Update app.py

app.py

@@ -1,30 +1,60 @@
-from fastai.vision.all import *
-import gradio as gr
+# 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)
 
-#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}.")
+    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
-    
-# categories=('Black people','White people')
-# def func_classi(img):
-#     pred,idx,probs=learn.predict(img)
-#     return dict(zip(categories,map(float,probs)))
+learn.export('model.pkl')
+
+im=PILImage.create(img)
+im=PILImage.create('img')
+im.thumbnail((192,192))
+
+learn=load_learner('model.pkl')
 
+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 = 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)
+demo = gr.Interface(fn=func_classi, inputs="image", outputs="label")
+demo.launch(inline=False)