app.py

# import gradio as gr

# def greet(name):
#     return "Hello " + name + "!!"

# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()



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

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

def main():
    learn = load_learner('02_trained_model_cats_vs_dogs.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))}

    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>"
    interpretation='default'
    enable_queue=True
    # gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=2)).launch(share=False)
    gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=2),title=title,description=description,article=article,examples=None,interpretation=interpretation,enable_queue=enable_queue).launch(share=True)


if __name__ == '__main__':
    main()