initial commit for checking 3 titles and setting them to like/don't like

README.md

@@ -5,23 +5,8 @@
     pip install -r requirements.txt
     python main.py
 
-#### To run the project in presentation mode:
-
-    python main.py --pres
-it will generate .json file which can be presented by running all cells of `Fuzzy_presentation.ipynb`
-
-#### Random mode 
-
-    python main.py --pres -r True
-
-#### Evaluation mode
-
-    python main.py --pres --eval 
-generates result.json file with 10 random games and 10 recomendations for each game, results can be evaluated in `Fuzzy_presentation.ipynb` file, with Jaccard Similiarity
-
 Processed dataset files are already provided, but can be created from the base ``games.csv`` file by running:
 
     python process_dataset.py
 
-
 If no ``GoogleNews-vectors-negative300.bin`` file is present, only ``games_processed.csv`` will be created.

app.py

@@ -0,0 +1,109 @@
+from flask import Flask, render_template, request
+import pandas as pd
+from fuzzy_controllers import fuzzy_controler_similiarity
+from numpy import dot
+from numpy.linalg import norm
+import json
+import multiprocessing
+import tqdm
+
+app = Flask(__name__)
+
+
+
+def find_games_categorical_similarity(game_1: pd.DataFrame, game_2: pd.DataFrame) -> float:
+    game_1_categorical = set(game_1['all_categorical'].tolist()[0])
+    game_2_categorical = set(game_2['all_categorical'].tolist()[0])
+    return round(len(game_1_categorical & game_2_categorical) / len(game_1_categorical | game_2_categorical), 2)
+
+
+def find_games_numerical_similarity(game_1: pd.DataFrame, game_2: pd.DataFrame) -> float:
+    game_1_popularity = float(game_1["fuzzy_popularity"].to_string(index=False))
+    game_2_popularity = float(game_2["fuzzy_popularity"].to_string(index=False))
+    return round(abs(game_1_popularity - game_2_popularity), 2)
+
+
+def find_games_word_vector_distance(game_1: pd.DataFrame, game_2: pd.DataFrame) -> float:
+    game_1_vector = game_1['all_categorical_vector'].tolist()[0]
+    game_2_vector = game_2['all_categorical_vector'].tolist()[0]
+    return round(dot(game_1_vector, game_2_vector) / (norm(game_1_vector) * norm(game_2_vector)), 2)
+
+
+def calculate_similarities(game_title, title_list, df):
+    if game_title in title_list:
+        title_list.remove(game_title)
+
+    args_list = []
+    for compared_title in title_list:
+        args_list.append((game_title, compared_title, df))
+
+    similarities = []
+    # call the function for each item in parallel with multiprocessing
+    with multiprocessing.Pool() as pool:
+        for result in pool.starmap(compare_games, tqdm.tqdm(args_list, total=len(args_list), desc='Searching')):
+            similarities.append(result)
+
+    all_games = []
+    for title, similarity in zip(title_list, similarities):
+        all_games.append({
+                    "title": title,
+                    "similarity": similarity
+                    })
+
+    sorted_games = sorted(all_games, key=lambda k: k['similarity'], reverse=True)
+    print("\n ==== Top 20 most similar games: ====")
+    for game in sorted_games[:20]:
+        print(f"- {game['title']}")
+    return sorted_games[:20]
+    # save_results(game_title=game_title, game_list=sorted_games)
+
+def save_results(game_title, game_list):
+    print("The full list of similar games available in the /results directory\n")
+    with open(f"results/similarity_list_{game_title.lower().replace(' ', '_')}.txt", 'w+') as fp:
+        json.dump(game_list, fp)
+
+def compare_games(title_1, title_2, df, show_graph=False):
+    game_1 = df.loc[df['name'] == title_1]
+    game_2 = df.loc[df['name'] == title_2]
+
+    categorical_similarity = find_games_categorical_similarity(game_1=game_1, game_2=game_2)
+    numerical_difference = find_games_numerical_similarity(game_1=game_1, game_2=game_2)
+    word_vector_distance = find_games_word_vector_distance(game_1=game_1, game_2=game_2)
+    similarity_score = fuzzy_controler_similiarity(categorical_data=categorical_similarity,
+                                                   numerical_data=numerical_difference,
+                                                   vector_distance=word_vector_distance, show_graph=show_graph)
+    return similarity_score
+
+
+if __name__ == '__main__':
+
+    df = pd.read_pickle('data/games_processed_vectorized.csv')
+    title_list = df["name"].values.tolist()
+    while True:
+        print("Welcome to Fuzzy Game Reccomender!\nType in a game title and we will find the most similar games from our database")
+        title = input("Enter the title or type 'exit' to leave: ")
+        if title == "exit":
+            break
+        else:
+            calculate_similarities(game_title=title, title_list=title_list, df=df)
+
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+
+@app.route('/', methods=['POST'])
+def form_post():
+    df = pd.read_pickle('data/games_processed_vectorized.csv')
+    first_game = request.form['first_game']
+    second_game = request.form['second_game']
+    third_game = request.form['third_game']
+    processed_text1 = first_game
+    processed_text2 = second_game
+    processed_text3 = third_game
+
+    title_list = df["name"].values.tolist()
+    similarities = calculate_similarities(game_title=processed_text1, title_list=title_list, df=df)
+
+    return similarities

