recommendation-api/tracks.py

import pandas as pd

# Read track infos and build the entry representation
tracks_df = pd.read_csv('data/music_info.csv')
tracks_df.fillna('', inplace=True)
tracks_df["entry"] = tracks_df["name"] + ", " + tracks_df["artist"] + ", " + tracks_df["year"].astype(str)

# Raw dataframe from the training set
model_df = pd.read_csv('data/model.csv')
model_interactions_df = model_df[['user_id', 'track_id']]
model_tracks_df = model_df[['entry']].drop_duplicates()

# Create a dictionary where user_id is the key and full track history value
user_to_track_history_df = pd.merge(tracks_df, model_interactions_df, on='track_id', how='left').astype(str)
user_to_track_history_dict = {user_id: group.drop('user_id', axis=1).to_dict('records')
                              for user_id, group in user_to_track_history_df.groupby('user_id')}

print("Count of tracks:", tracks_df.shape[0])
print("Count of interactions (model):", model_interactions_df.shape[0])
print("Count of tracks (model):", model_tracks_df.shape[0])

def get_users_with_track_interactions(ascending=False, limit=10):
    playcount_summary = model_interactions_df.groupby('user_id').size().reset_index(name='track_interactions')
    playcount_summary.sort_values(by='track_interactions', ascending=ascending, inplace=True)
    if limit is not None:
        playcount_summary = playcount_summary.head(limit)
    return playcount_summary.to_dict(orient='records')

def get_top_tracks_for_user(user_id: str, limit=10):
    track_list = user_to_track_history_dict.get(user_id, [])
    sorted_tracks = sorted(track_list, key=lambda x: int(x['playcount']) if 'playcount' in x and x['playcount'].isdigit() else 0, reverse=True)
    if limit is not None:
        sorted_tracks = sorted_tracks[:limit]
    return sorted_tracks

def get_unlistened_tracks_for_user(user_id: str):
    possible_tracks = model_tracks_df['entry'].tolist()
    listened_tracks = [track['entry'] for track in user_to_track_history_dict.get(user_id, [])]
    return list(set(possible_tracks) - set(listened_tracks))

def predictions_to_tracks(entries_and_predictions):
    tracks = []
    for entry, score in entries_and_predictions:
        track_info = tracks_df[tracks_df['entry'] == entry]
        if not track_info.empty:
            track_dict = track_info.to_dict('records')[0]
            track_dict['score'] = score.astype(str)
            tracks.append(track_dict)
    return tracks