app1.py

from fastai.vision.all import *
learn=load_learner('model.pkl')
def input_img(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}")
    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)))
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)