from fastai.vision.all import *
import gradio as gr


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'])



def classify_image(img):
    pred,idx,probs = learn.predict(img)
    return dict(zip(categories, map(float,probs)))



image = gr.inputs.Image(shape=(192,192))
label = gr.outputs.Label()
examples = ['ednar.jpg', 'maggie.jpg', 'bart.jpg']

intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
intf.launch(inline=False)