Test change

recommendation-api/server.py

@@ -18,6 +18,7 @@ async def startup_event():
     global learn
     tasks = [asyncio.ensure_future(setup_learner(model_filename))]  # assign some task
     learn = (await asyncio.gather(*tasks))[0]
+    print("Model initialized")
 
 @app.get("/users")
 async def get_users(limit: int = Query(10)):

recommendation-api/tracks.py

@@ -1,15 +1,21 @@
 import pandas as pd
 
+print("Initializing data")
+
 # 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)
 
+print("Music info parsed")
+
 # 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()
 
+print("Model data parsed")
+
 # 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')
@@ -46,4 +52,4 @@ def predictions_to_tracks(entries_and_predictions):
             track_dict = track_info.to_dict('records')[0]
             track_dict['score'] = score.astype(str)
             tracks.append(track_dict)
-    return tracks
+    return tracks