Fruit Multi-Classifier pt.2 Deployment

Author

galopy

Published

March 5, 2023

Fruit Multi-Classifier pt. 2 Deployment

In this blog, we will deploy our model into Hugging Face Space using gradio. I have written a detailed blog here on how to deploy. This blog omits some steps.

Apple, Pear, and Orange — Image of an apple, a pear, and an orange

So, we first need to install gradio.

!pip install gradio

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WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

We import fastai and gradio. We also need get_y function that we used for datablock to train our model.

And we load our learner and check if everything works the same way as it did.

learn.dls.vocab

['apple', 'orange', 'pear', 'random images']

path = Path()
path.ls()

(#17) [Path('app.py'),Path('apple.jpg'),Path('random images.jpg'),Path('apple and orange.jpg'),Path('flagged'),Path('Deployment.ipynb'),Path('export.pkl'),Path('apple and pear.jpg'),Path('pear and orange.jpg'),Path('orange.jpg')...]

im = PILImage.create(path/'apple and orange.jpg')
im.to_thumb(256, 256)

learn.predict(im)

((#2) ['apple','orange'],
 TensorBase([ True,  True, False, False]),
 TensorBase([0.9994, 0.9993, 0.0043, 0.0011]))

Looks like it’s doing good as we trained it.

Next, we want to get the vocab as categories to predict and use our learner to predict based on an input image.

classify_images(im)

{'apple': 0.9994100332260132,
 'orange': 0.9992908239364624,
 'pear': 0.004278946202248335,
 'random images': 0.0011345148086547852}

This is how we create gradio interface and try it before deploying into Hugging Face Space.

And we will use nbdev to turn our notebook into a python script. By using nbdev, we do not have to copy and paste each cell into a file.

import nbdev

nbdev.export.nb_export('Deployment.ipynb', '.', name='app')

!cat app.py

# AUTOGENERATED! DO NOT EDIT! File to edit: Deployment.ipynb.

# %% auto 0
__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'inf', 'get_y', 'classify_images']

# %% Deployment.ipynb 2
from fastai.vision.all import *
import gradio as gr

def get_y(path):
    return parent_label(path).split(' and ')

# %% Deployment.ipynb 3
learn = load_learner('export.pkl')

# %% Deployment.ipynb 8
categories = learn.dls.vocab
def classify_images(img):
    """classifies images and returns the probabilities on each categories."""
    pred, pred_idx, probs = learn.predict(img)
    return dict(zip(categories, map(float, probs)))

# %% Deployment.ipynb 10
image = gr.inputs.Image(shape=(192, 192))
label = gr.outputs.Label()
# If you have more or less examples, edit this list.
examples = ['apple.jpg', 'orange.jpg', 'apple and orange.jpg', 
            'pear and orange.jpg', 'apple and pear.jpg', 'apple and pear and orange.jpg',
            'random images.jpg']

inf = gr.Interface(fn=classify_images, inputs=image, outputs=label, examples=examples)
inf.launch(inline=False)

That’s it. To find out next steps, check out my detailed blog after nbdev. It goes over creating a requirements.txt file and uploading files into Hugging Face Space using git.