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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() |