Sentiment and emotion analysis API with FastAPI

sentiment_analysis_api_with_fastapi (v0.1)

Project started from this template

1. Installation
2. Getting started

├── app
│   ├── database.py
│   ├── devtools.py
│   ├── html_text_variables.py
│   ├── __init__.py
│   ├── main.py
│   ├── ml_models
│   │   ├── bert_sentiment.py
│   │   ├── __init__.py
│   │   ├── nlp_utils.py
│   │   └── roberta_emotion.py
│   ├── models.py
│   ├── routers
│   │   ├── admin.py
│   │   ├── auth.py
│   │   ├── bert_sentiment.py
│   │   ├── roberta_emotion.py
│   │   └── users.py
│   ├── schemas.py
│   └── templates
│       └── home.html
├── __init__.py
├── LICENSE
├── readme
│   └── image_xx.png
├── README.md
├── requirements.txt
├── run.sh
├── run_tests.sh
├── sqlite3
│   ├── dev_db.db
│   └── test_db.db
└── test
    ├── __init__.py
    ├── test_admin.py
    ├── test_auth.py
    ├── test_main.py
    ├── test_ml_service_v1.py
    ├── test_ml_service_v2.py
    ├── test_users.py
    └── utils.py

Installation

git clone https://github.com/MatthieuLeNozach/api_basemodel_for_machine_learning_with_fastapi

[CAUTION]: This project uses PyTorch based models, which rely on GPU computing with CUDA toolkit and Nvidia drivers. As some package installations are CUDA-specific, incompatible hardware and drivers may raise issues at install or import.
You may have to build your own python / torch environment.

Cuda drivers are also mentioned in the Docker image, you may want to remove this line from the Dockerfile:

RUN conda install -c conda-forge cudatoolkit=11.7

Set up a Python virtual environment

Python version: 3.10.14

cd /path/to/repository/root

OptionA: Using venv:

python3 -m venv venv

# Virtual environment activation:
source venv/bin/activate

# Installation of required packages from the requirements file
pip install -r requirements.txt

Option B: Using miniconda:

conda create --name fastapi_nlp python=3.10.14

# Virtual environment activation
conda activate fastapi_nlp

# Installation of required packages from the requirements file
conda install --file requirements.txt

Alternative: Mount project as a DevContainer in VSCode

Create image from the Dockerfile:

docker build -t nlp_api_image:latest

Use image as a DevContainer: #TODO- instructions

#TODO

Grant permissions to run.sh

chmod +x run.sh

Getting started

A. Securize the project

• Add .environment folder to the .gitignore file to stop exposing sensitive information to git commits
• Access the .environment folder from repository root, the folder may be hidden.

nano .environment/env.dev

nano .environment/env.test 

• Replace the dummy secret keys with your own keys. You can generate base64 secrets with this command:

openssl rand -base64 32

• Change the superuser password. Modify the file app/devtools.py with the credentials of your choice:

# from app/devtools.py
def create_superuser(db: Session):
    superuser = User(
        username='superuser@example.com',
        first_name='Super',
        last_name='User',
        hashed_password=pwd_context.hash('8888'),
...

Only an admin can register another admin. A permanent admin can be created once logged-in as the superuser, by sending a post request to this address: http://localhost:8080/admin/create

B. Run app in development mode

superuser argument initializes an admin at startup and deletes it at shutdown.

./run.sh dev superuser

superuser credentials (from app/dev_tools.py):

def create_superuser(db: Session):
    superuser = User(
        username='superuser@example.com',
        first_name='Super',
        last_name='User',
        hashed_password=pwd_context.hash('8888'),
        is_active=True,
        role='admin',
        has_access_sentiment=True,
        has_access_emotion=True
    )
    db.add(superuser)
    db.commit()

The authentication flow

Authentication as Superuser

curl -X 'POST' \
  'http://localhost:8080/auth/token' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/x-www-form-urlencoded' \
  -d 'grant_type=&username=superuser%40example.com&password=8888&scope=&client_id=&client_secret='

Access Machine Learning services

From http://localhost:8080/docs:

1. Authenticate:

   • Click on the lock
   • Fill username (ex: superuser@example.com)
   • Fill password (ex 8888)

2. Send input text

   • Sentiment route: localhost:8080/mlservice/sentiment/predict/interpreted:
   • Emotion route localhost:8080/mlservice/emotion/predict

3. Get output

   • Sentiment:
   • Emotion:

What's ready to use?

3.1 Endpoints and routers

A. Router structure

To achieve separation of concerns and improved code organization, endpoints and helper functions are grouped into logical modules with their own namespaces (ex /admin/...):

• auth router file for registration / security related helpers and endpoint functions (see below)
• admin router file for admin specific actions, like grant/revoke access rights, delete user
• user router file for user specific actions (change password, #TODO get service call history)
• bert_sentiment
• roberta_emotion

B. Endpoints

API endpoints documentation (see below), and HTTP request templates are available at this address: http://localhost:8080/docs

3.2 Natural Language Processing classifiers

A. Sentiment: BERT model

This multilanguage pretrained model returns sentiment probabilities for 5 target labels (0 = very negative to 4 = very positive).

More information on the model here

B. Emotion: Roberta base model

This pretrained model returns emotion probabilities for a total of 28 different emotions, the service selects the 5 most significant labels, returns their name and probability

More information on the model here