# ------------------------------------------------------------------
#     _____ _     _ _
#    |  ___(_) __| | | ___
#    | |_  | |/ _` | |/ _ \
#    |  _| | | (_| | |  __/
#    |_|   |_|\__,_|_|\___|          A small traffic sign classifier
# ------------------------------------------------------------------
# Formation Introduction au Deep Learning (FIDLE) - CNRS/MIAI/UGA
# ------------------------------------------------------------------
# JL Parouty 2023

import numpy as np
import matplotlib.pyplot as plt
import fidle


def show_prediction( prediction, x, y, x_meta ):

    # ---- A prediction is just the output layer
    #
    fidle.utils.subtitle("Output layer from model is (x100) :")
    with np.printoptions(precision=2, suppress=True, linewidth=95):
        print(prediction*100)

    # ---- Graphic visualisation
    #
    fidle.utils.subtitle("Graphically :")
    plt.figure(figsize=(8,2))
    plt.bar(range(43), prediction[0], align='center', alpha=0.5)
    plt.ylabel('Probability')
    plt.ylim((0,1))
    plt.xlabel('Class')
    plt.title('Trafic Sign prediction')
    fidle.scrawler.save_fig('05-prediction-proba')
    plt.show()

    # ---- Predict class
    #
    p = np.argmax(prediction)

    # ---- Show result
    #
    fidle.utils.subtitle('In pictures :')
    print("\nThe image :               Prediction :            Real stuff:")
    fidle.scrawler.images([x,x_meta[p], x_meta[y]], [p,p,y], range(3),  columns=3,  x_size=1.5, y_size=1.5, save_as='06-prediction-images')

    if p==y:
        print("YEEES ! that's right!")
    else:
        print("oups, that's wrong ;-(")