# -*- coding: utf-8 -*-

"""

Created on Fri Mar 26 11:43:51 2021

@author: JOEL

"""

import pandas as pd

import numpy as np

import tensorflow as tf

import keras

from keras import backend as K

from keras.models import Model

import pickle

import os

def mri_predict(axial,coronal,sagittal):

    axial = axial.reshape(16,256,256,1)

    coronal = coronal.reshape(16,256,256,1)

    sagittal = sagittal.reshape(16,256,256,1)

    axial_cnn = keras.models.load_model(os.path.join("signin","axcnn_new"+".h5"))

    coronal_cnn = keras.models.load_model(os.path.join("signin","corcnn_new"+".h5"))

    sagittal_cnn = keras.models.load_model(os.path.join("signin","sagcnn_new"+".h5"))

    filename = os.path.join("signin","softmax_reg_new"+".sav")

    s_model = pickle.load(open(filename, 'rb')) 

    ax_prediction = 0

    cor_prediction = 0

    sag_prediction = 0

    for i in range(16):

        axialtemp = axial[i,:,:,:]

        axialtemp = axialtemp.reshape(1,256,256,1)

        ax_predict = axial_cnn.predict(axialtemp)

        ax_prediction+=ax_predict 

        coronaltemp = coronal[i,:,:,:]

        coronaltemp = coronaltemp.reshape(1,256,256,1)

        cor_predict = coronal_cnn.predict(coronaltemp)

        cor_prediction+=cor_predict

        sagittaltemp = sagittal[i,:,:,:]

        sagittaltemp = sagittaltemp.reshape(1,256,256,1)

        sag_predict = sagittal_cnn.predict(sagittaltemp)

        sag_prediction+=sag_predict

    fp_sag = sag_prediction/16

    fp_ax = ax_prediction/16

    fp_cor = cor_prediction/16

    fp_ax = np.array(fp_ax)

    fp_cor = np.array(fp_cor)

    fp_sag = np.array(fp_sag)

    combined = np.concatenate((fp_ax,fp_cor,fp_sag),axis = 1)

    fp = int(s_model.predict(combined))

    perc = 0

    perc = (fp_ax[0,fp]+fp_cor[0,fp]+fp_sag[0,fp])/3

    return(fp,perc)