Update app.py

app.py

@@ -3,19 +3,25 @@ import gradio as gr
 
 
 #is_black(x) : return x[0].isupper()
-categories=('Black people','White people')
-def func_classi(image):
+
+def input_img(img)
     learn=load_learner('model.pkl')
-    race,_,probs = learn.predict(PILImage.create(image))
+    race,_,probs = learn.predict(PILImage.create(img))
     print(f"This is a: {race}.")
     print(f"Probability it's a black person: {probs[0]:.4f}.\nProbability it's a white person: {probs[1]:.4f}")
-
+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.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)
+#examples=('Black people','White people')
+#demo = gr.Interface(fn=func_classi, inputs=[gr.func_classi()], outputs=[gr.Textbook(label="Results")])
+#demo.launch(inline=False)