Cat-dog model

.gitattributes

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 *.gz filter=lfs diff=lfs merge=lfs -text
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 *.model filter=lfs diff=lfs merge=lfs -text
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 *.pb filter=lfs diff=lfs merge=lfs -text
 *.pickle filter=lfs diff=lfs merge=lfs -text
 *.pkl filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
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app.py

@@ -1,9 +1,22 @@
+from fastai.vision.all import *
 import gradio as gr
 
+def is_cat(x): return x[0].isupper()
 
-def greet(name):
-    return "Hello " + name + "!!"
+learner = load_learner("cat-dog-model.pkl")
 
+categories = ["Dog", "Cat"]
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+def classify_image(im):
+    _pred, _idx, probs = learner.predict(im)
+    return dict(zip(categories, map(float, probs)))
+
+# create interface
+
+im = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+examples = ["dog.jpg", "cat.jpg", "cog.jpg", "cog2.png", "cog3.png"]
+examples = [f"imgs/{im}" for im in examples]
+
+iface = gr.Interface(fn=classify_image, inputs=im, outputs=label, examples=examples)
+iface.launch(inline=False)

app_exp.py

@@ -0,0 +1,29 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../lesson2.ipynb.
+
+# %% auto 0
+__all__ = ['learner', 'categories', 'im', 'label', 'examples', 'intf', 'is_cat', 'classify_image']
+
+# %% ../lesson2.ipynb 11
+from fastai.vision.all import *
+import gradio as gr
+
+def is_cat(x): return x[0].isupper()
+
+# %% ../lesson2.ipynb 18
+learner = load_learner("cat-dog-model.pkl")
+
+# %% ../lesson2.ipynb 22
+categories = ["Dog", "Cat"]
+
+def classify_image(im):
+    _pred, _idx, probs = learner.predict(im)
+    return dict(zip(categories, map(float, probs)))
+
+# %% ../lesson2.ipynb 24
+im = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+examples = ["dog.jpg", "cat.jpg", "cog.jpg"]
+examples = [f"imgs/{im}" for im in examples]
+
+intf = gr.Interface(fn=classify_image, inputs=im, outputs=label, examples=examples)
+intf.launch(inline=False) #, share=True)