import gradio as gr
# from fastai.vision.widgets import *
from fastai.vision.all import *
# import skimage

learn = load_learner('3label.pkl')

# Cell
# categories = ('Tank', 'No-Tank')
labels = learn.dls.vocab

def classify_image(img):
    pred,idx,probs = learn.predict(img)
    # return dict(zip(categories, map(float,probs)))
    return {labels[i]: float(probs[i]) for i in range(len(labels))}

# Cell # 
image = gr.inputs.Image(shape=(224, 224))
label = gr.outputs.Label()
examples = ['fake_1.jpg','fake_2.jpg','tank_6.jfif','tank_7.jfif','tank_0.jfif', 'tank_1.jpg', 'tank_2.jfif', 'tank_3.jfif', 'tank_4.jfif', 'photo_1.jfif', 'photo_2.jfif', 'photo_3.jfif', 'photo_4.jfif', 'photo_5.jfif', 'photo_6.jfif']

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