Update app.py

app.py

@@ -20,10 +20,10 @@ def predict(team, inning, venue, hits, errors, lob, runs, opp_team, opp_runs, op
     # print(df.columns)
     # print(len(df.columns))
     xgb_model = xgb.XGBRegressor()
-    xgb_model.load_model('xgbr1_exp5_model.json')
+    xgb_model.load_model('xgbr1_exp6_model.json')
     
-    with open('pca_model3.pkl', 'rb') as f:
-        pca = pickle.load(f)
+    # with open('pca_model3.pkl', 'rb') as f:
+    #     pca = pickle.load(f)
 
     # with open('label_encoder_teams_xgbr1_exp3.pkl', 'rb') as f:
     #     label_encoder = pickle.load(f)
@@ -33,7 +33,7 @@ def predict(team, inning, venue, hits, errors, lob, runs, opp_team, opp_runs, op
 
     df = df.astype(int)
 
-    df = pca.transform(df)
+    # df = pca.transform(df)
 
     score = xgb_model.predict(df)
     if score[0] < 0:
@@ -59,7 +59,7 @@ def predict_2(team, inning, venue, hits, errors, lob, runs, opp_team, opp_runs,
     df = df.reindex(columns=df_main.columns, fill_value=0)
 
     cat_model = CatBoostRegressor()
-    cat_model.load_model('catbr1_exp6_model.json')
+    cat_model.load_model('catbr1_exp7_model.json')
     
     # with open('label_encoder_teams_catbr1_exp1.pkl', 'rb') as f:
     #     label_encoder = pickle.load(f)
@@ -72,10 +72,10 @@ def predict_2(team, inning, venue, hits, errors, lob, runs, opp_team, opp_runs,
 
     df = df.astype(int)
 
-    with open('pca_model3.pkl', 'rb') as f:
-        pca = pickle.load(f)    
+    # with open('pca_model3.pkl', 'rb') as f:
+    #     pca = pickle.load(f)    
 
-    df = pca.transform(df)
+    # df = pca.transform(df)
     
     score = cat_model.predict(df)
     if score[0] < 0: