remove playstation and xbox

app.py

@@ -6,13 +6,12 @@ import itertools
 import matplotlib.pyplot as plt
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import linear_kernel
-#from huggingface_hub import upload_file
-#fuzz = upload_file(path_in_repo="fuzz.py")
 from fuzzywuzzy import fuzz
 from sklearn.feature_extraction.text import TfidfVectorizer
 import gradio as gr
-from datasets import load_dataset
-#dataset = load_dataset('csv', data_files="steam-clean-games.csv") #seyia92coding/steam_games_2019.csv
+#from datasets import load_dataset
+#dataset = load_dataset('csv', data_files="steam-clean-games.csv", streaming=True)
+#df = pd.DataFrame.from_dict(dataset)
 df = pd.read_csv("steam-clean-games.csv",  error_bad_lines=False, encoding='utf-8')
 # the function to extract years
 def extract_year(date):
@@ -141,7 +140,7 @@ years_sorted = sorted(list(df['year'].unique()))
 recommender = gr.Interface(gradio_contents_based_recommender_v2, ["text", gr.inputs.Slider(1, 20, step=int(1)),
                                                             gr.inputs.Radio(['Year','Score','Weighted Score','Total Ratings']),
                                                             gr.inputs.Slider(int(years_sorted[0]), int(years_sorted[-1]), step=int(1)),
-                                                            gr.inputs.Radio(['windows','xbox','playstation','linux','mac']),
+                                                            gr.inputs.Radio(['windows','linux','mac']),
                                                             gr.inputs.Slider(0, 10, step=0.1)],
                         "dataframe")
 recommender.launch(debug=True)