Update app.py

app.py

@@ -1,57 +1,14 @@
-# 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()