create jupyter notebook for app, add examples and requirements.txt

.gitignore

@@ -0,0 +1 @@
+.gradio/

app.py

@@ -1,7 +1,33 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'categories', 'title', 'description', 'article', 'inputs', 'outputs', 'examples', 'intf', 'is_cat',
+           'classify_image']
+
+# %% app.ipynb 1
+from fastai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+def is_cat(x): return x[0].isupper()
+
+# %% app.ipynb 8
+learn = load_learner('model.pkl')
+
+# %% app.ipynb 10
+categories = ('Dog', 'Cat')
+
+def classify_image(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {categories[i]: float(probs[i]) for i in range(len(categories))} # convert probs (pytorch tensor) into floats
+
+# %% app.ipynb 12
+title = "Cat and Dog Classifier"
+description = "A classifier built with a fine-tuned Resnet 18 model."
+article="<p style='text-align: center'><a href='https://github.com/deenasun' target='_blank'>Deena Sun on Github</a></p>"
+inputs= gr.Image()
+outputs= gr.Label()
+examples = ['dog.jpg', 'cat.jpg', 'dino.jpg']
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+intf = gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs, examples=examples, title=title, description=description, article=article)
+intf.launch(share=True)

requirements.txt

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+fastai