Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""HS_Recomm_Metacritic_Gradio.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1cIAUS8Z2U2DXPEVmRdou9mqI0vwNT0_0
+"""
+
+import pandas as pd
+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
+
+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']]
+
+df_game_names = pd.read_csv("/content/Game_Titles_IDs.csv",  error_bad_lines=False, encoding='utf-8')
+
+#We will create a pivot table of users as rows and games as columns. 
+#The pivot table will help us make the calcuations of similarity between the reviewers.
+pivot = meta_df.pivot_table(index=['reviewer_ID'], columns=['game'], values='score')
+
+#Applying lambda function to multiple rows using Dataframe.apply()
+#(x-np.mean(x))/(np.max(x)-np.min(x)) = Formula
+pivot_n = pivot.apply(lambda x: (x-np.mean(x))/(np.max(x)-np.min(x)), axis=1)
+
+# step 2 - Fill NaNs with Zeros
+pivot_n.fillna(0, inplace=True)
+
+# step 3 - Transpose the pivot table
+pivot_n = pivot_n.T
+
+# step 4 - Locate the columns that are not zero (unrated)
+pivot_n = pivot_n.loc[:, (pivot_n != 0).any(axis=0)]
+
+# step 5 - Create a sparse matrix based on our pivot table
+piv_sparse = sp.sparse.csr_matrix(pivot_n.values)
+
+#Compute cosine similarity between samples in X and Y.
+game_similarity = cosine_similarity(piv_sparse)
+
+#Turn our similarity kernel matrix into a dataframe
+game_sim_df = pd.DataFrame(game_similarity, index = pivot_n.index, columns = pivot_n.index)
+
+# 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
+   return fuzz.ratio(a,b)
+   # exactly the same, the score becomes 100
+
+# a function to convert index to title
+def get_title_from_index(index):
+  return df_game_names.iloc[index]['game']
+
+# a function to return the most similar title to the words a user type
+def find_closest_title(title):
+  #matching_score(a,b) > a is the current row, b is the title we're trying to match
+   leven_scores = list(enumerate(df_game_names['game'].apply(matching_score, b=title)))
+   sorted_leven_scores = sorted(leven_scores, key=lambda x: x[1], reverse=True)
+   closest_title = get_title_from_index(sorted_leven_scores[0][0])
+   distance_score = sorted_leven_scores[0][1]
+   return closest_title, distance_score
+   # Bejeweled Twist, 100
+
+def game_recommendation(game):
+    #Insert closest title here
+    game, distance_score = find_closest_title(game)
+    #Counter for Ranking
+    number = 1
+    print('Recommended because you played {}:\n'.format(game))
+    
+    for n in game_sim_df.sort_values(by = game, ascending = False).index[1:6]:
+        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")
+
+recommender_interface.launch(debug=True)