Update app.py

app.py

@@ -12,8 +12,8 @@ import numpy as np
 import scipy as sp
 from scipy import sparse
 from sklearn.metrics.pairwise import cosine_similarity
-!pip install fuzzywuzzy
 from fuzzywuzzy import fuzz
+import gradio as gr
 
 meta_df = pd.read_csv("/content/Metacritic_Scores_File.csv",  error_bad_lines=False, encoding='utf-8') 
 meta_df = meta_df[['game', 'reviewer_ID', 'score']]
@@ -77,10 +77,6 @@ def game_recommendation(game):
         print("#" + str(number) + ": " + n + ", " + str(round(game_sim_df[game][n]*100,2)) + "% " + "match")
         number +=1
 
-!pip install gradio
-
-import gradio as gr
-
 recommender_interface = gr.Interface(game_recommendation, ["text"], 
                            ["text"], title="Top 5 Game Recommendations", description="This is a Recommendation Engine based on how Metacritic professional reviewers have scored games up to 2019 (apologies for the out of date data). Simply input a game you have enjoyed playing and it should return 5 games that have been rated similarily")