Create app.py

app.py

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+# -*- coding: utf-8 -*-
+import pandas as pd
+import numpy as np
+import re
+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("seyia92coding/steam-clean-games-2019")
+df = pd.read_csv(dataset,  error_bad_lines=False, encoding='utf-8')
+# the function to extract years
+def extract_year(date):
+   year = date[:4]
+   if year.isnumeric():
+      return int(year)
+   else:
+      return np.nan
+df['year'] = df['release_date'].apply(extract_year)
+df['steamspy_tags'] = df['steamspy_tags'].str.replace(' ','-')
+df['genres'] = df['steamspy_tags'].str.replace(';',' ')
+counts = dict()
+for i in df.index:
+   for g in df.loc[i,'genres'].split(' '):
+      if g not in counts:
+         counts[g] = 1
+      else:
+         counts[g] = counts[g] + 1
+def create_score(row):
+  pos_count = row['positive_ratings']  
+  neg_count = row['negative_ratings']
+  total_count = pos_count + neg_count
+  average = pos_count / total_count
+  return round(average, 2)
+def total_ratings(row):
+  pos_count = row['positive_ratings']  
+  neg_count = row['negative_ratings']
+  total_count = pos_count + neg_count
+  return total_count
+df['total_ratings'] = df.apply(total_ratings, axis=1)
+df['score'] = df.apply(create_score, axis=1)
+# Calculate mean of vote average column
+C = df['score'].mean()
+m = df['total_ratings'].quantile(0.90)
+# Function that computes the weighted rating of each game
+def weighted_rating(x, m=m, C=C):
+    v = x['total_ratings']
+    R = x['score']
+    # Calculation based on the IMDB formula
+    return round((v/(v+m) * R) + (m/(m+v) * C), 2)
+# Define a new feature 'score' and calculate its value with `weighted_rating()`
+df['weighted_score'] = df.apply(weighted_rating, axis=1)
+# create an object for TfidfVectorizer
+tfidf_vector = TfidfVectorizer(stop_words='english')
+tfidf_matrix = tfidf_vector.fit_transform(df['genres'])
+# create the cosine similarity matrix
+sim_matrix = linear_kernel(tfidf_matrix,tfidf_matrix)
+# 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)
+"""# Make our Recommendation Engine
+We need combine our formatted dataset with the similarity logic to return recommendations. This is also where we can fine-tune it if we do not like the results.
+"""
+##These functions needed to return different attributes of the recommended game titles
+#Convert index to title_year
+def get_title_year_from_index(index):
+   return df[df.index == index]['year'].values[0]
+#Convert index to title
+def get_title_from_index(index):
+   return df[df.index == index]['name'].values[0]
+#Convert index to title
+def get_index_from_title(title):
+   return df[df.name == title].index.values[0]
+#Convert index to score
+def get_score_from_index(index):
+   return df[df.index == index]['score'].values[0]
+#Convert index to weighted score
+def get_weighted_score_from_index(index):
+   return df[df.index == index]['weighted_score'].values[0]
+#Convert index to total_ratings
+def get_total_ratings_from_index(index):
+   return df[df.index == index]['total_ratings'].values[0]
+#Convert index to platform
+def get_platform_from_index(index):
+  return df[df.index == index]['platforms'].values[0]
+   
+# 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['name'].apply(matching_score, b=title))) #[(0, 30), (1,95), (2, 19)~~] A tuple of distances per index
+   sorted_leven_scores = sorted(leven_scores, key=lambda x: x[1], reverse=True) #Sorts list of tuples by distance [(1, 95), (3, 49), (0, 30)~~]
+   closest_title = get_title_from_index(sorted_leven_scores[0][0])
+   distance_score = sorted_leven_scores[0][1]
+   return closest_title, distance_score
+def gradio_contents_based_recommender_v2(game, how_many, sort_option, min_year, platform, min_score):
+  #Return closest game title match
+  closest_title, distance_score = find_closest_title(game)
+  #Create a Dataframe with these column headers
+  recomm_df = pd.DataFrame(columns=['Game Title', 'Year', 'Score', 'Weighted Score', 'Total Ratings'])
+  #find the corresponding index of the game title
+  games_index = get_index_from_title(closest_title)
+  #return a list of the most similar game indexes as a list
+  games_list = list(enumerate(sim_matrix[int(games_index)]))
+  #Sort list of similar games from top to bottom
+  similar_games = list(filter(lambda x:x[0] != int(games_index), sorted(games_list,key=lambda x:x[1], reverse=True)))
+  #Print the game title the similarity matrix is based on
+  print('Here\'s the list of games similar to '+'\033[1m'+str(closest_title)+'\033[0m'+'.\n')
+  #Only return the games that are on selected platform
+  n_games = []
+  for i,s in similar_games:
+    if platform in get_platform_from_index(i):
+      n_games.append((i,s))
+  #Only return the games that are above the minimum score
+  high_scores = []
+  for i,s in n_games:
+    if get_score_from_index(i) > min_score:
+      high_scores.append((i,s))
+    
+  #Return the game tuple (game index, game distance score) and store in a dataframe
+  for i,s in n_games[:how_many]: 
+    #Dataframe will contain attributes based on game index
+    row = {'Game Title': get_title_from_index(i), 'Year': get_title_year_from_index(i), 'Score': get_score_from_index(i), 
+           'Weighted Score': get_weighted_score_from_index(i), 
+           'Total Ratings': get_total_ratings_from_index(i),}
+    #Append each row to this dataframe       
+    recomm_df = recomm_df.append(row, ignore_index = True)
+  #Sort dataframe by Sort_Option provided by user
+  recomm_df = recomm_df.sort_values(sort_option, ascending=False)
+  #Only include games released same or after minimum year selected
+  recomm_df = recomm_df[recomm_df['Year'] >= min_year]
+  return recomm_df
+#Create list of unique calendar years based on main df column
+years_sorted = sorted(list(df['year'].unique()))
+#Interface will include these buttons based on parameters in the function with a dataframe output
+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.Slider(0, 10, step=0.1)],
+                        "dataframe")
+recommender.launch(debug=True)