🎬 Project by TomMakesThings, rogerchenrc and laviniafr 🎬

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A̲b̲o̲u̲t̲

This is a natural language processing (NLP) group project in which we tested different NLP techniques and model architectures to create a CI/CD pipeline to train and deploy a multi-label classifier. The classifier was trained on dataset of movie descriptions to predict the top fitting genre(s) with 12 possible values including: Drama, Comedy, Action, Crime, Thriller, Romance, Horror, Adventure, Mystery, Family, Fantasy and Sci-Fi. The state of the best trained model was then saved to file and deployed on a custom built web server. For more information, see our GitHub pages site.

R̲u̲n̲t̲i̲m̲e̲ I̲n̲s̲t̲r̲u̲c̲t̲i̲o̲n̲s̲

Conda environment:

To ensure all team members could execute the code during development, it was created using a conda environment. This environment has been saved as a YAML file, environment.yml, and is included in the repository. To recreate this environment:

1. Download the code in the main repository from Code ⇨ Download ZIP
2. Extract the contents of the zip
3. Open the Anaconda prompt and navigate to the folder of the extracted code, e.g. cd Downloads/Movie-Genre-Predictor
4. Enter conda env create -f environment.yml, where environment.yml is the file path of the enviroment file

To run the classifier:

5. From the Anaconda prompt, run python Web_App/flaskr/main.py to run the web application

To run the Jupyter notebooks:

6. From the Anaconda prompt, run jupyter notebook
7. Navigate to the notebook