modified: app.ipynb

app.py

@@ -17,23 +17,27 @@ import gradio as gr
 # %%
 #im = PILImage.create('teddybear.jpg')
 #im = PILImage.create('grizzly.jpg')
-# im = PILImage.create('blackbear.jpg')
+im = PILImage.create('blackbear.jpg')
+
+im.thumbnail((192, 192))
+im
 
-# im.thumbnail((192, 192))
-# im
 
 # %% [markdown]
 # Let's import the model and create the learner:
 
 # %%
 #|export
+import pathlib
+plt = platform.system()
+if plt == 'Windows': pathlib.PosixPath = pathlib.WindowsPath
 learn = load_learner('export.pkl')
 
 # %% [markdown]
 # With the learner we can to the predictions (inference):
 
 # %%
-# learn.predict(im)
+learn.predict(im)
 
 # %% [markdown]
 # The available categories are contained in the vocab:
@@ -54,7 +58,7 @@ def classify_image(img):
 # Testing the function:
 
 # %%
-# classify_image(im)
+classify_image(im)
 
 # %% [markdown]
 # ## Gradio App
@@ -77,7 +81,7 @@ intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=exa
 intf.launch(inline=False)
 
 # %%
-# intf.close()
+intf.close()
 
 # %% [markdown]
 # ## Export