import os
from django import forms
from .models import *
from keras.models import load_model
from keras import backend as keras_backend
import numpy as np
from keras.preprocessing import image


class SignForm(forms.ModelForm):
    """Form with one Image upload field,
    acquiring the image and predicting the letter."""

    class Meta:
        # assign model to the form
        model = Sign
        fields = ['gesture']

    def __init__(self, *args, **kwargs):
        super(SignForm, self).__init__(*args, **kwargs)
        # disable labels and validation errors for input fields.
        self.fields['gesture'].label = ""
        self.fields['gesture'].error_messages = {
            'blank': 'INVALID!!11', 'null': 'NULL11!', 'required': ''}

    def sav(self):
        # get the image uploaded from the form.
        uploaded_image = super(SignForm, self).save()
        image_name = uploaded_image.gesture.name
        keras_backend.clear_session()

        classifier = load_model('Trained_model.h5')

        # Prediction of single image
        loaded_image = image.load_img(
            f'./media/{image_name}', target_size=(64, 64))
        img_array = image.img_to_array(loaded_image)
        img_dims = np.expand_dims(img_array, axis=0)
        classifier_result = classifier.predict(img_dims)
        predicted_char = ''

	#map to the character in the alphabet.
        if classifier_result[0][0] == 1:
            predicted_char = 'A'
        elif classifier_result[0][1] == 1:
            predicted_char = 'B'
        elif classifier_result[0][2] == 1:
            predicted_char = 'C'
        elif classifier_result[0][3] == 1:
            predicted_char = 'D'
        elif classifier_result[0][4] == 1:
            predicted_char = 'E'
        elif classifier_result[0][5] == 1:
            predicted_char = 'F'
        elif classifier_result[0][6] == 1:
            predicted_char = 'G'
        elif classifier_result[0][7] == 1:
            predicted_char = 'H'
        elif classifier_result[0][8] == 1:
            predicted_char = 'I'
        elif classifier_result[0][9] == 1:
            predicted_char = 'J'
        elif classifier_result[0][10] == 1:
            predicted_char = 'K'
        elif classifier_result[0][11] == 1:
            predicted_char = 'L'
        elif classifier_result[0][12] == 1:
            predicted_char = 'M'
        elif classifier_result[0][13] == 1:
            predicted_char = 'N'
        elif classifier_result[0][14] == 1:
            predicted_char = 'O'
        elif classifier_result[0][15] == 1:
            predicted_char = 'P'
        elif classifier_result[0][16] == 1:
            predicted_char = 'Q'
        elif classifier_result[0][17] == 1:
            predicted_char = 'R'
        elif classifier_result[0][18] == 1:
            predicted_char = 'S'
        elif classifier_result[0][19] == 1:
            predicted_char = 'T'
        elif classifier_result[0][20] == 1:
            predicted_char = 'U'
        elif classifier_result[0][21] == 1:
            predicted_char = 'V'
        elif classifier_result[0][22] == 1:
            predicted_char = 'W'
        elif classifier_result[0][23] == 1:
            predicted_char = 'X'
        elif classifier_result[0][24] == 1:
            predicted_char = 'Y'
        elif classifier_result[0][25] == 1:
            predicted_char = 'Z'
        keras_backend.clear_session()

        # remove image after prediction
        try:
            os.remove(f'./media/{image_name}')
        except:
            print("unable to remove image")
        return predicted_char