app.py

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

# %% auto 0
__all__ = ['path', 'dls', 'learn', 'categories', 'title', 'description', 'article', 'inputs', 'outputs', 'examples', 'intf',
           'is_cat', 'classify_image']

# %% app.ipynb 1
from fastai.vision.all import *
import gradio as gr

def is_cat(x): return x[0].isupper()

# %% app.ipynb 4
path = untar_data(URLs.PETS)/'images'

dls = ImageDataLoaders.from_name_func('.',
    get_image_files(path), valid_pct=0.2, seed=42,
    label_func=is_cat,
    item_tfms=Resize(192))

# %% app.ipynb 8
learn = load_learner('model.pkl')

# %% app.ipynb 10
categories = ('Dog', 'Cat')

def classify_image(img):
    img = PILImage.create(img)
    pred,pred_idx,probs = learn.predict(img)
    return {categories[i]: float(probs[i]) for i in range(len(categories))} # convert probs (pytorch tensor) into floats

# %% app.ipynb 12
title = "Cat and Dog Classifier"
description = "A classifier built with a fine-tuned Resnet 18 model."
article="<p style='text-align: center'><a href='https://github.com/deenasun' target='_blank'>Deena Sun on Github</a></p>"
inputs= gr.Image()
outputs= gr.Label()
examples = ['dog.jpg', 'cat.jpg', 'dino.jpg']

intf = gr.Interface(fn=classify_image, inputs=inputs, outputs=outputs, examples=examples, title=title, description=description, article=article)
intf.launch(share=True)