initial upload

app.py

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+import gradio as gr
+import fastai
+from fastai.vision.all import *
+
+learn = load_learner("export.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 = 'Animal Classifier'
+description = 'Animal classifier tried with downloaded data from internet. Created as a demo for Gradio and HuggingFace Spaces.'
+
+examples = ['c.jpeg', 'e.jpeg', 'e2.jpeg', 'e3.jpeg', 'g.jpeg', 'ec.jpeg']
+interpretation = 'default'
+enable_queue = True
+
+gr.Interface(fn = predict, 
+             inputs = gr.inputs.Image(shape=(224,224)),
+             outputs = gr.outputs.Label(num_top_classes = 4),
+             title = title, 
+             description = description, 
+             examples = examples,
+             interpretation = interpretation, 
+             enable_queue = enable_queue
+             ).launch(share = False)

export.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:27349ddffdd666145c588a9c924b32faa2ee70717d20db03790982a725cfb714
+size 46977377

requirements.txt

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+fastai