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https://github.com/mickaelandrieu/nutriscore-app
A project based on OpenFoodFacts data to build a machine learning model able to predict a nutriscore grade
https://github.com/mickaelandrieu/nutriscore-app
healthcare-application machine-learning nutriscore
Last synced: about 1 month ago
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A project based on OpenFoodFacts data to build a machine learning model able to predict a nutriscore grade
- Host: GitHub
- URL: https://github.com/mickaelandrieu/nutriscore-app
- Owner: mickaelandrieu
- License: mit
- Created: 2022-01-06T00:47:06.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-01-11T08:15:09.000Z (almost 3 years ago)
- Last Synced: 2024-04-14T18:21:23.599Z (9 months ago)
- Topics: healthcare-application, machine-learning, nutriscore
- Language: Jupyter Notebook
- Homepage: https://share.streamlit.io/mickaelandrieu/nutriscore-app/main/src/app.py
- Size: 167 MB
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# OpenFoodFacts Nutriscore predictor
## How to get the dataset
As the dataset is too big to be shared on GitHub, you must download it on [OpenFoodFacts (products.csv: 4Go)](https://static.openfoodfacts.org/data/en.openfoodfacts.org.products.csv).
## Local installation
```bash
python -m venv dev
source dev/Scripts/activate
pip install -r requirements.txt
```
## Docker installation
### Build the image
```bash
docker build --tag app:1.0 .
```
### Access the Streamlit application
```bash
docker run --publish 8501:8501 -it app:1.0 -m streamlit run src/app.py
```
Then access [http://localhost:8501](http://localhost:8501).
### Access the REST API
```bash
docker run --publish 8501:8501 -it app:1.0 src/api.py
```
Then access [http://localhost:8501](http://localhost:8501/docs)
> Everytime you update the project, you must build a new image with a new tag.
## Train the model
1. Download the RAW data on OpenFoodFacts ;
2. Execute `notebooks/eda.ipynb` notebook to clean the data ;
3. Execute `notebooks/feature_encoding_and_selection.ipynb` notebook to transform the data ;
4. Execute `src/create_folds.py` to prepare data for training ;
5. Execute `src/train.py` to train the model ;
## Evaluate the performance of the model
```bash
python src/report.py --fold=1
```
> fold value is in range [0,4]
## Test the machine learning model (Without Docker)
### Using a Streamlit Web app
```bash
streamlit run src/app.py
```
### Using a REST API
```bash
python src/api.py
```
## Quality tools
```bash
python -m isort src/
python -m black src/
python -m flake8 src/ --count --statistics
```
## LICENSE
This project is provided under the MIT license.