Optimization

app.py

@@ -43,12 +43,6 @@ repo_mat = Repository(
     local_dir="data_mat", clone_from=DATASET_REPO_URL_MAT, use_auth_token=HF_TOKEN, repo_type="dataset"
 )
 
-df_ps_train_ori = pd.read_hdf('model/df_ps_train_new.hdf')
-df_ps_train_extra = pd.read_hdf('data_train/df_ps_train_extra.hdf')
-pickle_path = 'model/giantMatrix_new.pickle'
-with open(pickle_path, 'rb') as f:
-    ps_matrix_ori = pickle.load(f)
-
 from fetchPlaylistTrackUris import *
 from recommender import *
 

recommender.py

@@ -4,6 +4,7 @@ from scipy.sparse import csr_matrix
 import numpy as np
 import pandas as pd
 from scipy.sparse import vstack
+import global_var
 
 def add_row_train(df, list_tid):
     new_pid_add = df.iloc[-1].name +1
@@ -25,9 +26,8 @@ def inference_row(list_tid, ps_matrix):
 
 
 def get_best_tid(current_list, ps_matrix_row, K=50, MAX_tid=10):
-
-
-    df_ps_train = pd.concat([df_ps_train_ori,df_ps_train_extra])
+    df_ps_train_extra = pd.read_hdf('data_train/df_ps_train_extra.hdf')
+    df_ps_train = pd.concat([global_var.df_ps_train_ori,df_ps_train_extra])
     
     sim_vector, sparse_row = inference_row(current_list, ps_matrix_row)
     sim_vector = sim_vector.toarray()[0].tolist()
@@ -82,7 +82,7 @@ def inference_from_tid(list_tid, K=50, MAX_tid=10):
     with open("data_mat/giantMatrix_extra.pickle",'rb') as f:
         ps_matrix_extra = pickle.load(f)
     
-    ps_matrix = vstack((ps_matrix_ori,ps_matrix_extra))
+    ps_matrix = vstack((global_var.ps_matrix_ori,ps_matrix_extra))
 
     result, sparse_row = get_best_tid(list_tid, ps_matrix.tocsr(), K, MAX_tid)
     ps_matrix_extra = vstack((ps_matrix_extra,sparse_row.todok()))