diff --git a/challenge/sample_student.py b/challenge/sample_student.py
index bd9d924..0257480 100644
--- a/challenge/sample_student.py
+++ b/challenge/sample_student.py
@@ -27,11 +27,90 @@ def classify(im):
     Returns: One of three strings: 'brick', 'ball', or 'cylinder'
     
     '''
+    #im = imread(r"C:\Users\haris\images\ball_5.jpg");
+    def convert_to_grayscale_image(im):
+        gray_image=np.mean(im,axis=2);
+        return gray_image;
+    gray_img=convert_to_grayscale_image(im/255.)
+    def make_gaussian_kernel(size, sigma):
+    
+        kernel = np.zeros((size, size))
+    
+        #Handle sigma = 0 case (will result in dividing by zero below if unchecked)
+        if sigma == 0:
+            return kernel 
+
+        #Helpful for indexing:
+        k = int((size-1)/2)
+
+        for i in range(size):
+            for j in range(size):
+                kernel[i, j] = (1/(2*np.pi*sigma**2))*np.exp(-((i-k)**2 + (j-k)**2)/(2*sigma**2))
+
+        return kernel
+    gaussian_kernel = make_gaussian_kernel(size = 40, sigma = 1.0)
+    def filter_2d(im, kernel):
+        '''
+        Filter an image by taking the dot product of each 
+        image neighborhood with the kernel matrix.
+        Args:
+        im = (H x W) grayscale floating point image
+        kernel = (M x N) matrix, smaller than im
+        Returns: 
+        (H-M+1 x W-N+1) filtered image.
+        '''
+
+        M, N = kernel.shape
+        H, W = im.shape
+        filtered_image = np.zeros((H-M+1, W-N+1), dtype = 'float64')
+
+        for i in range(filtered_image.shape[0]):
+            for j in range(filtered_image.shape[1]):
+                image_patch = im[i:i+M, j:j+N]
+                filtered_image[i, j] = np.sum(np.multiply(image_patch, kernel))
+
+        return filtered_image
+    
+    filterd_image = filter_2d(gray_img,gaussian_kernel)
+    
+    #Implement Sobel kernels as numpy arrays
+    Kx = np.array([[1, 0, -1],
+                   [2, 0, -2],
+                   [1, 0, -1]])
+
+    Ky = np.array([[1, 2, 1],
+                   [0, 0, 0],
+                   [-1, -2, -1]])
+    Gx = filter_2d(filterd_image, Kx)
+
+    Gy = filter_2d(filterd_image, Ky)
+
+    G = np.sqrt(Gx**2+Gy**2)
+    magnitude_sum=np.sum(G)
+    
+    G_direction = np.arctan2(Gy, Gx)
+    
+    def tune_thresh(thresh = 0):
+        fig = figure(0, (8,8))
+        imshow(G > thresh)
+    
+    thresh = 0.30
+    edges_and_angles = np.zeros(G.shape)*np.NaN #Create empty array of NaNs
+    #Replace pixels with gradient estimates above thresh with the direction of the gradient estimate:
+    edges_and_angles[G>thresh] = G_direction[G>thresh]
+    
+
     
     
 
     #Let's guess randomly! Maybe we'll get lucky.
     labels = ['brick', 'ball', 'cylinder']
-    random_integer = np.random.randint(low = 0, high = 3)
+
+    if magnitude_sum>4000:
+        output_result = 'brick'
+    elif 1800  <= magnitude_sum < 2800:
+        output_result = 'ball'
+    else:
+        output_result = 'cylinder'
     
-    return labels[random_integer]
\ No newline at end of file
+    return output_result