app.py

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

def is_cat(x): return x[0].isupper()

learn = load_learner('model.pkl', pickle_module=dill)


categories = ('Dog', 'Cat')

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

image = gr.Image()
label = gr.Label()

examples = ['siamese.png']

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


# labels = learn.dls.vocab



# title = "Pet Breed Classifier"
# description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
# article = "<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"


# gr.Interface(
#     fn=predict,
#     inputs=gr.Image(shape=(512, 512)),  # Updated to newer Gradio syntax
#     outputs=gr.Label(num_top_classes=3),  # Updated to newer Gradio syntax
#     title=title,
#     description=description,
#     article=article,
#     examples=examples
# ).launch()