Update app.py

app.py

@@ -4,12 +4,33 @@ from PIL import Image
 #
 #learn = load_learner('export.pkl')
 learn = torch.load('digit_classifier.pth')
+learn.eval() #switch to eval mode
 labels = [str(x) for x in range(10)]
+
+#Define function to reduce image of arbitrary size to 8x8 per model requirements.
+def reduce_image_count(image):
+    output_size = (8, 8)
+    block_size = (image.shape[0] // output_size[0], image.shape[1] // output_size[1])
+    output = np.zeros(output_size)
+    
+    for i in range(output_size[0]):
+        for j in range(output_size[1]):
+            block = image[i*block_size[0]:(i+1)*block_size[0], j*block_size[1]:(j+1)*block_size[1]]
+            count = np.count_nonzero(block)
+            output[i, j] = 16 - ((count / (block_size[0] * block_size[1])) * 16)
+            
+    return output
+
 def predict(img):
     #First take input and reduce it to 8x8 px as the dataset was
+    pil_image = Image.open(img) #get image
+    gray_img = pil_image.convert('L')#grayscale
+    pic = np.array(gray_img) #convert to array
+    inp_img=reduce_image_count(pic)#Reduce image to required input size
+    
+    otpt=F.softmax(learn.forward(inp_img.view(-1,64)))
+    #pred,pred_idx,probs = learn.predict(img)
     
-    img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    return {labels[i]: float(otpt[0].data[i]) for i in range(len(labels)),'image': inp_img}
 
-gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)),   outputs=[gr.outputs.Label(num_top_classes=3), gr.outputs.Image()]).launch(share=True)