API Deployment – Air Paradis Project

📘 This documentation is also available in French 🇫🇷

This folder contains the code and configuration necessary to deploy the sentiment prediction API.

🎯 Objective

Deploy a REST API that allows to:

Predict the sentiment of a tweet (positive or negative) using an LSTM model trained on embeddings (Word2Vec or GloVe),
Report prediction errors.

The API uses the best-performing model registered on MLflow and serves it via FastAPI.

📦 Overall Architecture

Diagram:

    ┌──────────────────────────────┐
    │    End user (UI)             │
    │    (Streamlit interface)     │
    └────────────┬─────────────────┘
                 │
      [HTTP call to /predict]
                 │
    ┌────────────▼─────────────┐
    │       FastAPI API        │
    │  (server launched via    │
    │   Uvicorn)               │
    └────────────┬─────────────┘
                 │
    Model loading from MLflow
                 │
      Tweet preprocessing
                 │
          Prediction (inference)
                 │
       Sentiment response (JSON)

🧪 API Workflow

Model loading
- The best performing model is registered in MLflow under a fixed name (SentimentAnalysisLSTM) and a Production stage.
- The API loads this model at startup using the URI:
```
models:/SentimentAnalysisLSTM/Production
```
Text preprocessing
- Text is cleaned, tokenized, lemmatized, and converted into vectors (embeddings).
Prediction
- The LSTM model (loaded from MLflow) makes a prediction.
- The result (positive or negative) is returned as JSON.
User interface (optional)
- A Streamlit interface allows visual interaction with the API.

🐳 Deployment with Docker

A Dockerfile is provided for easy deployment on any Docker-compatible host (NAS, cloud, etc.).

This Docker image should be pushed to DockerHub with every validated version (see CI/CD below).

For data sovereignty, deployment is preferably done on a NAS with Intel N100 CPU (energy-efficient, sufficient for inference).
Docker can be automatically redeployed using:

cron (simple), or
Watchtower in docker-compose/kubernetes setups.

Development command (local test):

docker build -t sentiment-api .
docker run --rm --env-file ../../.env -p 8000:8000 -p 8080:8080 sentiment-api

In production (e.g. via docker-compose or cloud services), environment variables must be defined in the compose file or secret manager.

🛠 Notes on AVX2 / Cloud Deployment

Update: the NAS initially failed due to lack of AVX2 support. Some N100-based NAS motherboards disable AVX2 in BIOS.

When not accessible, fallback deployment is done on Google Cloud.
Issue: public cloud services do not allow multiple ports to be exposed easily.

Two solutions:

Split into two containers: FastAPI + Streamlit,
Use a reverse proxy (e.g., Nginx) to map ports.

➡️ We chose the second option to keep a single Docker image.

Update 2: AVX2 was enabled via BIOS. Deployment is now active both locally (NAS) and on cloud, with continuous deployment on NAS (see section below).

🔁 CI/CD – GitHub Actions

A pipeline is defined in .github/workflows/ to:

Run unit tests on every push,
Check API health before deployment (local/cloud),
Build and push Docker image to DockerHub.

Tests are defined in:

deployment/api/tests/

Docker image auto-redeployment on NAS is handled by Watchtower, which checks DockerHub updates (like a cron job, with logs).

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