from fastai.vision.all import *
import gradio as gr
def is_cat(x): return x[0].isupper() 


learn = load_learner('model.pkl')

categories = ('Dog', 'Cat')
def predict(img):
    img = PILImage.create(img)
    pred,pred_idx,probs = learn.predict(img)
    return {categories[i]: float(probs[i]) for i in range(len(categories))}

title = "Dog Cat Classifier"
description = "A pet classifier trained a Pets dataset. Created as a demo using Gradio and HuggingFace Spaces."

article="<p style='text-align: center'><a href='https://Solab5.github.io' target='_blank'>Morris Twinomugisha</a></p>"
examples = ['dog.jpg', 'cat.jpg']
interpretation='default'
enable_queue=True

gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch(share='')