from fastai.vision.all import *
import gradio as gr
import glob
learn = load_learner('model.pkl')

labels = learn.dls.vocab

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

title='Japanese Food Classifier'
description = ('Japanese food classifier trained on duckduckgo image search generated custom dataset, fastai library and fine-tuned with the resnet18 model')
article="<p style='text-align: center'><a href='https://github.com/seamusbarnes/learn-in-public/blob/main/fastai/lesson01.ipynb' target='_blank'>Trained model</a></p>"

subfolder = Path('images')
search_pattern = str(subfolder/'*.jpg')
jpg_files = glob.glob(search_pattern)

intf = gr.Interface(
    fn=predict,
    inputs=gr.Image(height=512, width=512),
    outputs=gr.Label(num_top_classes=10),
    title=title,
    description=description,
    article=article,
    examples=jpg_files,
    examples_per_page=37
    )
intf.launch()