FastAPI, Celery, and Redis Project

This project demonstrates a simple but scalable setup using FastAPI, Celery, and Redis, containerized with Docker and orchestrated with Docker Compose.

Description

This application is a basic example of how to integrate FastAPI with Celery for background task processing, using Redis as a message broker and result backend. The setup also includes Flower for monitoring the Celery tasks.

Features

• FastAPI: A modern, fast web framework for building APIs with Python 3.7+.
• Celery: An asynchronous task queue/job queue based on distributed message passing.
• Redis: An in-memory database used as a message broker for Celery.
• Flower: A web-based tool for monitoring and administrating Celery clusters.
• Docker and Docker Compose: For easy development, deployment, and scaling.

Prerequisites

• Docker
• Docker Compose

Getting Started

Setting Up

1. Clone the Repository

   git clone [email protected]:uuid-0/SentimentClassification.git

2. Build and Run with Docker Compose

   docker-compose up --build

This command builds the images for your FastAPI application, Celery worker, Redis, and Flower, and then starts the containers.

Accessing the Application

• FastAPI application will be running at http://localhost:8000
• Flower dashboard for monitoring Celery at http://localhost:5555

Structure

• app/: FastAPI application code.
• Dockerfile: Dockerfile for building the FastAPI application image.
• docker-compose.yml: Docker Compose configuration file.
• requirements.txt: Python dependencies required for the application.

Usage

This FastAPI application provides an endpoint for analyzing sentiment in a given text and another endpoint to check the status and result of the analysis.

Analyzing Sentiment

To analyze the sentiment of a text, send a POST request to the /analyze-sentiment/ endpoint with the text as a parameter. This will enqueue the task in Celery for processing.

Endpoint: POST /analyze-sentiment/

Parameter: text - The text for which you want to analyze sentiment.

Example:

curl -X 'POST' \
  'http://localhost:8000/analyze-sentiment/' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{"text": "Your text here"}'

This will return a JSON response with a task_id, which you can use to check the status of the task.

Checking Task Status

To check the status of the sentiment analysis task, send a GET request to the /task/{task_id} endpoint, replacing {task_id} with the ID returned from the /analyze-sentiment/ endpoint.

Example:

curl -X 'GET' \
  'http://localhost:8000/task/<task_id>' \
  -H 'accept: application/json'

The response will be a JSON object containing the status of the task. If the task is completed, it will also include the result of the sentiment analysis.

License

MIT

Contact me

LinkedIn