--- a
+++ b/project/signin/prediction.py
@@ -0,0 +1,53 @@
+# -*- 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)