import pandas as pd
from fuzzy_controllers import fuzzy_controler_similiarity
from numpy import dot
from numpy.linalg import norm
import json

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):
    all_games = []
    for compared_title in title_list:
        if game_title != compared_title:
            all_games.append({
                "title": compared_title,
                "similarity": compare_games(title_1=game_title, title_2=compared_title, df=df, show_graph=False)
                })
    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']}")
    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: str, title_2: str, df: pd.DataFrame, show_graph: bool = False) -> float:
    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()[:2000]
    run_program = True
    while run_program:
        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":
            run_program = False
        else:
            calculate_similarities(game_title=title, title_list=title_list, df=df)