feat: Update app.py and add model with LFS

.gitignore

@@ -0,0 +1 @@
+.DS_Store

app.py

@@ -1,7 +1,48 @@
+# -*- coding: utf-8 -*-
+"""app
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1SudNQMVhTicfHu2_eWce7kTx7dNNFgIE
+"""
+
+from fastai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+def is_cat(x):
+  return x[0].isupper()
+
+im = PILImage.create('dog.webp')
+im.thumbnail((192,192))
+im
+
+#|export
+learn = load_learner('model (1).pkl')
+
+learn.predict(im)
+
+#|export
+categories = ('Dog','Cat')
+
+def classify_image(img):
+  pred,idx,probs = learn.predict(img)
+  return dict(zip(categories, map(float,probs)))
+
+classify_image(im)
+
+#|export
+image = gr.Image()
+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)
+
+"""*** we will export some essential selected cells as the prediction script as a python file using the below commands"""
+
+!pip install nbdev
+
+!pip install --upgrade nbdev
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()

model (1).pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dee7b4ff81acaef681bc94e16ce2b4e5bd0bb206341d7c8089354109b4aa37df
+size 47069063