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app.py.py

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+# -*- coding: utf-8 -*-
+"""app
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1h6riFrXG5PjYEq3Lj4OM0z3NPXmroaWI
+"""
+
+import gradio as gr
+from fastai.vision.all import *
+
+snail_learner = load_learner('snail_learner_1_2.pkl')
+
+labels = snail_learner.dls.vocab
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = snail_learner .predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+title = "Snail or Garden?"
+description = "A snail classifier trained on internet images with fastai and validated in local garden snails pictures."
+article="<p style='text-align: center'><a href='https://valentinrac.com' target='_blank'>Valentin website</a></p>"
+examples = ['snail1.jpg', 'snail2.jpg','slug.jpg','snail93.jpg','snail4.jpg','snail91.jpg','snail_slug2.jpg']
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(),
+    outputs=gr.Label(),
+    live=True,
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+)
+
+iface.launch(share=True)

requirements.txt.txt

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+fastai

snail_learner_1_2.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:66e4a1138d5485b598d62a887e1aae7eb4a45dc5b8a80907924e9f0834fa3639
+size 46972362