Create app.py

app.py

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+# This Python 3 environment comes with many helpful analytics libraries installed
+# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
+# For example, here's several helpful packages to load
+
+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+
+# Input data files are available in the read-only "../input/" directory
+# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
+
+import os
+for dirname, _, filenames in os.walk('/kaggle/input'):
+    for filename in filenames:
+        print(os.path.join(dirname, filename))
+
+# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
+# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
+
+#|default_exp app
+
+#|export
+#!pip install fastbook
+import fastbook
+from fastbook import *
+#!pip install fastai
+from fastai.vision.widgets import *
+#!pip install gradio
+import gradio as gr
+
+import IPython
+from IPython.display import display
+from PIL import Image
+
+import pathlib
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
+
+def search_images(term, max_images=50):
+    print(f"Searching for '{term}'")
+    return search_images_ddg(term, max_images)
+
+learn = load_learner('model.pkl')
+
+breeds = ('Labrador Retrievers','German Shepherds','Golden Retrievers','French Bulldogs','Bulldogs','Beagles','Poodles','Rottweilers','Chihuahua')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    #return dict(zip(breeds, map(float,probs)))
+    return "This is " + pred
+
+image = gr.components.Image()
+label = gr.components.Label()
+
+examples = ['dog.jpg','labrador.jpeg','dunno.jpg']
+
+for x in examples:
+    Image.open(x)
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False,share = True)