add image file

.gitattributes

@@ -32,3 +32,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,3 +1,28 @@
+# import gradio as gr
+# def greet (name): return f"Hello {name}!" 
+# gr.Interface(fn=greet, inputs="text", outputs="text"). launch()
+
+_all_ = ['is_cat', 'learn', 'classify_image'
+, 'categories',
+'image',
+'label',  'examples', 'intf']
+# Cell
+from fastai.vision.al1 import * 
 import gradio as gr
-def greet (name): return f"Hello {name}!" 
-gr.Interface(fn=greet, inputs="text", outputs="text"). launch()
+def is_cat(x): return x[0].isupper()
+# Cell
+learn = load_learner('model.pkl')
+# Cell
+categories = ('Dog', 'Cat')
+format_float = lambda x: "{:.10f}".format(float(x))
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict (zip(categories, map (format_float,probs)))
+
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+examples = ['dog.jpg'
+, 'cat.jpg', 'cos.png']
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch (inline=False)