import gradio as gr
from huggingface_hub import hf_hub_download
from fastai.learner import load_learner
from fastai.vision.all import *

learn = load_learner(hf_hub_download("lpattori/Vinchucas","model.pkl"))

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

title = "Kissing Bug Recognizer"
description = "Kissing Bug Recognizer trained on the CEPAVE dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
article = ""
examples = [img for img in Path("examples").rglob("*")]

inputs = gr.Image(height=512, width=512)
outputs = gr.Label(num_top_classes=3)
demo = gr.Interface(fn=predict,inputs=inputs,
                    outputs=outputs,title=title,
                    description=description,article=article,examples=examples)
demo.launch()