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

# AUTOGENERATED! DO NOT EDIT! File to edit: inference-face-crop-model.ipynb.

# %% auto 0
__all__ = ['learner', 'categories', 'classify_image']

# %% inference-face-crop-model.ipynb 10
from fastai.vision.all import *
import gradio as gr

learner = load_learner('./face-crop-model.pkl')

categories = ('happy', 'other')

def classify_image(img):
    img = img["composite"]
    # print(img.shape)
    img = img[:,:, :3]
    pred, idx, probs = learner.predict(img)
    return dict(zip(categories, map(float, probs)))

with gr.Blocks() as demo:

    image_edit = gr.ImageEditor(type="numpy", crop_size="1:1.2", canvas_size=(300, 300))
        # image = gr.Image()
    label = gr.Label()
    examples = ['happy.png', "happy2.png", "happy3.png", "happy4.png", 'other.png', 'other2.png', 'other3.png', 'other4.png', 'other5.png',]

    intf = gr.Interface(fn=classify_image, inputs=image_edit, outputs=label, examples=examples,
                       description="Use crop button to resize around the face for best results")
# intf.launch(inline=False)
demo.launch(share=True)