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https://github.com/pechouc/data-certification-api


https://github.com/pechouc/data-certification-api

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README 

        
# URL of Prediction API



https://data-certification-ubkzofvvoq-ew.a.run.app



# Data certification API



Le Wagon Data Science certification exam starter pack for the predictive API test.



**💡  This challenge is completely independent of other challenges. It is not required to complete any other challenge in order to work on this challenge.**



## Setup



### Duplicate the repository for the API challenge



**📝  Let's duplicate the repository of the API challenge.**



Go to https://github.com/lewagon/data-certification-api:

- Click on `Use this template`

- Enter the repository name `data-certification-api`

- Set it as **Public**

- Click on `Create repository from template`

- Click on `Code`

- Select `SSH`

- Copy the SSH URL of the repository (the format is `[email protected]:YOUR_GITHUB_NICKNAME/data-certification-api.git`)



### Clone the repository for the API challenge



**📝  Now we will clone your new repository.**



Open your terminal and run the following commands:



👉  replace `YOUR_GITHUB_NICKNAME` with your **github nickname** and `PASTE_REPOSITORY_URL_HERE` with the SSH URL you just copied:



``` bash

cd ~/code/YOUR_GITHUB_NICKNAME

git clone PASTE_REPOSITORY_URL_HERE

cd data-certification-api

```



### Look around



**💡  The content of the challenge should look like this:**



``` bash

tree

```



```

.

├── Dockerfile

├── MANIFEST.in

├── Makefile

├── README.md

├── api

│   ├── __init__.py

│   └── app.py

├── exampack

│   ├── __init__.py

│   ├── data

│   ├── models

│   ├── predictor.py

│   ├── tests

│   │   └── __init__.py

│   └── utils.py

├── notebooks

├── requirements.txt

├── scripts

│   └── exampack-run

└── setup.py

```



Open your favourite text editor and proceed with the challenge.



## API challenge



**📝  In this challenge, you are provided with a trained model saved as `model.joblib`. The goal is to create an API that will predict the popularity of a song based on its other features.**



👉  You will only need to edit the code of the API in `api/app.py` 🚨



👉  The package versions listed in `requirements.txt` should work out of the box with the pipelined model saved in `model.joblib`



### Install the required packages



The `requirements.txt` file lists the exact version of the packages required in order to be able to load the pipelined model that we provide.



``` bash

pip install -r requirements.txt

```



  👉  If you encounter a version conflict while installing the packages 👈



   



In this case you will need to create a new virtual environment in order to be able to load the pipeline.



👉  Only execute this commands if you encounter an issue while installing the packages 🚨



``` bash

pyenv install 3.8.6

pyenv virtualenv 3.8.6 certif

pyenv local certif

pip install -r requirements.txt

```



### Run a uvicorn server



**📝  Start a `uvicorn` server in order to make sure that the setup works correctly.**



Run the server:



```bash

uvicorn api.app:app --reload

```



Open your browser at http://localhost:8000/



👉  You should see the response `{ "ok": true }`



You will now be able to work on the content of the API while `uvicorn` automatically reloads your code as it changes.



### API specification



**Predict the popularity of a Spotify song**



`GET /predict`



| Parameter | Type | Description |

|---|---|---|

| acousticness | float | whether the track is acoustic |

| danceability | float | describes how suitable a track is for dancing |

| duration_ms | int | duration of the track in milliseconds |

| energy | float | represents a perceptual measure of intensity and activity |

| explicit | int | whether the track has explicit lyrics |

| id | string | id for the track |

| instrumentalness | float | predicts whether a track contains no vocals |

| key | int | the key the track is in |

| liveness | float | detects the presence of an audience in the recording |

| loudness | float | the overall loudness of a track in decibels |

| mode | int | modality of a track |

| name | string | name of the track |

| release_date | string | release date |

| speechiness | float | detects the presence of spoken words in a track |

| tempo | float | overall estimated tempo of a track in beats per minute |

| valence | float | describes the musical positiveness conveyed by a track |

| artist | string | artist who performed the track |



Returns a dictionary with the `artist`, the `name` of the song and predicted `popularity` as an integer.



Example request:



```

/predict?acousticness=0.654&danceability=0.499&duration_ms=219827&energy=0.19&explicit=0&id=0B6BeEUd6UwFlbsHMQKjob&instrumentalness=0.00409&key=7&liveness=0.0898&loudness=-16.435&mode=1&name=Back%20in%20the%20Goodle%20Days&release_date=1971&speechiness=0.0454&tempo=149.46&valence=0.43&artist=John%20Hartford

```



Example response:



``` json

{

  "artist": "John Hartford",

  "name": "Back in the Goodle Days",

  "popularity": 28

}

```



👉 It is your turn, code the endpoint in `api/app.py`. If you want to verify what data types the pipeline expects, have a look at the docstring of the `create_pipeline` method in `exampack/trainer.py`.



## API in production



**📝  Push your API to production on the hosting service of your choice.**



  👉  If you opt for Google Cloud Platform 👈



   



Once you have changed your `GCP_PROJECT_ID` in the `Makefile`, run the following commands to build and deploy your containerized API to Container Registry and finally Cloud Run.