Update app.py

app.py

@@ -1,4 +1,4 @@
-import gradio
+import gradio as gr
 from fastai.vision.all import *
 
 # MODELS_PATH = Path('./models')
@@ -15,9 +15,10 @@ LEARN = load_learner('Garbage_Classification.pkl')
 LABELS = LEARN.dls.vocab
 
 def gradio_predict(img):
+    img = img.resize((512, 512))
     img = PILImage.create(img)
     _pred, _pred_idx, probs = LEARN.predict(img)
-    labels_probs = {LABELS[i]: float(probs[i]) for i, _ in enumerate(LABELS)}
+    labels_probs = dict(zip(categories, map(float,probs)))
     return labels_probs
 
 # with open('gradio_article.md') as f:
@@ -32,14 +33,21 @@ def gradio_predict(img):
 #     "theme": "default",
 # }
 
-demo = gradio.Interface(fn=gradio_predict,
-                      inputs=gradio.inputs.Image(shape=(512, 512)),
-                      outputs=gradio.outputs.Label(num_top_classes=5),
-                      **interface_options)
+image = gr.Image(type="pil", label="Upload an image")
+label = gr.Label()
+examples = ['battery.jpg', 'biological.jpg', 'cardboard.jpg']
 
-launch_options = {
-    "enable_queue": True,
-    "share": False, 
-}
+intf = gr.Interface(fn=gradio_predict, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)
 
-demo.launch(**launch_options)
+# demo = gr.Interface(fn=gradio_predict,
+#                       inputs=gradio.Image(type="pil", label="Upload an image"),
+#                       outputs=gradio.outputs.Label(num_top_classes=5),
+#                       **interface_options)
+
+# launch_options = {
+#     "enable_queue": True,
+#     "share": False, 
+# }
+
+# demo.launch(**launch_options)