adding rayan prompt

utils.py

@@ -120,11 +120,11 @@ def get_optimized_prediction(modellocation, model_filename, bucket_name, selecte
     y_test = import_data("emailcampaigntrainingdata", 'modelCC/ytest.csv')
     
     # load model from S3
-    key = modellocation + model_filename
-    with tempfile.TemporaryFile() as fp:
-        s3_client.download_fileobj(Fileobj=fp, Bucket=bucket_name, Key=key)
-        fp.seek(0)
-        regr = joblib.load(fp)
+    # key = modellocation + model_filename
+    # with tempfile.TemporaryFile() as fp:
+    #     s3_client.download_fileobj(Fileobj=fp, Bucket=bucket_name, Key=key)
+    #     fp.seek(0)
+    #     regr = joblib.load(fp)
 #     print(type(regr))
     ########### SAVE MODEL #############
 #     filename = 'modelCC.sav'
@@ -137,8 +137,9 @@ def get_optimized_prediction(modellocation, model_filename, bucket_name, selecte
 #     loaded_model = pickle.load(open(filename, 'rb'))
 #     result = loaded_model.score(X_test, Y_test)
     ########################################
-    y_pred = regr.predict(X_test)
-    r2_test = r2_score(y_test, y_pred)
+    regr = joblib.load('models/models.sav')
+    # y_pred = regr.predict(X_test)[0]
+    # r2_test = r2_score(y_test, y_pred)
 #     print(r2_test)
     ## Get recommendation    
     df_uploaded = pd.DataFrame(columns=['character_cnt', "url_cnt", "industry"])