fuzzy_controllers.py

@@ -91,9 +91,9 @@ def fuzzy_controler_similiarity(categorical_data: str, numerical_data: str, vect
     FSS.set_crisp_output_value("big", 1)
 
     # TODO: add Word_vector_distance to rules
-    R1 = "IF (Categorical_similarity IS average) AND (Numerical_difference IS average) THEN (Similarity IS average)"
-    R2 = "IF (Categorical_similarity IS small) AND (Numerical_difference IS big) THEN (Similarity IS small)"
-    R3 = "IF (Categorical_similarity IS big) AND (Numerical_difference IS small) THEN (Similarity IS big)"
+    R1 = "IF (Categorical_similarity IS average) OR (Numerical_difference IS average) THEN (Similarity IS average)"
+    R2 = "IF (Categorical_similarity IS small) OR (Numerical_difference IS big) THEN (Similarity IS small)"
+    R3 = "IF (Categorical_similarity IS big) OR (Numerical_difference IS small) THEN (Similarity IS big)"
 
     FSS.add_rules([R1, R2, R3])
 

main.py

@@ -4,11 +4,8 @@ from numpy import dot
 from numpy.linalg import norm
 import json
 import multiprocessing
-from tqdm.auto import tqdm
-from sys import argv
-import sys, getopt
-import argparse
-import random
+import tqdm
+
 
 def find_games_categorical_similarity(game_1: pd.DataFrame, game_2: pd.DataFrame) -> float:
     game_1_categorical = set(game_1['all_categorical'].tolist()[0])
@@ -28,7 +25,7 @@ def find_games_word_vector_distance(game_1: pd.DataFrame, game_2: pd.DataFrame)
     return round(dot(game_1_vector, game_2_vector) / (norm(game_1_vector) * norm(game_2_vector)), 2)
 
 
-def calculate_similarities(game_title, title_list, df, test=False):
+def calculate_similarities(game_title, title_list, df):
     if game_title in title_list:
         title_list.remove(game_title)
 
@@ -39,7 +36,7 @@ def calculate_similarities(game_title, title_list, df, test=False):
     similarities = []
     # call the function for each item in parallel with multiprocessing
     with multiprocessing.Pool() as pool:
-        for result in pool.starmap(compare_games, tqdm(args_list, total=len(args_list), desc='Searching')):
+        for result in pool.starmap(compare_games, tqdm.tqdm(args_list, total=len(args_list), desc='Searching')):
             similarities.append(result)
 
     all_games = []
@@ -50,7 +47,6 @@ def calculate_similarities(game_title, title_list, df, test=False):
                     })
 
     sorted_games = sorted(all_games, key=lambda k: k['similarity'], reverse=True)
-    if (test): return sorted_games[:20]
     print("\n ==== Top 20 most similar games: ====")
     for game in sorted_games[:20]:
         print(f"- {game['title']}")
@@ -73,73 +69,15 @@ def compare_games(title_1, title_2, df, show_graph=False):
                                                    vector_distance=word_vector_distance, show_graph=show_graph)
     return similarity_score
 
