Add application file

app.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: chickli.ipynb.
+
+# %% auto 0
+__all__ = ['temp', 'learn', 'breeds', 'image', 'label', 'examples', 'intf', 'classify_image']
+
+# %% chickli.ipynb 2
+#!pip install fastbook
+import fastbook
+from fastbook import *
+#!pip install fastai
+from fastai.vision.widgets import *
+#!pip install gradio
+import gradio as gr
+
+import IPython
+from IPython.display import display
+from PIL import Image
+
+def search_images(term, max_images=50):
+    print(f"Searching for '{term}'")
+    return search_images_ddg(term, max_images)
+
+# %% chickli.ipynb 3
+import pathlib
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
+#plt = platform.system()
+#if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
+
+learn = load_learner('model.pkl')
+
+breeds = ('Labrador Retrievers','German Shepherds','Golden Retrievers','French Bulldogs','Bulldogs','Beagles','Poodles','Rottweilers','Chihuahua')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    #return dict(zip(breeds, map(float,probs)))
+    return "This is " + pred
+
+image = gr.components.Image()
+label = gr.components.Label()
+
+examples = ['dog.jpg','labrador.jpeg','dunno.jpg']
+
+for x in examples:
+    Image.open(x)
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False,share=True)