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# 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)
from fastai.vision.all import *
learn=load_learner('model.pkl')
def input_img(img):
race,_,probs = learn.predict(PILImage.create('img'))
#race,_,probs = learn.predict(PILImage.create(img))
#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}")
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
im=PILImage.create(img)
im=PILImage.create('img')
im.thumbnail((192,192))
learn.predict(im)
categories=('Black people','White people')
def func_classi(img):
pred,idx,probs=learn.predict(img)
return dict(zip(categories,map(float,probs)))
func_classi(im)
import gradio as gr
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.launch(inline=False) |