#import gradio as gr



#def greet(name):
#    return "Hello " + name + "!!"
#
#demo = gr.Interface(fn=greet, inputs="text", outputs="text")
#demo.launch()

# second time used this only but huggingface doesn't like it so moving to a different method
#learner=load_learner('model.pkl')

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

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

dls = ImageDataLoaders.from_lists('.', fnames=['cat.jpeg','dog.jpeg'], labels=['cat','dog'], vocab=['cat', 'dog'])

learner = vision_learner(dls, resnet18, metrics=error_rate)

learner.load('model')


categories = ['Dog','Cat']
def classify_image(img):
    pred,pred_idx,probs = learner.predict(img)
    return dict(zip(categories, map(float,probs)))

image=gr.Image()

#image=gr.Image(shape=(192,192))
label=gr.Label()
examples=['dog.jpeg', 'cat.jpeg','dogcat.jpeg']
titleText="Dog vs Cat Classifier"
descriptionText="Upload an image of a dog or a cat to  predict the probabilities of each class."

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