--- a
+++ b/modules/NeuralNet/management/predictManager.py
@@ -0,0 +1,36 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import tensorflow as tf
+import sys
+from Status.Status import Status
+sys.path.append('/home/skjena/cnnCancerTherapy/modules/NeuralNet/core/classifiers/dnnClassifier')
+sys.path.append('/home/skjena/cnnCancerTherapy/modules/NeuralNet/core/regressors/dnnRegressor')
+
+from NeuralNet.core.classifiers.dnnClassifier import DNNClassifierModel
+#from NeuralNet.core.regressors.dnnRegressor import DNNRegressorModel
+from NeuralNet.core.classifiers.dnnClassifier import dataProcessor
+
+class predictManager():
+	def __init__(self, predict_x, expected):
+		self.predict_x = predict_x
+		self.expected = expected
+
+	def run(self, dnnModel, problemType):
+		self.status.message(1, "run(self, model)")
+		if(problemType == "0"):
+			predictions = dnnModel.predict(
+			input_fn=lambda:dataProcessor.predict_input_fn(self.predict_x,
+			labels=None, batch_size=DNNClassifierModel.getBatchSize()))
+		else:
+			predictions = dnnModel.predict(
+			input_fn=lambda:dataProcessor.predict_input_fn(self.predict_x,
+			labels=None, batch_size=DNNRegressorModel.getBatchSize()))
+
+		for pred_dict, expec in zip(predictions, expected):
+			template = ('\nPrediction is "{}" ({:.1f}%), expected "{}"')
+			class_id = pred_dict['class_ids'][0]
+			probability = pred_dict['probabilities'][class_id]
+			# print the correct answer's label, it's probability scaled into a percentage, and the expected class from the list
+		print(template.format(dataProcessor.TUMOR[class_id], 100 * probability, expec))
+		self.status.message(0, "run(self, model)")
+		return