updating img files

app.py

@@ -7,31 +7,30 @@ title = "Open/Closed Door Classifier"
 description = "A classifier trained using fastai on search images of open and closed doors." \
     "Created for Lesson 2 in the fastai course."
 
-examples = ['./open-door.jpeg', 
-    './crack_2.jpg', './red_arch.jpg', 
-    './green.jpg', './red.jpg', './opening_door.jpg', 
-    './inside.jpg', './cracked_3.jpg', './old.jpg', 
-    './blue.jpg', './closed-door.jpeg', './crack_1.jpg']
-
+examples = ['open-door.jpg', 
+    'crack_2.jpg', 'red_arch.jpg', 
+    'green.jpg', 'red.jpg', 'opening_door.jpg', 
+    'inside.jpg', './cracked_3.jpg', './old.jpg', 
+    'blue.jpg']
 
+examples = list(map(lambda x: "examples/" + x, examples))
+#print(examples)
 
 
 learn = load_learner('door_model.pkl')
 labels = learn.dls.vocab
 
-def predict(img):
-    img = PILImage.create(img)
+def predict(img_input):
+    img = PILImage.create(img_input)
     pred,pred_idx,probs = learn.predict(img)
     return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
 
-
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(shape=(512, 512)),
     outputs=gr.Label(num_top_classes=3),
     title=title,
     description=description,
-    examples=examples,
-    interpretation="default").queue().launch()
+    examples=examples).queue().launch()