| 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) | |