Update app.py

app.py

@@ -1,36 +1,33 @@
 from fastai.vision.all import *
 import gradio as gr
-
+from PIL import Image as PILImage
 
 learn = load_learner('model.pkl')
 
-
-
-categories = (['abraham_grampa_simpson', 'agnes_skinner', 'apu_nahasapeemapetilon', 'barney_gumble', 'bart_simpson',
- 'carl_carlson', 'charles_montgomery_burns', 'chief_wiggum', 'cletus_spuckler', 'comic_book_guy', 'disco_stu',
-  'edna_krabappel', 'fat_tony', 'gil', 'groundskeeper_willie', 'homer_simpson', 'kent_brockman', 'krusty_the_clown',
-   'lenny_leonard', 'lionel_hutz', 'lisa_simpson', 'maggie_simpson', 'marge_simpson', 'martin_prince', 'mayor_quimby',
-    'milhouse_van_houten', 'miss_hoover', 'moe_szyslak', 'ned_flanders', 'nelson_muntz', 'otto_mann', 'patty_bouvier',
-     'principal_skinner', 'professor_john_frink', 'rainier_wolfcastle', 'ralph_wiggum', 'selma_bouvier', 'sideshow_bob',
-      'sideshow_mel', 'snake_jailbird', 'troy_mcclure', 'waylon_smithers'])
-
-
+categories = ['abraham_grampa_simpson', 'agnes_skinner', 'apu_nahasapeemapetilon', 'barney_gumble', 
+              'bart_simpson', 'carl_carlson', 'charles_montgomery_burns', 'chief_wiggum', 
+              'cletus_spuckler', 'comic_book_guy', 'disco_stu', 'edna_krabappel', 'fat_tony', 
+              'gil', 'groundskeeper_willie', 'homer_simpson', 'kent_brockman', 'krusty_the_clown', 
+              'lenny_leonard', 'lionel_hutz', 'lisa_simpson', 'maggie_simpson', 'marge_simpson', 
+              'martin_prince', 'mayor_quimby', 'milhouse_van_houten', 'miss_hoover', 'moe_szyslak', 
+              'ned_flanders', 'nelson_muntz', 'otto_mann', 'patty_bouvier', 'principal_skinner', 
+              'professor_john_frink', 'rainier_wolfcastle', 'ralph_wiggum', 'selma_bouvier', 
+              'sideshow_bob', 'sideshow_mel', 'snake_jailbird', 'troy_mcclure', 'waylon_smithers']
 
 def classify_image(img):
-    img = PILImage.create(img).resize((192, 192))
-    pred,idx,probs = learn.predict(img)
-    return dict(zip(categories, map(float,probs)))
-
-
-
-image = gr.Image(type='pil')
-label = gr.Label()
-examples = ['ednar.jpg', 'maggie.jpg', 'bart.jpg']
-
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-intf.launch(inline=False)
-
-
-
-
-
+    img = PILImage.fromarray(img).resize((192, 192))
+    pred, idx, probs = learn.predict(img)
+    return {cat: float(prob) for cat, prob in zip(categories, probs)}
+
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type='numpy'),
+    outputs=gr.Label(),
+    examples=[
+        'ednar.jpg',
+        'maggie.jpg',
+        'bart.jpg'
+    ]
+)
+
+demo.launch()