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from fastai.vision.all import *
import gradio as gr
import pathlib
import os
import platform
# The below is for testing your app.py on a Windows laptop e.g. Visual Studio Code
print(platform.system())
if platform.system() == 'Windows':
# NotImplementedError: cannot instantiate 'PosixPath' on your system
# workaround for Windows where path seperator is '/'. Linux is '\'.
posix_backup = pathlib.PosixPath # remember the original path thingy
try:
pathlib.PosixPath = pathlib.WindowsPath # change to Windows
path = Path() # get current path in your runtime environment e.g. laptop, Colab, HuggingFace, Kaggle
learn = load_learner(path/'export.pkl')
finally: # when program is finished, switch back to original path thingy
pathlib.PosixPath = posix_backup
else: # Darwin aka MacOS, Linux, etc...
path = Path()
learn = load_learner(path/'export.pkl')
# Not needed, since we don't return a dict below
# same as bear_types = ['grizzly','black','teddy']
# bear_types = learn.dls.vocab
# categories = bear_types
def classify_image(img):
pred,idx,probs = learn.predict(img)
prob = float(probs[idx]) * 100
return f"This is a {pred}.\n Confidence Level : {prob:.4f}%"
# return dict(zip(categories, map(float,probs))) # not really needed here
# Define example images
# These must be local images in your repository.
# URLs to images don't seem to work well ?
# Image source: Wikipedia
examples = ['images/chicken.jpg',
'images/dog.jpg',
'images/fish.jpg']
# Define input component for image upload
image_input = gr.Image()
# Define output component for displaying text
text_output = gr.Textbox(type="text", label="Output")
# Define Gradio Interface
iface = gr.Interface(
fn=classify_image, # the function we defined above
inputs=image_input,
outputs=text_output,
# live=True means you click on any element
# and all other elements update immediately.
# Therefore, there is no Submit button needed anymore.
# Works fine, but may break Clear button,
# since there is no image uploaded yet to predict -> Error
# AssertionError: Expected an input
# live=False will give you a Submit button.
live=True,
examples=examples
)
# Run the interface
# To create a public link, set `share=True` in `launch()`.
# iface.launch(share=True)
# Note: share is for notebooks
# When deploying to HuggingFace leave it out, or else -> Error ?
iface.launch()
# Use CTRL-C to stop server in Visual Studio Code
# ^CKeyboard interruption in main thread... closing server.
# Killing tunnel 127.0.0.1:7860 <> https://6479f1bbea54b008f.gradio.live
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