diff --git a/iSeefood/SeefoodAI.py b/iSeefood/SeefoodAI.py
index 9575573..7c4cbbf 100644
--- a/iSeefood/SeefoodAI.py
+++ b/iSeefood/SeefoodAI.py
@@ -20,15 +20,16 @@
 import numpy as np
 import tensorflow as tf
 from PIL import Image
+import os
+
 
 class SeefoodAI(object):
-    # Single private instance 
+    # Single private instance
     __instance = None
-    
-    # Initilize global variables. 
-    global sess, class_scores, x_input, keep_prob
-    
-    # Initilize an AI object to be running 
+    # Initilize global variables.
+    global sess, class_scores, x_input, keep_prob, scores
+
+    # Initilize an AI object to be running
     def __init__(self):
         ''' Virtually private constructor '''
         if SeefoodAI.__instance != None:
@@ -37,25 +38,24 @@ def __init__(self):
             SeefoodAI.__instance = self
             SeefoodAI.__instance.__setup()
         print "+ Seefood AI instance has been created!"
-        
-    
+
     @staticmethod
     def getInstance():
         ''' Static access method '''
         if SeefoodAI.__instance is None:
             SeefoodAI()
         return SeefoodAI.__instance
-    
 
     def __setup(self):
         ''' Setting-up the SeefoodAI instance'''
-        # try initializing the AI instance attrs, catch possible errors. 
-        # TODO: Make it pretty :) 
-        global sess, class_scores, x_input, keep_prob    
+        # try initializing the AI instance attrs, catch possible errors.
+        # TODO: Make it pretty :)
+        global sess, class_scores, x_input, keep_prob
 
         try:
-            sess = tf.Session() 
-            saver = tf.train.import_meta_graph('saved_model/model_epoch5.ckpt.meta')
+            sess = tf.Session()
+            saver = tf.train.import_meta_graph(
+                'saved_model/model_epoch5.ckpt.meta')
             saver.restore(sess, tf.train.latest_checkpoint('saved_model/'))
             graph = tf.get_default_graph()
             x_input = graph.get_tensor_by_name('Input_xn/Placeholder:0')
@@ -67,11 +67,15 @@ def __setup(self):
             print '++++++ [No errors occured during initialization +++++'
 
         print("+ Setting up instance ....")
- 
+
     def process(self, image_path):
         '''TODO: Accept file path '''
-        global sess, class_scores, x_input, keep_prob    
-        # Open passed image, then convert it to RGB 
+
+        if not self.pathValidation(image_path):  # Validate given path.
+            return -1
+
+        global sess, class_scores, x_input, keep_prob
+        # Open passed image, then convert it to RGB
         image = Image.open(image_path).convert('RGB')
         # Resize image to 227x227
         image = image.resize((227, 227), Image.BILINEAR)
@@ -79,26 +83,50 @@ def process(self, image_path):
         img_tensor = [np.asarray(image, dtype=np.float32)]
         print '+ Looking for food in ' + image_path + ' ...... '
 
-        #Run the image in the model.
+        # Run the image in the model.
         scores = sess.run(class_scores, {x_input: img_tensor, keep_prob: 1.})
-        
-        print '+ Statistics: ', scores
+        self.__setScores(scores) # Set score variable
+        print '+ Statistics: ', self.getScores()
         # Calculate score and display result
-        self.__scoresCalculation(scores)
+
+        self.__setScores(scores)
         print("_________ Analyzing image.... Done! __________")
 
     def pathValidation(self, filePath):
         ''' TODO: Validate given file path '''
-        if isinstance(filePath, basestring):
+        if isinstance(filePath, basestring):  # Verify that instance is a string type & !empty.
+            if self.checkFileExtension(filePath) and self.directoryExist(filePath):  # Verify path existance
+                print '+ Path validation ... '
+            # check if the path end with .png || .jpg
             return True
         else:
             return False
 
-    def __scoresCalculation(self, scores):
+    def directoryExist(self, filePath):
+        ''' Verify the existance of the given path '''
+        if os.path.exists(filePath):
+            return True
+        return False
+
+    def checkFileExtension(self, filePath):
+        ''' Verify that the given path points to an image file (png, jpg) '''
+        if filePath.endswith('.png') or filePath.endswith('.jpg'):
+            return True
+        else:
+            return False
+
+    def __setScores(self, stat):
         ''' TODO: Optimaze and generate a final score'''
+        global scores
+        scores = stat
         # if np.argmax = 0; then the first class_score was higher, e.g., the model sees food.
         # if np.argmax = 1; then the second class_score was higher, e.g., the model does not see food.
         if np.argmax(scores) == 1:
             print "+ Result:  Oops! No food here... :( "
         else:
             print "+ Results: YAY! I see food! :)"
+
+    def getScores(self):
+        ''' Return last analyzed image stat. '''
+        global scores
+        return scores