Create dog_vs_cat.py

dog_vs_cat.py

@@ -1,58 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Dog Vs Cat.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1_3DbuTTvb83jCX-oa6nmg4bMAAyuPNdo
-"""
-
-!pip install gradio
-!pip install fastbook
-!pip install -U duckduckgo_search
-!pip install --upgrade duckduckgo-search
-
-"""# Dogs vs Cats"""
-
-from fastbook import *
-from fastai.vision.all import *
-import gradio as gr
-
-def is_cat(x) : return x[0].isupper
-
-urls = search_images_ddg('dog', max_images=1)
-dest = 'images/dogs.jpg'
-download_url(dest = dest, url=urls[0], show_progress=False)
-
-urls = search_images_ddg('cat', max_images=1)
-dest = 'images/cats.jpg'
-download_url(dest = dest, url=urls[0], show_progress=False)
-
-urls = search_images_ddg('animal', max_images=1)
-dest = 'images/dunno.jpg'
-download_url(dest = dest, url=urls[0], show_progress=False)
-
-im = PILImage.create('images/dogs.jpg')
-im.thumbnail((192,192))
-im
-
-learn = load_learner('model.pkl')
-learn.predict(im)
-
-categories = ('Dog','Cat')
-
-def classify_images(img):
-  pred, idx, probs = learn.predict(img)
-  return dict(zip(categories, map(float,probs)))
-
-classify_images(im)
-
-intf = gr.Interface(
-    fn=classify_images,
-    inputs=gr.Image(),
-    outputs=gr.Label(),
-    examples=['images/dogs.jpg', 'images/cats.jpg', 'images/dunno.jpg']
-)
-
-intf.launch()
-