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


#is_black(x) : return x[0].isupper()

def input_img(img):
    learn=load_learner('model.pkl')
    race,_,probs = learn.predict(img)
    #print(f"This is a: {race}.")
    processed_output=(f"This is a: {race}./nProbability it's a black person: {probs[0]:.4f}.\nProbability it's a white person: {probs[1]:.4f}")
    return processed_output
    
# categories=('Black people','White people')
# def func_classi(img):
#     pred,idx,probs=learn.predict(img)
#     return dict(zip(categories,map(float,probs)))

image=gr.inputs.Image(shape=(192,192))
label=gr.outputs.Label()
examples=('Black people','White people')
#demo = gr.Interface(fn=func_classi, inputs="image", outputs="label")
demo = gr.Interface(fn=input_img, inputs="image", outputs="label")
demo.launch(inline=False)

#image=gr.inputs.Image(shape=(192,192))
#label=gr.outputs.Label()
#examples=('Black people','White people')
#demo = gr.Interface(fn=func_classi, inputs=[gr.func_classi()], outputs=[gr.Textbook(label="Results")])
#demo.launch(inline=False)