pet_classifier / app.py
mpiendl's picture
Create app.py
52bed5a
raw
history blame
439 Bytes
import gradio as gr
from fastai.vision.all import *
import skimage
learn = load_learner('export.pkl')
labels = learn.dls.vocab
def predict(img):
img = PILImage.create(img)
pred,pred_idx,probs = learn.predict(img)
return {labels[i]: float(probs[i]) for i in range(len(labels))}
gr.Interface(
fn=predict,
inputs=gr.inputs.Image(share=(512,512)),
outputs=gr.outputs.Label(num_top_classes=3)
).launch(share=True)