Update app.py

app.py

@@ -9,12 +9,11 @@ Original file is located at
 
 import pandas as pd
 import numpy as np
-!pip install fuzzywuzzy
 from fuzzywuzzy import fuzz
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 
-df = pd.read_csv("/content/Metacritic_Reviews_Only.csv",  error_bad_lines=False, encoding='utf-8')
+df = pd.read_csv("Metacritic_Reviews_Only.csv",  error_bad_lines=False, encoding='utf-8')
 
 #Remove title from review
 def remove_title(row):
@@ -48,18 +47,6 @@ cosine_similarity_array = cosine_similarity(tfidf_df)
 # Create a DataFrame from the cosine_similarity_array with tfidf_df.index as its rows and columns.
 cosine_similarity_df = pd.DataFrame(cosine_similarity_array, index=tfidf_df.index, columns=tfidf_df.index)
 
-# Print the top 5 rows of the DataFrame
-# cosine_similarity_df.head()
-
-# # Find the values for the game Batman: Arkham City
-# cosine_similarity_series = cosine_similarity_df.loc['Batman: Arkham City']
-
-# # Sort these values highest to lowest
-# ordered_similarities = cosine_similarity_series.sort_values(ascending=False)
-
-# # Print the results
-# print(ordered_similarities)
-
 # create a function to find the closest title
 def matching_score(a,b):
   #fuzz.ratio(a,b) calculates the Levenshtein Distance between a and b, and returns the score for the distance
@@ -81,7 +68,7 @@ def find_closest_title(title):
    return closest_title, distance_score
    # Bejeweled Twist, 100
 
-find_closest_title('Batman Arkham Knight')
+#find_closest_title('Batman Arkham Knight')
 
 """# Build Recommender Function
 
@@ -139,9 +126,7 @@ def recommend_games(game1, game2, game3, keyword1, keyword2, keyword3, max_resul
         continue
 
 
-recommend_games('Mortal Kombat', 'Street Fighter', 'Overwatch', 'Kombat', 'Fighter', 'Overwatch', 5)
-
-!pip install gradio
+# recommend_games('Mortal Kombat', 'Street Fighter', 'Overwatch', 'Kombat', 'Fighter', 'Overwatch', 5)
 
 import gradio as gr