RedBricks - FastAPI Tweet Sentiment Analysis

This project is a FastAPI application for predicting tweet sentiment using a pre-trained LSTM model. It provides an API endpoint where you can input a tweet and get its predicted sentiment.

Features

• API Endpoint: Accepts a tweet text and returns the predicted sentiment.
• LSTM Model: Uses a TensorFlow LSTM model to classify sentiments.
• Deployment: The application is deployed on Koyeb, allowing easy access and testing.
• CI/CD Pipeline: Automated deployment with GitHub Actions when changes are pushed to the repository.

Deployed Application

The application is deployed and accessible at: Koyeb Deployment Link.

Getting Started

1. Clone the Repository

git clone https://github.com/kishon45229/RedBricks.git
cd RedBricks

2. Install Dependencies

Ensure you have Python 3.7+ installed. Then, install the necessary dependencies:

pip install -r requirements.txt

3. Run the Application

To run the application locally, use:

uvicorn main:app --reload

Replace main:app with the appropriate module and app name if different.

4. Access the API Documentation

Once the application is running, navigate to http://127.0.0.1:8000/docs to access the interactive API documentation powered by Swagger UI.

API Endpoint

• POST /predict: Predicts the sentiment of a given tweet.

Request Example:

{
  "tweet": "It's a great day!"
}

Response Example:

{
  "sentiment": "Happy"
}

Deployment on Koyeb

1. Create a Koyeb Account: If you haven't already, create an account on Koyeb.
2. Deploy from GitHub: Connect your GitHub repository and select the FastAPI application repository.
3. Setup Build and Deployment: Koyeb will automatically detect your FastAPI application and create a build. Configure environment variables and any required secrets.
4. Access the Deployed Application: Once deployed, you can access your FastAPI app using the provided Koyeb URL.

CI/CD Pipeline

This project is integrated with a CI/CD pipeline using GitHub Actions. The pipeline includes:

• Linting and Formatting: Ensures code quality and consistency.
• Testing: Runs sample tests to ensure functionality.
• Deployment: Automatically deploys to Koyeb upon successful testing.

Read More

I have written a detailed article on deploying a FastAPI application on Koyeb with an automated CI/CD pipeline using GitHub Actions. You can read it on Medium: Deploying a FastAPI Deep Learning Application on Koyeb with Automated CI/CD Pipeline using GitHub Actions.

This article covers step-by-step instructions on setting up the deployment and CI/CD pipeline, with examples and code snippets to help you follow along.

Contributing

Feel free to fork the repository and submit pull requests. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License.

Acknowledgements

• FastAPI for the web framework.
• Koyeb for the hosting platform.