add auto notebook parser

.ipynb_checkpoints/app-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app copy.py

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+from fastai.vision.all import *
+import gradio as gr
+
+learn = load_learner('catornot_model.pkl')
+
+categories = ('Dog', 'Cat')
+
+def classify_image(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 = ['dog.jpg', 'cat.jpg', 'dunno.jpg']
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)

app.py

@@ -1,17 +1,22 @@
-from fastai.vision.all import *
-import gradio as gr
+# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
 
+# %% auto 0
+__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
+
+# %% app.ipynb 2
 learn = load_learner('catornot_model.pkl')
 
+# %% app.ipynb 5
 categories = ('Dog', 'Cat')
 
 def classify_image(img):
-    pred, idx, probs = learn.predict(img)
-    return dict(zip(categories, map(float, probs)))
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
 
-image = gr.inputs.Image(shape=(192, 192))
-label = gr.outputs.Label()
+# %% app.ipynb 7
+image = gr.Image(shape=(192,192))
+label = gr.Label()
 examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']
 
 intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-intf.launch(inline=False)
+intf.launch()

saving-a-basic-fastai-model.ipynb:Zone.Identifier

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+[ZoneTransfer]
+ZoneId=3
+HostUrl=about:internet