AI Deploy - Tutoriel - Déployer une app pour de l'analyse de sentiment avec Hugging Face et Flask (EN)

Comment déployer une app pour analyser les sentiments d'un texte en utilisant Hugging Face et Flask

Last updated 3rd November, 2022.

AI Deploy is in beta. During the beta-testing phase, the infrastructure’s availability and data longevity are not guaranteed. Please do not use this service for applications that are in production, as this phase is not complete.

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The purpose of this tutorial is to show you how to deploy a web service for sentiment analysis on text using Hugging Face pretrained models.
In order 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.

Overview of the app:

Hugging Face Overview

For more information about Hugging Face, please visit


We also suggest you do some tests to find out which Hugging Face model is right for your use case. Find examples on our GitHub repository.


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

Flask tree structure

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

Write the Flask application

Create a Python file named

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 = {"RoBERTa" : transformers.pipeline("sentiment-analysis", model="siebert/sentiment-roberta-large-english"), "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 :

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,host='')

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.




Here we will mainly discuss how to write the code, the requirements.txt file and the Dockerfile. If you want to see the whole code, 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/" ]

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

Launch the following command from the Dockerfile directory to build your application image:

docker build . -t sentiment_analysis_app:latest

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.

Don't forget the --user=42420:42420 argument if you want to simulate the exact same behaviour 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

The shared registry of AI Deploy should only be used for testing purposes. Please consider attaching your own Docker registry. More information about this can be found here.

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

ovhai registry list

Login on the shared registry with your usual OpenStack 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

  • You can also imagine deploying an Object Detection model with Flask in this tutorial.
  • Discover an other tool to deploy easily AI models: Gradio. Refer to this documentation.


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