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https://github.com/anras5/songs-classifier
Classify songs' genres with Machine Learning
https://github.com/anras5/songs-classifier
data-science docker machine-learning mlflow pandas python scikit-learn seaborn streamlit tpot
Last synced: 16 days ago
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Classify songs' genres with Machine Learning
- Host: GitHub
- URL: https://github.com/anras5/songs-classifier
- Owner: anras5
- Created: 2024-05-11T16:14:09.000Z (9 months ago)
- Default Branch: master
- Last Pushed: 2024-06-05T21:49:20.000Z (8 months ago)
- Last Synced: 2024-11-20T23:26:15.815Z (3 months ago)
- Topics: data-science, docker, machine-learning, mlflow, pandas, python, scikit-learn, seaborn, streamlit, tpot
- Language: Jupyter Notebook
- Homepage:
- Size: 13.1 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Songs Classifier
**Songs Classifier** is a project that consists of two parts.
- In `Data analysis` you can find Jupyter notebook with data analysis of the dataset and a lot of insights into how the data looks like and how the machine learning models were trained.
- In `Classifier App` you can find an app that uses `Streamlit`, `mlflow` and `spotipy` to allow users to classify songs from Spotify with trained Gradient Boosting Tree. Everything is running in containers.
# How to run?
## Data analysis
```shell
cd data-analysis
docker compose up
```
The jupyter lab is available at `localhost:8888`.
## Classifier App
Create a `.env` file inside the `classifier-app/frontend` directory with the same structure as `.env.example`. Get your own
Spotify credentials from [Spotify Developers Website](https://developer.spotify.com/dashboard).
```shell
cd classifier-app
docker compose up
```
Frontend app is available at `localhost:8080` and backend app (model) is available at `localhost:5000`.
### Develop backend (if you want to change the model)
```shell
cd classifier-app/backend
docker build -f develop.dockerfile -t mlflow-develop-image .
docker run -it -v "${PWD}:/app" -p 5000:5000 --rm mlflow-develop-image /bin/bash
```
Change the `${PWD}` to `$(pwd)` if you are on Linux. If you have already built the image in the past skip the second
command.
**Run the training**
```shell
python3 src/train.py --file_path=/app/data/dataset_cleaned.csv
```
Script prints out `runid` at the end.
**Serve the model**
```shell
mlflow models serve --model-uri runs://GradBoostClassifier --no-conda -p 5000 -h 0.0.0.0
```
# How does the app look like?
