Sentiment analysis

Projet d'Analyse de sentiment avec BERT et FastAPI

Ce projet est basé sur un entrainement et déploiement d'un modèle de classification binaire en utilisant les Transformers via BERT et FastAPI.

Application web : https://cine-s4gn.vercel.app

API : https://g2-sentiment-analysis-844747804346.us-central1.run.app/docs

Rapport final : https://docs.google.com/document/d/1lDZlo5FaIMBxwYxUze4pZZXf0Ag2MjEX/edit?usp=drivesdk&ouid=109115452173640326314&rtpof=true&sd=true

PowerPoint : https://docs.google.com/presentation/d/1lLk-iH9e87zmPT-Fjl8XBUeJGmiEOiJX/edit?usp=drivesdk&ouid=109115452173640326314&rtpof=true&sd=true

Notebook : https://colab.research.google.com/drive/14fYRsI99CuV09egAwon33Gcwqnfdu6iI#scrollTo=OCNDEYHyx9ZA

Development Requirements

• Python >= 3.10
• Pip
• Poetry (Python Package Manager)

M.L Model Environment

MODEL_PATH=./ml/model/
MODEL_NAME=model.pkl

ENV Variables

Set HF_TOKEN and HF_API_URL in your environment variables

Update /predict

To update your machine learning model, add your load and method change here at predictor.py

Installation

python -m venv venv
source venv/bin/activate
cp .env.example .env
make install

Runnning Localhost

make run

Deploy app

make deploy

Running Tests

make test

Access Swagger Documentation

http://localhost:8080/docs

Access Redocs Documentation

http://localhost:8080/redoc

Project structure

Files related to application are in the app or tests directories. Application parts are:

app
|
| # Fast-API stuff
├── api                 - web related stuff.
│   └── routes          - web routes.
├── core                - application configuration, startup events, logging.
├── models              - pydantic models for this application.
├── services            - logic that is not just crud related.
├── main-aws-lambda.py  - [Optional] FastAPI application for AWS Lambda creation and configuration.
└── main.py             - FastAPI application creation and configuration.
|
| # ML stuff
├── data             - where you persist data locally
│   ├── interim      - intermediate data that has been transformed.
│   ├── processed    - the final, canonical data sets for modeling.
│   └── raw          - the original, immutable data dump.
│
├── notebooks        - Jupyter notebooks. Naming convention is a number (for ordering),
|
├── ml               - modelling source code for use in this project.
│   ├── __init__.py  - makes ml a Python module
│   ├── pipeline.py  - scripts to orchestrate the whole pipeline
│   │
│   ├── data         - scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features     - scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   └── model        - scripts to train models and make predictions
│       ├── predict_model.py
│       └── train_model.py
│
└── tests            - pytest

GCP

Deploying inference service to Cloud Run

Authenticate

1. Install gcloud cli
2. gcloud auth login
3. gcloud config set project <PROJECT_ID>

Enable APIs

1. Cloud Run API
2. Cloud Build API
3. IAM API