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

learn = load_learner('nyc_iconic_buildings.pkl')

labels = learn.dls.vocab
def predict(img):
    img = PILImage.create(img)
    pred, pred_idx, probs = learn.predict(img)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}

title = 'NYC Iconic Buildings Classifier'
description = 'This is an image classifier based on fastai course, lesson 2. Image references: Wikipedia'
interpretation = 'default'
examples = ['empire_state_building.jpg', 'flatiron_building.jpg', 'chrysler_building.jpg']
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, 
             examples=examples,
             interpretation=interpretation, enable_queue=enable_queue).launch()