updated the repository

Pets_Classification/__init__.py

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+__version__ = "0.0.1"

Pets_Classification/_modidx.py

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+# Autogenerated by nbdev
+
+d = { 'settings': { 'branch': 'main',
+                'doc_baseurl': '/',
+                'doc_host': 'https://AyyCompadre.github.io',
+                'git_url': 'https://github.com/AyyCompadre/Pets_Classification',
+                'lib_path': 'Pets_Classification'},
+  'syms': {}}

Pets_Classification/app.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
+
+# %% auto 0
+__all__ = ['plt', 'learn', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_image']
+
+# %% ../app.ipynb 2
+from fastai.vision.all import *
+import gradio as gr
+import pathlib as pl
+plt = platform.system()
+
+def is_cat(x): return x[0].isupper()
+
+# %% ../app.ipynb 4
+if plt == 'Linux' : pl.WindowsPath = pl.PosixPath
+learn = load_learner('model.pkl')
+
+# %% ../app.ipynb 6
+categories = ('Dog', 'Cat')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+# %% ../app.ipynb 8
+image = gr.Image(height=192, width = 192)
+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)

app

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+#!/usr/bin/env python
+
+# In[ ]:
+
+#|default_exp
+
+# In[5]:
+
+get_ipython().run_line_magic("pip", "install gradio==3.50")
+get_ipython().run_line_magic("pip", "install nbdev")
+get_ipython().run_line_magic("nbdev_migrate", '')
+get_ipython().run_line_magic("pip", "install fastai")
+get_ipython().run_line_magic("pip", "install ipywidgets")
+get_ipython().run_line_magic("pip", "install git+https://github.com/fastai/nbdev.git gradio fastcore")
+
+# In[6]:
+
+import pathlib as pl
+
+import gradio as gr
+import nbdev
+#|export
+from fastai.vision.all import *
+from fastcore.net import HTTPError, urljson
+
+plt = platform.system()
+
+
+def is_cat(x):
+	return x[0].isupper()
+
+
+# In[ ]:
+
+im = PILImage.create("dog.jpg")
+im.thumbnail((192, 192))
+im
+
+# In[ ]:
+
+#|export
+if plt == "Linux":
+	pl.WindowsPath = pl.PosixPath
+learn = load_learner("model.pkl")
+
+# In[ ]:
+
+learn.predict(im)
+
+# In[ ]:
+
+#|export
+categories = ("Dog", "Cat")
+
+
+def classify_image(img):
+	pred, idx, probs = learn.predict(img)
+	return dict(zip(categories, map(float, probs)))
+
+
+# In[ ]:
+
+classify_image(im)
+
+# In[ ]:
+
+#|export
+image = gr.Image(height=192, width=192)
+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)
+
+# In[7]:
+
+from nbdev.export import nb_export
+
+nb_export("app.ipynb", "app")

app.py

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

settings.ini

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+[DEFAULT]
+# All sections below are required unless otherwise specified.
+# See https://github.com/fastai/nbdev/blob/master/settings.ini for examples.
+
+### Python library ###
+repo = Pets_Classification
+lib_name = Pets_Classification
+version = 0.0.1
+min_python = 3.7
+license = apache2
+black_formatting = False
+
+### nbdev ###
+doc_path = _docs
+lib_path = Pets_Classification
+nbs_path = nbs
+recursive = True
+tst_flags = notest
+put_version_in_init = True
+
+### Docs ###
+branch = main
+custom_sidebar = False
+doc_host = https://%(user)s.github.io
+doc_baseurl = /
+git_url = https://github.com/%(user)s/%(repo)s
+title = %(lib_name)s
+
+### PyPI ###
+audience = Developers
+author = William Downing
+author_email = will.downing00@gmail.com
+copyright = 2024 onwards, %(author)s
+description = This is the calssifier between dogs and cats
+keywords = nbdev jupyter notebook python
+language = English
+status = 3
+user = AyyCompadre
+
+### Optional ###
+# requirements = fastcore pandas
+# dev_requirements = 
+# console_scripts =