Update app.py

app.py

@@ -2,14 +2,18 @@ from fastai.vision.core import PILImageBW, TensorImageBW
 from datasets import ClassLabel
 import gradio as gr
 from fastai.learner import load_learner
+from PIL import Image
+from numpy import array
 
 def get_image_attr(x): return x['image']
 def get_target_attr(x): return x['target']
+def get_label_attr(x): return x['label']
 
 def img2tensor(im: Image.Image):
   return TensorImageBW(array(im)).unsqueeze(0)
 
 classLabel = ClassLabel(names=['T - shirt / top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'], id=None)
+labels = classLabel.names
 
 def add_target(x:dict):
   x['target'] = classLabel.int2str(x['label'])
@@ -20,13 +24,19 @@ learn = load_learner('export.pkl', cpu=True)
 def classify(inp):
   img = PILImageBW.create(inp)
   item = dict(image=img)
-  pred, _, _ = learn.predict(item)
-  return classLabel.int2str(int(pred))
+  pred, _, prob = learn.predict(item)
+  return {label: float(prob[i]) for i, label in enumerate(labels)}
+  # return classLabel.int2str(int(pred))
+
+examples = ['shoes.jpg', 't-shirt.jpg']
+interpretation='default'
 
 iface = gr.Interface(
     fn=classify,
-    inputs=gr.inputs.Image(),
-    outputs="text",
+    inputs=gr.inputs.Image(image_mode='L'),
+    outputs=gr.outputs.Label(num_top_classes=3),
     title="Fashion Mnist Classifier",
     description="fastai deployment in Gradio.",
+    examples=examples,
+    interpretation=interpretation,
 ).launch()