services using compose:

$ cp .env.original .env

Only for mac M1 users:

• There is dockerfile for M1 macs model/Dockerfile.M1. This docker file downloads tensoflow compiled for M1
• Change docker-compose.yaml to use that docker file.
• Remove tensorflow for requirements.txt
• Remember change back docker-compose.yaml and requirements.txt in the submission.

Edit .env file and fill UID and GID env variables using command id -u and id -g.

$ docker-compose up --build -d

To stop the services:

$ docker-compose down

Note: Why do I need to setup UID and GID?

UID (User ID) and GID (Group ID) are used in Docker images to specify the user and group that should be used when running a container from the image.

When a Docker container is started, it runs as a specific user and group within the container's filesystem. By default, the user and group used in the container are the same as the ones on the host system that are running the container. However, this can lead to security issues because the container may have different permissions and access than the host system.

To avoid these issues, Docker provides the ability to specify a UID and GID when building a Docker image. This allows you to create a user and group within the container that has specific permissions and access. For example, you may want to create a user that has read-only access to certain directories or files within the container.

In addition to security, specifying a UID and GID can also help with the portability of Docker images. By using specific UIDs and GIDs, you can ensure that the container runs as expected across different host systems, even if those systems have different user and group configurations.

Overall, using UID and GID in a Docker image helps to ensure that containers are secure, portable, and operate with the appropriate permissions and access levels.

Following a style guide keeps the code's aesthetics clean and improves readability, making contributions and code reviews easier. Automated Python code formatters make sure your codebase stays in a consistent style without any manual work on your end. If adhering to a specific style of coding is important to you, employing an automated to do that job is the obvious thing to do. This avoids bike-shedding on nitpicks during code reviews, saving you an enormous amount of time overall.

We use Black and isort for automated code formatting in this project, you can run it with:

$ isort --profile=black . && black --line-length 88 .

Wanna read more about Python code style and good practices? Please see:

• The Hitchhiker’s Guide to Python: Code Style
• Google Python Style Guide

We provide unit tests along with the project that you can run and check from your side the code meets the minimum requirements of correctness needed to approve. To run just execute:

We make use of multi-stage docker builds so we can have into the same Dockerfile environments for testing and also for deploying our service.

Run:

$ cd api/
$ docker build -t flask_api_test --progress=plain --target test .

You will only need to pay attention to the logs corresponding to the testing code which will look like this:

#10 [test 1/1] RUN ["pytest", "-v", "/src/tests"]
#10 sha256:707efc0d59d04744766193fe6873d212afc0f8e4b28d035a2d2e94b40826604f
#10 0.537 ============================= test session starts ==============================
#10 0.537 platform linux -- Python 3.8.13, pytest-7.1.1, pluggy-1.0.0 -- /usr/local/bin/python
#10 0.537 cachedir: .pytest_cache
#10 0.537 rootdir: /src
#10 0.537 collecting ... collected 4 items
#10 0.748
#10 0.748 tests/test_api.py::TestIntegration::test_bad_parameters PASSED           [ 25%]
#10 0.757 tests/test_api.py::TestEnpointsAvailability::test_feedback PASSED        [ 50%]
#10 0.769 tests/test_api.py::TestEnpointsAvailability::test_index PASSED           [ 75%]
#10 0.772 tests/test_api.py::TestEnpointsAvailability::test_predict PASSED         [100%]
#10 0.776
#10 0.776 ============================== 4 passed in 0.24s ===============================
#10 DONE 0.8s

You are good if all tests are passing.

Same as api, run:

$ cd model/
$ docker build -t model_test --progress=plain --target test .

You must have the full pipeline running (see previous section) and the following Python libraries installed:

• requests==2.28.1
• pytest==7.1.2

You can install them using the file tests/requirements.txt with:

$ pip3 install -r tests/requirements.txt

Then, from the project root folder run:

$ python tests/test_integration.py

If the output looks like this, then the integration tests are passing:

.
----------------------------------------------------------------------
Ran 2 tests in 0.299s

OK

If you want to learn more about testing Python code, please read:

• Effective Python Testing With Pytest
• The Hitchhiker’s Guide to Python: Testing Your Code