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

# dependent function
# labeling function
def is_cat(x):
    return x[0].isupper() 


learn = load_learner('model.pkl')



categories = ('Dog', 'Cat')

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

image = gr.inputs.Image(shape=(192,192))
label = gr.outputs.Label()
# examples =  ['dog.jpg', 'cat.jpg', 'dunno.jpg']

# gradio interface what kind of ouput 
# input and output 
# can provide some examples 
# the gradio interface can take different kinds of inputs 
intf = gr.Interface(fn=classify_image, inputs = image, outputs=label)#, examples = examples)
intf.launch(inline = False)