updated the app.py file

app.py

@@ -1,7 +1,88 @@
+# -*- coding: utf-8 -*-
+"""app.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1F6tnDcfVlChPXwu4UwSHq-bggJlcmyj-
+
+# This notebook makes predictions based on saved model.
+
+#Import Libraries
+"""
+#|default_exp app
+
+
+#|export
+!pip install -Uqq fastai
+!pip install -Uqq fastbook
+!pip install -Uqq gradio
+!pip install -Uqq nbdev
+
+"""#Dogs Vs Cats"""
+
+#|export
+from fastai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!" 
+#|export
+def is_cat(x): return x[0].isupper()
+
+'''
+im = PILImage.create('/content/dog.JPG')
+im.thumbnail((192,192))
+im
+'''
+#|export
+#Use the model.pkl file that is the classifier model
+learn = load_learner('/content/model.pkl')
+
+# Commented out IPython magic to ensure Python compatibility.
+#Now predicted the image to determine if it is a Cat ? It correctly predicts that it is NOT a Cat.
+#learn.predict(im)
+
+# %time learn.predict(im)
+
+"""#Now create a GRADIO Interface that has this information
+
+Gradio requires us to give it a function that it is going to call. Our's function is `classify_image(img)`
+
+learn.predict(img) returns 3 things :
+pred --> Prediction as a String
+idx --> Its index
+probs --> Probability that the image is a Cat ?
+
+Gradio wants to get back a dictionary containing each of the posible categories -- which is this case is a DOg or a Cat -- and the probability of each one..
+
+"""
+
+#|export
+categories = ('Dog','Cat')
+
+def classify_image(img):
+  pred,idx,probs = learn.predict(img)
+  return dict(zip(categories,map(float,probs)))
+
+#classify_image(im)
+
+"""#Now creating our GRADIO Interface"""
+
+#|export
+image = gr.Image(height=192,width=192)
+label = "label"
+examples = ['/content/dog.JPG','/content/cat.JPG','/content/dunno.JPG']
+intf = gr.Interface(fn = classify_image,inputs=image,outputs=label,examples=examples)
+intf.launch(inline=False)
+
+"""#Export the notebook as Python script"""
+'''
+!pip install -Uqq nbdev
+
+#from nbdev.export import notebook2script
+
+from nbdev import nbdev_export
+
+!pip install -Uqq nbconvert
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+!jupyter nbconvert app.ipynb --to python
+'''

app1.py

@@ -0,0 +1,7 @@
+import gradio as gr
+
+def greet(name):
+    return "Hello " + name + "!!" 
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()