import gradio as gr
from fastai.vision.all import *
from huggingface_hub import from_pretrained_fastai

repo_id = "kurianbenoy/paddy_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": "Paddy Doctor",
    "description": "Paddy cultivation requires consistent supervision because several diseases and pests might affect the paddy crops, leading to up to 70% yield loss. This spaces is an online demo to showcase a model build for [real-world Kaggle competition](https://www.kaggle.com/competitions/paddy-disease-classification/overview) to identify diseases from images of paddy leaves.",
    "interpretation": "default",
    "layout": "horizontal",
    # Audio from validation file
    "examples": [
        "100098.jpg",
        "100002.jpg",
        "100048.jpg"
    ],
    "allow_flagging": "never",
}

demo = gr.Interface(
    fn=predict,
    inputs=gr.inputs.Image(shape=(480, 480)),
    outputs=gr.outputs.Label(num_top_classes=3),
    **interface_options,
)

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

demo.launch(**launch_options)