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AI Deploy - Tutorial - Deploy an app for sentiment analysis with Hugging Face and Flask

Stephanie R.
Updated

Learn how to deploy a web service for sentiment analysis on text using Hugging Face pre-trained models. To do this, you will use Flask, an open-source micro framework for web development in Python. You will also learn how to build and use a custom Docker image for a Flask application.

AI Deploy is covered by OVHcloud Public Cloud Special Conditions.

Overview of the app:

PaaS_AI_deploy_sentimentAnalysis01.png

For more information about Hugging Face, please visit the Hugging Face website.

 

Requirements

We also suggest that you conduct some tests to determine which Hugging Face model is best suited for your use case. Find examples on our GitHub repository.

 

Instructions

First, the tree structure of your folder should be as follows:

PaaS_AI_deploy_sentimentAnalysis02.png

Find more information about the Flask application here to get ready to use it.

 

Write the Flask application

Create a Python file named app.py.

Inside that file, import your required modules:

from flask import Flask, jsonify, render_template, request, make_response
import transformers

Create Flask app:

app = Flask(__name__)

Load Hugging Face models:

# create a python dictionary for your models d = {<key>: <value>, <key>: <value>, ..., <key>: <value>}
dictOfModels = {"BERT" : transformers.pipeline('sentiment-analysis', model="nlptown/bert-base-multilingual-uncased-sentiment")}
# create a list of keys to use them in the select part of the html code
listOfKeys = []
for key in dictOfModels :
        listOfKeys.append(key)

Write the inference function:

def get_prediction(message,model):
    # inference
    results = model(message)  
    return results

Define the GET method:

@app.route('/', methods=['GET'])
def get():
    # in the select we will have each key of the list in option
    return render_template("home.html", len = len(listOfKeys), listOfKeys = listOfKeys)

Define the POST method:

@app.route('/', methods=['POST'])
def predict():
    message = request.form['message']
    # choice of the model
    results = get_prediction(message, dictOfModels[request.form.get("model_choice")])
    print(f'User selected model : {request.form.get("model_choice")}')
    my_prediction = f'The feeling of this text is {results[0]["label"]} with probability of {results[0]["score"]*100}%.'
    return render_template('result.html', text = f'{message}', prediction = my_prediction)

Start your app:

if __name__ == '__main__':
    # starting app
    app.run(debug=True,host='0.0.0.0')

 

Write the requirements.txt file for the application

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.

Flask==2.1.0

transformers==4.4.2

torch==1.6.0

Here we will mainly discuss how to write the app.py code, the requirements.txt file and the Dockerfile. If you want to see the whole codebase and download the required contents for the static and templates directories, please refer to the GitHub repository.

 

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 a Python image:

FROM 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

Define your default launching command to start the application:

CMD [ "python" , "/workspace/app.py" ]

Give correct access rights to 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 sentiment_analysis_app:latest

# Build image targeting the linux/amd64 architecture
docker buildx build --platform linux/amd64 -t sentiment_analysis_app: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

The dot argument . indicates that your build context (place of the Dockerfile and other needed files) is the current directory.

The -t argument allows you to choose the identifier to give to your image. Usually, image identifiers are composed of a name and a version tag <name>:<version>. For this example, we chose sentiment_analysis_app:latest.

 

Test it locally (optional)

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

docker run --rm -it -p 5000:5000 --user=42420:42420 sentiment_analysis_app:latest

The -p 5000:5000 argument indicates that you want to execute a port redirection from the port 5000 of your local machine into the port 5000 of the Docker container. The port 5000 is the default port used by Flask applications.

NOTE: Don't forget the --user=42420:42420 argument if you want to simulate the exact same behavior that will occur on AI Deploy apps. It executes the Docker container as the specific OVHcloud user (user 42420:42420).

Once started, your application should be available on http://localhost:5000.

 

Push the image into the shared registry

NOTE: The shared registry should only be used for testing purposes. Please consider creating and attaching your own registry. More information about this can be found here. The images pushed to this registry are for AI Tools workloads only, and will not be accessible for external uses.

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

ovhai registry list

Login 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 sentiment_analysis_app:latest <shared-registry-address>/sentiment_analysis_app:latest
docker push <shared-registry-address>/sentiment_analysis_app:latest

 

Launch the AI Deploy app

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

ovhai app run --default-http-port 5000 --cpu 4 <shared-registry-address>/sentiment_analysis_app:latest

--default-http-port 5000 indicates that the port to reach on the app URL is the 5000.

--cpu 4 indicates that we request 4 CPUs for that app.

Consider adding the --unsecure-http attribute if you want your application to be reachable without any authentication.

 

Go further

  • Discover another tool to deploy easily AI models: Gradio. Refer to this documentation.

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.

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