File size: 2,137 Bytes
91b5b18 f50cad2 91b5b18 b1fc623 e7d7520 91b5b18 f50cad2 76ce5a9 f50cad2 91b5b18 d5b1e82 91b5b18 d0d6c51 f50cad2 91b5b18 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
# Gradio app interface to dog_breed_classifier model fine-tuned on kaggle.
# This is the project from lesson 2 of the fastai Deep Learning course.
#
# Reference:
# Kaggle: https://www.kaggle.com/code/mpfoley73/dog-breed-classification
# Dog Breed dataset: https://www.kaggle.com/datasets/khushikhushikhushi/dog-breed-image-dataset
# Tanishq blog: https://www.tanishq.ai/blog/posts/2021-11-16-gradio-huggingface.html
# Fastai: https://course.fast.ai/Lessons/lesson2.html
#
import gradio as gr
from fastai.vision.all import *
import skimage
import pathlib
# Uncomment this for local (Windows) development.
# Reference: https://stackoverflow.com/questions/57286486/i-cant-load-my-model-because-i-cant-put-a-posixpath
#
# posix_backup = pathlib.PosixPath
# try:
# pathlib.PosixPath = pathlib.WindowsPath
# learn = load_learner('dog_breed_classifier.pkl')
# finally:
# pathlib.PosixPath = posix_backup
#
# Uncomment this for Hugging Face
learn = load_learner('dog_breed_classifier.pkl')
labels = learn.dls.vocab
def predict(img):
img = PILImage.create(img)
pred,pred_idx,probs = learn.predict(img)
return {labels[i]: float(probs[i]) for i in range(len(labels))}
title = "Dog Breed Classifier"
description = "A dog breed classifier trained on the Dog Breed dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
article="<p style='text-align: center'><a href='https://mpfoley73.netlify.app/post/2024-07-21-deploying-a-deep-learning-model/' target='_blank'>Blog post</a></p>"
examples = ['chester_14.jpg']
# interpretation='default'
# enable_queue=True
# Construct a Gradio Interface object from the function (usually an ML model
# inference function), Gradio input components (the number should match the
# number of function parameters), and Gradio output components (the number
# should match the number of values returned by the function).
gr.Interface(
fn=predict,
inputs=gr.Image(),
outputs=gr.Label(),
title=title,
description=description,
article=article,
examples=examples,
# interpretation=interpretation,
# enable_queue=enable_queue
).launch()
|