import gradio
from fastai.vision.all import *

MODELS_PATH = Path('./models')
EXAMPLES_PATH = Path('./examples')

# Required function expected by fastai learn object
# it wasn't exported as a part of the pickle
# as it was defined externally to the learner object
# during the training time dataloaders setup
def label_func(filepath):
    return filepath.parent.name

LEARN = load_learner(MODELS_PATH/'usk-coffee-convnext_nano_935625.pkl')
LABELS = LEARN.dls.vocab

def gradio_predict(img):
    img = PILImage.create(img)
    _pred, _pred_idx, probs = LEARN.predict(img)
    labels_probs = {LABELS[i]: float(probs[i]) for i, _ in enumerate(LABELS)}
    return labels_probs

with open('gradio_article.md') as f:
    article = f.read()

interface_options = {
    "title": "USK-Coffee bean classifer (USK-Coffee|Convnext-nano|fast.ai)",
    "description": "A coffee bean image classifier(ConvNext nano) fine tuned on the USK-Coffee (https://comvis.unsyiah.ac.id/usk-coffee/) dataset using fastai & timm.",
    "article": article,
    "examples" : [f'{EXAMPLES_PATH}/{f.name}' for f in EXAMPLES_PATH.iterdir()],
    "interpretation": "default",
    "layout": "horizontal",
    "allow_flagging": "never",
    "enable_queue": True 
}

demo = gradio.Interface(fn=gradio_predict,
                      inputs=gradio.inputs.Image(shape=(512, 512)),
                      outputs=gradio.outputs.Label(num_top_classes=5),
                      **interface_options)

launch_options = {
    "enable_queue": True,
    "share": False, 
}

demo.launch(**launch_options)