recommendation-api/recommender.py

from fastai.learner import Learner
import pandas as pd

from tracks import get_unlistened_tracks_for_user, predictions_to_tracks

def get_recommendations_for_user(learn: Learner, user_id: str, limit: int = 5):
    not_listened_tracks = get_unlistened_tracks_for_user(user_id)
    print(len(not_listened_tracks))

    # Get predictions for the tracks user hasn't listened yet
    input_dataframe = pd.DataFrame({'user_id': [user_id] * len(not_listened_tracks), 'entry': not_listened_tracks})
    test_dl = learn.dls.test_dl(input_dataframe)
    predictions = learn.get_preds(dl=test_dl)

    # Associate them with prediction score and sort
    tracks_with_predictions = list(zip(not_listened_tracks, predictions[0].numpy()))
    tracks_with_predictions.sort(key=lambda x: x[1], reverse=True)

    print(tracks_with_predictions[:limit])

    # Pick n and return as full tracks
    recommendations = predictions_to_tracks(tracks_with_predictions[:limit])

    return {
        "user_id": user_id,
        "limit": limit,
        "recommendations": recommendations
    }