cat classifier

app.py

@@ -1,7 +1,46 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: cat_classifier.ipynb.
+
+# %% auto 0
+__all__ = ['path', 'files', 'dls', 'learn', 'categories', 'label_func', 'image_classifier']
+
+# %% cat_classifier.ipynb 1
+from fastai.vision.all import ImageDataLoaders, URLs, untar_data, get_image_files, Resize, \
+    vision_learner, resnet34, error_rate, default_device, load_learner, show_image, PILImage
+import torch
+
+# %% cat_classifier.ipynb 3
+path = untar_data(URLs.PETS)
+path.ls()
+
+# %% cat_classifier.ipynb 5
+files = get_image_files(path/'images')
+
+files[0]
+
+# %% cat_classifier.ipynb 6
+def label_func(fn:str): 
+    return fn[0].isupper()
+
+# %% cat_classifier.ipynb 7
+dls = ImageDataLoaders.from_name_func(path/'images', fnames=files, label_func=label_func,
+                                      item_tfms=Resize(224), device=default_device(1)) 
+
+# %% cat_classifier.ipynb 9
+learn = vision_learner(dls, resnet34, metrics=error_rate)
+learn.fine_tune(1)
+
+# %% cat_classifier.ipynb 12
 import gradio as gr
+categories = ('dog', 'cat')
+def image_classifier(input): 
+    pred, idx, probs = learn.predict(input) 
+    result = dict(zip(categories, map(float, probs)))
+    print('result', result)
+    return result
+
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+gr.Interface(fn=image_classifier,
+             inputs=gr.Image(type="pil"),
+             outputs=gr.Label(),
+             examples=['./cat.jpeg', './dog.jpg']).launch(share=True)