import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

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

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

learn = load_learner('model.pkl')

categories = ('Dog', 'Cat')

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

image = "image"
label = "label"

intf = gr.Interface(fn=classify_image, inputs=image, outputs=label)
intf.launch(inline=False, share = True)