--- title: "AI Deploy - Tutorial - Deploy an app for sentiment analysis with Hugging Face and Flask" description: "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 fra" url: https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-deploy-flask-hugging-face-sentiment-analysis lang: en --- > For AI agents: the complete documentation index is available at https://docs.us.ovhcloud.com/en/llms.txt, the full documentation bundle is available at https://docs.us.ovhcloud.com/en/llms-full.txt. # AI Deploy - Tutorial - Deploy an app for sentiment analysis with Hugging Face and Flask **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.** **Note:** AI Deploy is covered by **[OVHcloud Public Cloud Special Conditions](https://us.ovhcloud.com/legal/service-specific-terms/)**. Overview of the app: For more information about Hugging Face, please visit [the Hugging Face website](https://huggingface.co/).   ## Requirements - Access to the [OVHcloud Control Panel](https://manager.us.ovhcloud.com/) - An AI Deploy project created inside a [Public Cloud project](https://us.ovhcloud.com/public-cloud/) in your OVHcloud account - A [user for AI Deploy](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/manage-users-roles.md) - [Docker](https://www.docker.com/get-started) installed on your local computer - Some knowledge about building an image and [Dockerfile](https://docs.docker.com/engine/reference/builder/) 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](https://github.com/ovh/ai-training-examples/tree/main/notebooks/natural-language-processing/text-classification/hugging-face/sentiment-analysis-twitter).   ## Instructions First, the tree structure of your folder should be as follows: Find more information about the Flask application [here](https://flask.palletsprojects.com/en/2.0.x/quickstart/#a-minimal-application) to get ready to use it.   ### Write the Flask application Create a Python file named `app.py`. Inside that file, import your required modules: ```python from flask import Flask, jsonify, render_template, request, make_response import transformers ``` Create Flask app: ```python app = Flask(__name__) ``` Load Hugging Face models: ```python # create a python dictionary for your models d = {: , : , ..., : } 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: ```python def get_prediction(message,model): # inference results = model(message) return results ``` Define the GET method: ```python @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: ```python @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: ```python 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`. ```console 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](https://github.com/ovh/ai-training-examples/tree/main/apps/flask/sentiment-analysis-hugging-face-app).   ### 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: ```console FROM python:3.8 ``` Create the home directory and add your files to it: ```console WORKDIR /workspace ADD . /workspace ``` Install the `requirements.txt` file which contains your needed Python modules using a `pip install ...` command: ```console RUN pip install -r requirements.txt ``` Define your default launching command to start the application: ```console CMD [ "python" , "/workspace/app.py" ] ``` Give correct access rights to **ovhcloud user** (`42420:42420`): ```console 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: ```console # 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` **Note:** The dot argument `.` indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. **Note:** 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** `:`. 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: ```console docker run --rm -it -p 5000:5000 --user=42420:42420 sentiment_analysis_app:latest ``` **Note:** 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. **Warning:** **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 **Warning:** **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](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/manage-registries.md). 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: ```console ovhai registry list ``` Login on the shared registry with your usual AI Platform user credentials: ```console docker login -u -p ``` Push the compiled image into the shared registry: ```console docker tag sentiment_analysis_app:latest /sentiment_analysis_app:latest docker push /sentiment_analysis_app:latest ```   ### Launch the AI Deploy app The following command starts a new app running your Flask application: ```console ovhai app run --default-http-port 5000 --cpu 4 /sentiment_analysis_app:latest ``` **Note:** `--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](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-deploy-gradio-sketch-recognition.md). Browse all the [AI & Machine Learning guides](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/landing-page-ai-machine-learning.md). Browse the rest of the [OVHcloud US documentation](https://docs.us.ovhcloud.com/en/index.md) for guides on our other products and services. Learn more about [AI & Machine Learning](https://us.ovhcloud.com/public-cloud/ai-machine-learning/) on the OVHcloud US website. If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://us.ovhcloud.com/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project.