revert to old model.pkl

Dockerfile

@@ -6,8 +6,8 @@ COPY recommendation-api .
 COPY data/music_info.csv data/music_info.csv
 COPY data/model.csv data/model.csv
 COPY data/model.pkl data/model.pkl
-COPY data/model_25k.csv data/model_25k.csv
-COPY data/model_25k.pkl data/model_25k.pkl
+COPY data/model_next_25k.csv data/model_next_25k.csv
+COPY data/model_next_25k.pkl data/model_next_25k.pkl
 
 # Install dependencies from the requirements.txt file located within the recommendation-api directory
 RUN pip install -r requirements.txt

recommendation-api/server.py

@@ -10,7 +10,7 @@ from recommender import get_recommendations_for_user
 from learner import setup_learner, custom_accuracy
 
 app = FastAPI()
-model_filename = 'data/model_25k.pkl'
+model_filename = 'data/model.pkl'
 learn = None
 
 @app.on_event("startup")

recommendation-api/tracks.py

@@ -10,7 +10,7 @@ tracks_df["entry"] = tracks_df["name"] + ", " + tracks_df["artist"] + ", " + tra
 print("Music info parsed")
 
 # Raw dataframe from the training set
-model_df = pd.read_csv('data/model_25k.csv')
+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()