Create app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Copy of dogs_cats.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1pu75-TcRCtcDPFHn3zA6IqSsH8BM5yka
+
+## Gradio Pets
+
+# New Section
+"""
+
+# !pip install -Uqq fastai
+
+
+#/ default_exp app
+
+from fastai.vision.all import *
+import gradio as gr
+
+def is_cat(x): return x[0].isupper()
+
+# path = untar_data(URLs.PETS)/'images'
+
+# dls = ImageDataLoaders.from_name_func('.',
+#     get_image_files(path), valid_pct=0.2, seed=42, #giving
+#     label_func=is_cat,
+#     item_tfms=Resize(192))
+
+# dls.show_batch()
+
+# learn = vision_learner(dls, resnet18, metrics=error_rate)
+# learn.fine_tune(3)
+
+# learn.export('model.pkl')
+
+# cell 
+learn= load_learner('model.pk1')
+
+categories= ('Dog', 'Cat')
+
+# # function we need to define for gradio
+
+# # predcition is a string and prob ius a
+
+# # grradio wants dict with each category and prob of each
+
+# # zip -- putting together .... dict-- putting into correct format
+
+# # gradio doesnt work with tensors thus need to convert to float
+
+
+def classify_image(img):
+  pred,idx,probs= learn.predict(img)
+  return dict(zip(categories, map(float, probs)))
+
+im=PILImage.create('dog.jpg')
+im.thumbnail((192,192))
+im
+
+learn.predict(im)
+
+classify_image(im)
+
+examples= ['dog.jpg', 'cat.jpg']
+
+gr.Interface(fn=classify_image, inputs=[gr.Image(type="pil")], outputs=[gr.Label(num_top_classes=2)], examples=examples).launch()