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

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

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

# %% ../weed_classifier.ipynb 2
learn = load_learner('export.pkl')

# %% ../weed_classifier.ipynb 3
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))}

# %% ../weed_classifier.ipynb 5
title = "Weed Classifier"
description = "A weed classifier trained on the Kaggle V2 Plant Seedling dataset with fastai. Dataset has mostly african weeds in it at the moment."
article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
examples = ['SugarBeet.png']
interpretation='default'
enable_queue=True
gr.Interface(fn=predict,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).launch(enable_queue=enable_queue)