-def get_game_info_from_df(data_games, game_title):
-  finded_game = data_games.loc[data_games["name"] == game_title]
-  # print(finded_game)
-  result_dict = {
-    "title" : finded_game["name"].values[0],
-    "price" : finded_game["price"].values[0],
-    "all_categorical" : finded_game["all_categorical"].values[0],
-  }
-  return result_dict
-
-
-def get_game_info(data_game):
-  # finded_game = data_games.loc[data_games["name"] == game_title]
-  # print(finded_game)
-  result_dict = {
-    "title" : data_game["name"],
-    "price" : data_game["price"],
-    "all_categorical" : data_game["all_categorical"],
-  }
-  return result_dict
-
-def main(argv):
-    df = pd.read_pickle('data/games_processed_vectorized.csv')
-    title_list = df["name"].values.tolist()
-
-    test_mode = False
-    random_mode = False
-    eval_mode = False
-    eval_random_mode = False
-
-    opts, args = getopt.getopt(argv, "r:", ["pres", "eval", "evalrandom"])
-    for opt, arg in opts:
-        if "--pres" == opt: 
-            test_mode = True
-        if "--eval" == opt: 
-            eval_mode = True
-        if "--evalrandom" == opt: 
-            eval_random_mode = True
-        if "-r" == opt: 
-            random_mode = arg
-    if (True == test_mode):
-        game_list = ["Call of Duty®: Modern Warfare® 2", "Project CARS", "DayZ", "STAR WARS™ Jedi Knight - Mysteries of the Sith™", "Overcooked"]
-        if (random_mode): game_list = [random.choice(title_list)]
-        if (eval_mode or eval_random_mode): game_list = [random.choice(title_list) for i in range(10)]
-        result_dict = {"results": []}
-        for item in game_list:
-            if not eval_random_mode:
-                titles_results = calculate_similarities(game_title=item, title_list=title_list, df=df, test=test_mode)
-            if eval_random_mode:
-                titles_results = [{"title": random.choice(title_list)} for i in range(10)]
-            game_result = get_game_info_from_df(df, item)
-            game_result["fuzzy_similiar"] = [get_game_info_from_df(df, title_item["title"]) for title_item in titles_results[:10]]
-            result_dict["results"].append(game_result)
-        with open("results/result.json", "w", encoding="UTF-8") as outfile:
-          json.dump(result_dict, outfile, ensure_ascii=False)
-
-    if (False == test_mode):
-        while True:
-            print("Welcome to Fuzzy Game Reccomender!\nType in a game title and we will find the most similar games from our database")
-            title = input("Enter the title or type 'exit' to leave: ")
-            if title == "exit":
-                break
-            else:
-                calculate_similarities(game_title=title, title_list=title_list, df=df)
-
 
 if __name__ == '__main__':
-  main(sys.argv[1:])
 
-    
+    df = pd.read_pickle('data/games_processed_vectorized.csv')
+    title_list = df["name"].values.tolist()
+    while True:
+        print("Welcome to Fuzzy Game Reccomender!\nType in a game title and we will find the most similar games from our database")
+        title = input("Enter the title or type 'exit' to leave: ")
+        if title == "exit":
+            break
+        else:
+            calculate_similarities(game_title=title, title_list=title_list, df=df)

templates/index.html

@@ -0,0 +1,105 @@
+<head>
+  <script>
+const toggleCheckboxes = document.querySelectorAll('input[type="checkbox"]');
+
+toggleCheckboxes.forEach(checkbox => {
+  checkbox.addEventListener('change', function() {
+    const target = document.getElementById(this.id.replace('toggle', ''));
+    const label = target.previousElementSibling;
+    label.style.visibility = this.checked ? 'visible' : 'hidden';
+  });
+});
+  </script>
+
+    <style>
+        h1 {
+    border: 2px #eee solid;
+    color: brown;
+    text-align: center;
+    padding: 10px;
+}
+
+html, body {
+  height: 100%;
+  margin: 0;
+  padding: 0;
+  background-color: #ADD8E6; /* Light blue color */
+}
+
+form {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  height: 100%;
+  background-color: white;
+  padding: 20px;
+  border-radius: 10px;
+  box-shadow: 0 0 10px gray;
+}
+
+input[type="text"] {
+  width: 50%;
+  padding: 10px;
+  margin: 10px 0;
+  font-size: 16px;
+  background-color: lightgray;
+  border: none;
+  border-radius: 5px;
+}
+
+input[type="submit"] {
+  padding: 10px 20px;
+  font-size: 16px;
+  background-color: lightblue;
+  color: white;
+  border: none;
+  border-radius: 5px;
+  cursor: pointer;
+}
+
+.red-border {
+  border: 2px solid red;
+  display: inline-block;
+  padding: 5px;
+  border-radius: 5px;
+  visibility: hidden;
+}
+
+button.toggle-border {
+  padding: 5px 10px;
+  font-size: 14px;
+  background-color: lightgray;
+  border: none;
+  border-radius: 5px;
+  cursor: pointer;
+  margin-left: 10px;
+}
+
+
+
+    </style>
+</head>
+
+<form action="" method="post">
+  <div>
+    <input type="checkbox" id="first_game_toggle">
+    <label for="first_game_toggle">I don't like this game</label>
+    <label for="first_game" class="red-border">First game:</label>
+    <input type="text" id="first_game" name="first_game">
+  </div>
+  <div>
+    <input type="checkbox" id="second_game_toggle">
+    <label for="second_game_toggle">I don't like this game</label>
+    <label for="second_game" class="red-border">Second game:</label>
+    <input type="text" id="second_game" name="second_game">
+  </div>
+    <div>
+    <input type="checkbox" id="third_game_toggle">
+    <label for="third_game_toggle">I don't like this game</label>
+    <label for="third_game" class="red-border">Second game:</label>
+    <input type="text" id="third_game" name="third_game">
+  </div>
+  <input type="submit" value="Submit">
+</form>
+