Spaces:
Paused
Paused
File size: 8,669 Bytes
40d04ab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 |
# AI Deploy - Tutorial - Deploy an app for sentiment analysis with Hugging Face and Flask
> **Note** Access to the full documentation [here](https://docs.ovh.com/gb/en/publiccloud/ai/deploy/tuto-flask-hugging-face-sentiment-analysis/).
**Last updated 3rd November, 2022.**
> **Note**
> 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.
>
> AI Deploy is covered by **[OVHcloud Public Cloud Special Conditions](https://storage.gra.cloud.ovh.net/v1/AUTH_325716a587c64897acbef9a4a4726e38/contracts/d2a208c-Conditions_particulieres_OVH_Stack-WE-9.0.pdf)**.
>
## Objective
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.<br>
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:
{.thumbnail}
For more information about Hugging Face, please visit <https://huggingface.co/>.
## Requirements
- Access to the [OVHcloud Control Panel](https://www.ovh.com/auth/?action=gotomanager&from=https://www.ovh.co.uk/&ovhSubsidiary=GB);
- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/en-gb/public-cloud/) in your OVHcloud account;
- A [user for AI Deploy](https://docs.ovh.com/gb/en/publiccloud/ai/users/);
- [Docker](https://www.docker.com/get-started) installed on your local computer;
- Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/);
We also suggest you do some tests to find out which [Hugging Face model](https://huggingface.co/models) is right 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 = {<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 :
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==1.1.2
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 code, 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
Launch the following command from the **Dockerfile** directory to build your application image:
```console
docker build . -t sentiment_analysis_app:latest
```
> **Note**
> 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:
```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**
> 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
> **Warning**
> 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](https://docs.ovh.com/gb/en/publiccloud/ai/training/add-private-registry/).
>
Find the adress of your shared registry by launching this command:
```console
ovhai registry list
```
Login on the shared registry with your usual OpenStack credentials:
```console
docker login -u <user> -p <password> <shared-registry-address>
```
Push the compiled image into the shared registry:
```console
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:
```console
ovhai app run --default-http-port 5000 --cpu 4 <shared-registry-address>/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
- You can also imagine deploying an Object Detection model with **Flask** in this [tutorial](https://docs.ovh.com/gb/en/publiccloud/ai/deploy/web-service-yolov5/).
- Discover an other tool to deploy easily AI models: **Gradio**. Refer to this [documentation](https://docs.ovh.com/gb/en/publiccloud/ai/deploy/tuto-gradio-sketch-recognition/).
|