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from pathlib import Path | |
import gradio as gr | |
import numpy as np | |
from fastai.learner import load_learner | |
learner = load_learner(Path("model.pkl")) | |
labels = learner.dls.vocab | |
def classify(image: np.array): | |
results = learner.predict(image) | |
probabilities = results[2] | |
probabilities = map(float, probabilities) | |
return dict(zip(labels, probabilities)) | |
description = """<p>Give me a cactus picture and I'll try to guess the variety.</p> | |
<p>I can recognize the following types of cactus:</p> | |
<ul> | |
{list_items} | |
</ul> | |
<p>This model was trained <a href="https://www.kaggle.com/jdsleppy/cactus-classifier/">here</a> by <a href="https://www.joelsleppy.com">Joel</a>.</p> | |
""".format( | |
list_items='\n'.join([f' <li>{label}</li>' for label in labels]) | |
) | |
gr.Interface( | |
fn=classify, | |
inputs="image", | |
outputs="label", | |
examples=["examples/disco.jpg", "examples/scarlet_crown.jpg", "examples/silver_torch.webp"], | |
title="Cactus Classifier", | |
description=description, | |
allow_flagging="never", | |
).launch() |