nneji123 / media-recommendation-engine Goto Github PK

Media Recommendation Engine, an API that recommends content such as movies, tv shows, anime, songs etc. Built with FastAPI.

• Features
• API Reference
• Screenshots
• Demo
• Run on Local Machine
• Deployment
• Contributing
• Technologies Used
• FAQ
• License

• Light/dark mode toggle
• Live previews
• Fullscreen mode
• Cross platform

  GET /api/home

  GET /api/items/${id}

Takes two numbers and returns the sum.

1. Clone the repository to your local machine:

git clone https://github.com/Nneji123/Media-Recommendation-Engine.git

2. Install the requirements:

cd Media-Recommendation-Engine
pip install -r requirements.txt

3. Open a bash/cmd in the directory and run:

uvicorn app:app --reload --port 8000

4. After the above steps have been carried out you can now view the documentation of the API.

To visit the FastAPI documentation go to http://localhost:8000/ with a web browser.

1. Make sure the API is running already from the above steps.
2. Install locust:

pip install locust

3. Run locust tests

  cd tests
  locust -f locust_test_load.py

4. Set the number of IP's and address and then run the load tests

1. Install the requirements

pip install -r requirements.txt

2. Change the directory

cd tests

3. Run the tests with pytest

pytest test_api.py --html=pytest_report.html --self-contained-html

Assuming you have git and heroku cli installed just carry out the following steps:

1. Clone the repository

git clone https://github.com/Nneji123/Media-Recommendation-Engine.git

2. Change the working directory

cd Media-Recommendation-Engine

3. Create the heroku app

heroku create your-app-name 

Replace your-app-name with the name of your choosing.

4. Set the heroku cli git remote to that app

heroku git:remote your-app-name

5. Set the heroku stack setting to container

heroku stack:set container

6. Push to heroku

git push heroku main

You can also deploy the API to AWS using a free tier EC2 instance by watching the video below:

Contributions are always welcome!

See CONTRIBUTING.MD for ways to get started.

Please adhere to this project's code of conduct.

This API can be used to suggest recommendations for an end user such as movies, games, songs etc.

Yes

MIT