app.py

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

# %% auto 0
__all__ = ['path', 'title', 'description', 'article', 'learn', 'examples', 'interpretation', 'enable_queue', 'labels',
           'classify_image']

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

# %% ../Barifier.ipynb 2
path = Path()
path.ls(file_exts='.pkl')


# %% ../Barifier.ipynb 3
title = "Bear Classifier"
description = "A bear breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."

article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"

import pathlib
plt = platform.system()
if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath

learn = load_learner('export.pkl')

examples = ['tddd.jpg']

interpretation='default'

enable_queue=True

labels = learn.dls.vocab

def classify_image(img):
    img = PILImage.create(img)
    pred,pred_idx,probs = learn.predict(img)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}

gr.Interface(fn=classify_image,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch(share=True)


# %%