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

import gradio as gr
from fastai.vision.all import *
import skimage
import pathlib
temp = pathlib.PosixPath
pathlib.PosixPath = pathlib.WindowsPath
pathlib.PosixPath = temp

learn = load_learner('model.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 = "Female/Male Classifier"
description = "A Female/Male classifier trained on the duckduckgo search result with fastai. Created as a demo for Gradio and HuggingFace Spaces."
## article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
examples = ['femaleDefault.jpg', 'maleDefault.jpg',
       'dragQueen1.jpg', 'dragQueen2.jpg',
       'femaleAngry1.jpg', 'femaleAngry2.jpg',
       'femaleMuscle1.jpg', 'femaleMuscle2.jpg',
       'maleAsian.jpg', 'maleEurope.jpg',
       'femaleAsian.jpg', 'femaleDefault.jpg',
       'maleCrying2.jpg', 'maleCrying2No.jpg']
#interpretation='default'
enable_queue=True
#
## gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()
#
inter = gr.Interface(
   fn=predict,
   inputs=gr.inputs.Image(shape=(512, 512)),
   outputs=gr.outputs.Label(),
   title=title,
   description=description,
   examples=examples,
   cache_examples=True,
   examples_per_page=2)

inter.queue()
inter.launch()