import numpy as np # helps with the math
import matplotlib.pyplot as plt # to plot error during training
import tensorflow as tf

from rich.progress import Progress

from data.get_data import pull_training_data, normalize_training_data
from data.db_connect import Database
from data.build_weather import get_weather, get_sun_and_moon_phase
from data.stats_importer import Importer

from neuralnet.neuralnetwork import NeuralNetwork

# input data
inputs = np.array([[0, 0, 1, 0],
                   [0, 0, 1, 1],
                   [0, 0, 0, 0],
                   [1, 1, 0, 0],
                   [1, 1, 1, 1],
                   [1, 1, 0, 1]])
# output data
outputs = np.array([[0], [0], [0], [1], [1], [1]])

if __name__ == '__main__':
    db_file = "./database/baseball.db"
    db_conn = Database(db_file)

    query = "SELECT game_date, game_number, park_id FROM games ORDER BY game_date"
    all_games = db_conn.selectall(query)

    compiled_training_data = None
    compiled_training_results = None
    
    with Progress() as p_bar:
        p_bar_val = p_bar.add_task("Getting Data ...", total=len(all_games))
        for game in all_games:
            p_bar.update(p_bar_val, advance=1)
            game_result, training_data = pull_training_data(db_conn, str(game[0]), game[1], game[2])

            training_data = normalize_training_data(training_data)

            if compiled_training_results is None:
                compiled_training_data = np.array([training_data])
                compiled_training_results = np.array([game_result])
            else:
                compiled_training_data = np.append(compiled_training_data, np.array([training_data]), axis=0)
                compiled_training_results = np.append(compiled_training_results, np.array([game_result]), axis=0)

    ctd_len = len(compiled_training_data)
    ctr_len = len(compiled_training_results)

    td = compiled_training_data[:ctd_len-100]
    tdt = compiled_training_data[ctd_len-100:]

    tr = compiled_training_results[:ctr_len-100]
    trt = compiled_training_results[ctr_len-100:]

    nn = NeuralNetwork(len(compiled_training_data[0]))
    nn.train(td, tr)
    nn.summary()
    predictions = nn.predict(np.array(tdt))

    total_num = 0
    accurate_num = 0
    for pred, act in zip(predictions, trt):
        total_num += 1
        pred = round(pred[0] * 1000) / 1000.0
        guess = round(pred)
        print(f"Pred: {pred} -> Gue: {guess} -> Res: {act} -> Cor: {guess == act}")

        accurate_num += int(guess==act)

    print(f"Total: {total_num} -> Accu: {accurate_num} -> Perc: {accurate_num/total_num}")
    #for index in range(len(trt)):
    #    print(f"pred: {nn.predict(np.array([tdt[index]]))} : act : {trt[index]}")

    """
    build_db_path = "./data/sql/build_db .sql"
    fill_parks_path = "./data/sql/prefill_parks.sql"
    fill_teams_path = "./data/sql/prefill_teams.sql"

    db_conn.run_sql_file(build_db_path)
    db_conn.run_sql_file(fill_parks_path)
    db_conn.run_sql_file(fill_teams_path)

    imp = Importer(db_conn)
    imp.parse_all_data("./data/stats/", "./data/stats/imported/")
    """

"""
else:
    # create neural network   
    NN = NeuralNetwork(inputs, outputs)
    # train neural network
    NN.train()

    # create two new examples to predict                                   
    example = np.array([[1, 1, 1, 0]])
    example_2 = np.array([[0, 0, 1, 1]])

    # print the predictions for both examples                                   
    print(NN.predict(example), ' - Correct: ', example[0][0])
    print(NN.predict(example_2), ' - Correct: ', example_2[0][0])

    # plot the error over the entire training duration
    plt.figure(figsize=(15,5))
    plt.plot(NN.epoch_list, NN.error_history)
    plt.xlabel('Epoch')
    plt.ylabel('Error')
    plt.savefig('plot.png')
"""