AI Deploy - Tutorial - Deploy and call a spam classifier with FastAPI

Learn how to deploy an API for Spam classification.

The use case is the Spam Ham Collection Dataset.

The objective of this tutorial is to show how it is possible to create, deploy, and call an API with AI Deploy.

To do this, we will use FastAPI, a web framework for developing RESTful APIs in Python. You will also learn how to build and use a custom Docker image for a FastAPI API.

Requirements

• Access to the OVHcloud Control Panel
• An AI Deploy project created inside a Public Cloud project in your OVHcloud account
• A user for AI Deploy
• Docker installed on your local computer
• Some knowledge about building an image and Dockerfile
• You also should have followed the tutorial AI Notebooks - Tutorial - Build your spam classifier. Find the code on the GitHub repository. You will be able to understand the creation of the spam classification model.

Instructions

You are going to follow different steps to build your FastAPI app.

• More information about FastAPI capabilities can be found here.
• A direct link to the full code can be found here.

Here we will mainly discuss how to write the model.py and app.py codes, the requirements.txt file, and the Dockerfile. If you want to see the whole code, please refer to the GitHub repository.

Create the API

Two Python files are created to define the model and build the API.

Define the model.py file

This Python file is dedicated to building and defining the Logistic Regression model. You can find the full code here.

First, we have to load the Spam Ham Collection Dataset.

def load_data():

    PATH = 'SMSSpamCollection'
    df = pd.read_csv(PATH, delimiter = "\t", names=["classe", "message"])
    X = df['message']
    y = df['classe']

    return X, y

Then, we create the function to split the dataset for training and test.

def split_data(X, y):

    ntest = 2000/(3572+2000)
    X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=ntest, random_state=0)

    return X_train, y_train

The last function allows us to build the model.

def spam_classifier_model(Xtrain, ytrain):

    model_logistic_regression = LogisticRegression()
    model_logistic_regression = model_logistic_regression.fit(Xtrain, ytrain)

    coeff = model_logistic_regression.coef_
    coef_abs = np.abs(coeff)

    quantiles = np.quantile(coef_abs,[0, 0.25, 0.5, 0.75, 0.9, 1])

    index = np.where(coeff[0] > quantiles[1])
    newXtrain = Xtrain[:, index[0]]

    model_logistic_regression = LogisticRegression()

    model_logistic_regression.fit(newXtrain, ytrain)

    return model_logistic_regression, index

# extract input and output data
data_input, data_output = load_data()

# split data
X_train, ytrain = split_data(data_input, data_output)

# transform and fit training set
vectorizer = CountVectorizer(stop_words='english', binary=True, min_df=10)
Xtrain = vectorizer.fit_transform(X_train.tolist())
Xtrain = Xtrain.toarray()

# use the model and index for prediction
model_logistic_regression, index = spam_classifier_model(Xtrain, ytrain)

Write the app.py file

Initialize an instance of FastAPI.

app = FastAPI()

Define the data format.

class request_body(BaseModel):
    message : str

Process the message sent by the user.

def process_message(message):

    desc = vectorizer.transform(message)
    dense_desc = desc.toarray()
    dense_select = dense_desc[:, index[0]]

    return dense_select

Define the GET method.

@app.get('/')
def root():
    return {'message': 'Welcome to the SPAM classifier API'}

Create the POST method.

@app.post('/spam_detection_path')
def classify_message(data : request_body):

    message = [
        data.message
    ]

    if (not (message)):
        raise HTTPException(status_code=400, detail="Please Provide a valid text message")

    dense_select = process_message(message)
    label = model_logistic_regression.predict(dense_select)
    proba = model_logistic_regression.predict_proba(dense_select)

    if label[0]=='ham':
        label_proba = proba[0][0]
    else:
        label_proba = proba[0][1]

    return {'label': label[0], 'label_probability': label_proba}

Write the requirements.txt file for the API

The requirements.txt file will allow us to write all the modules needed to make our application work. This file will be useful when writing the Dockerfile.

fastapi==0.87.0
pydantic==1.10.2
uvicorn==0.20.0
pandas==1.5.1
scikit-learn==1.1.3

Write the Dockerfile for the application

Your Dockerfile should start with the FROM instruction indicating the parent image to use. In our case we choose to start from the python:3.8 image:

python:3.8

Create the home directory and add your files to it:

WORKDIR /workspace
ADD . /workspace

Install the requirements.txt file which contains your needed Python modules using a pip install ... command:

RUN pip install -r requirements.txt

Set the listening port of the container:

EXPOSE 8000

Define the entrypoint and the default launching command to start the application:

ENTRYPOINT ["uvicorn"]
CMD ["app:app", "--host", "0.0.0.0"]

Give correct access rights to an ovhcloud user (42420:42420):

RUN chown -R 42420:42420 /workspace
ENV HOME=/workspace

Build the Docker image from the Dockerfile

From the directory containing your Dockerfile, run one of the following commands to build your application image:

# Build the image using your machine's default architecture
docker build . -t fastapi-spam-classification:latest

# Build image targeting the linux/amd64 architecture
docker buildx build --platform linux/amd64 -t fastapi-spam-classification:latest .

• The first command builds the image using your system’s default architecture. This may work if your machine already uses the linux/amd64 architecture, which is required to run containers with our AI products. However, on systems with a different architecture (e.g. ARM64 on Apple Silicon), the resulting image will not be compatible and cannot be deployed.

• The second command explicitly targets the linux/AMD64 architecture to ensure compatibility with our AI services. This requires buildx, which is not installed by default. If you haven’t used buildx before, you can install it by running: docker buildx install

Test it locally (optional)

Launch the following Docker command to launch your application locally on your computer:

docker run --rm -it -p 8000:8000 --user=42420:42420 fastapi-spam-classification:latest

Once started, your application should be available on http://localhost:8000.

Push the image into the shared registry

Find the address of your shared registry by launching this command:

ovhai registry list

Log in on the shared registry with your usual AI Platform user credentials:

docker login -u <user> -p <password> <shared-registry-address>

Push the compiled image into the shared registry:

docker tag fastapi-spam-classification:latest <shared-registry-address>/fastapi-spam-classification:latest
docker push <shared-registry-address>/fastapi-spam-classification:latest

Launch the AI Deploy app

The following command starts a new app running your FastAPI application:

ovhai app run \
      --default-http-port 8000 \
      --cpu 4 \
      <shared-registry-address>/fastapi-spam-classification:latest

Interact with the deployed API through the dashboard

By clicking on the link of your AI Deploy app, you will arrive at the following page.

How to interact with your API?

You can add /docs at the end of the URL of your app.

It provides a complete dashboard for interacting with the API!

To be able to send a message for classification, select /spam_detection_path in the green box. Click on Try it out and type the message of your choice in the dedicated zone.

To get the result of the prediction, click on the Execute button.

Congratulations! You have obtained the result of the prediction with the label and the confidence score.

Go further

• You can imagine deploying an AI model with another tool: Gradio. Read this tutorial.
• Another way to create an AI Deploy app is to use Streamlit! Follow this tutorial.

For more information and tutorials, please see our other AI & Machine Learning support guides or explore the guides for other OVHcloud products and services.

If you need training or technical assistance to implement our solutions, contact your sales representative or click on this link to get a quote and ask our Professional Services experts for a custom analysis of your project.