added model

app-0.py

@@ -0,0 +1,7 @@
+import gradio as gr
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()

app.py

@@ -1,7 +1,33 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ..\lesson2-app.ipynb.
+
+# %% auto 0
+__all__ = ['posix_backup', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_image']
+
+# %% ..\lesson2-app.ipynb 2
+from fastai.vision.all import * 
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+def is_cat(x): return x[0].isupper()
+
+# %% ..\lesson2-app.ipynb 4
+#learn = load_learner('model.pkl')
+import pathlib
+posix_backup = pathlib.PosixPath
+try:
+    pathlib.PosixPath = pathlib.WindowsPath
+    learn = load_learner('model.pkl')
+finally:
+    pathlib.PosixPath = posix_backup
+
+# %% ..\lesson2-app.ipynb 6
+categories = ('Dog','Cat')
+def classify_image(img):
+    pred, idx, probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# %% ..\lesson2-app.ipynb 8
+image = gr.inputs.Image(shape=(192,192))
+label = gr.outputs.Label()
+examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b440be75447ed6f29821e760162328fe1eb743a7faf7c88d5012e010d7f86fa8
+size 47064702