import gradio as gr
from huggingface_hub import from_pretrained_fastai
from fastai.vision.all import *
repo_id = "hugginglearners/flowers_101_convnext_model"

learn = from_pretrained_fastai(repo_id)
labels = learn.dls.vocab


def predict(img):
    img = PILImage.create(img)
    _pred, _pred_w_idx, probs = learn.predict(img)
    # gradio doesn't support tensors, so converting to float
    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
    return labels_probs

interface_options = {
    "title": "Identify which flower it is?",
    "description": "It’s difficult to fathom just how vast and diverse our natural world is.There are over 5,000 species of mammals, 10,000 species of birds, 30,000 species of fish – and astonishingly, over 400,000 different types of flowers.\n Identify which flower variety it is by uploading your images of flowers.",
    "interpretation": "default",
    "layout": "horizontal",
    "allow_flagging": "never",
}

demo = gr.Interface(
    fn=predict,
    inputs=gr.inputs.Image(shape=(192, 192)),
    outputs=gr.outputs.Label(num_top_classes=3),
    **interface_options,
)

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

demo.launch(**launch_options)