import gradio as gr
from fastai.vision.all import *
#import skimage # for interpretation

learn = load_learner('grain_classifier.pkl')

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

title = "Grain Classifier"
description = "A grain classifier trained on the 150 online images 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>"
examples = ['sample/barley_seed.jpeg', 
            'sample/corn_seed.jpeg', 
            'sample/rye_seed.jpeg', 
            'sample/wheat_seed.jpeg']
interpretation='default' # what parts of the input are responsible for the output
enable_queue=True 

gr.Interface(fn=predict,
             inputs=gr.inputs.Image(shape=(224, 224)),
             outputs=gr.outputs.Label(num_top_classes=4),
             title=title,
             description=description,
             #article=article,
             examples=examples,
             interpretation=interpretation,
             enable_queue=enable_queue).launch()