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