| # 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() | |