Implement prediction interface

app.py

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+from fastai.vision.all import *
+import gradio
+
+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))}
+
+image = gradio.Image(height=512, width=512)
+label = gradio.Label(num_top_classes=3)
+gradio.Interface(fn=predict, inputs=image, outputs=label).launch(share=True)

export.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:56bc75d42036f29966617d4c006c206281a4573c60084931d653ca73bc2cebbb
+size 46984766

export.pkl:Zone.Identifier

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+[ZoneTransfer]
+ZoneId=3
+ReferrerUrl=https://www.kaggle.com/
+HostUrl=https://www.kaggleusercontent.com/kf/167388572/eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..1CBCbxv6dmJny_qaBNPHJw.pE4lpzJ-QjfGWIOMTyyRCZydr2NAaVbJojjVrhkJRJkIMDuaSvfZLcvoLcZNuSidDS-STzes82f9vHxD3hFwTO6yO6bpb8m4g1dHcf5MGnVoQs6fXSfsCvniYYyPj11QweoNsrGXp0G-X-x0ikEesPEtGLeXgG6VCNobEZi61zJ6CNsfexazn0s0pTSn3-NnCr1XTAId_eav6oLzRWy7ux12TvrGLyuEz7VzVTQIXpkxBZtkqiAFixmdq3MFLYP0ceQFnI_kCdC2SksNu8uoPZycA2oYKBDHjUMotjoqDnb7VXfechVUi0e9cEfK560maK1vorIgM1UmWkX5OQSDsgi6FPgX12tyFbXVXj9dFjKmlmpG5Xc7u6gV1csNeV7OveTl10MkIX_w1t-AupdTxsTqLsd-uKzb-YglxDfYC16iFrTW5wKKpJ9_KaQZZMdjEFz8XYpRUBaw7Rj5kHZ1itL6nMBJX6piKJJ8npHXmgYSauTPuIRdhIvbX9Dxq7Gk7gB8LhKRUvOG9B7C79aTUgH2s6T2H64jFI1paU3yGoI8Pobb6B-Qu2JYqUwlLCo2aidA6ayBLXSbTR_cKvE3OoB5X5WhmQeOUUD4LPZ06VVkZT32r3AWTF9tTU16YR--DZH5l3ht5DjnA0zVtlhbrg.3k4jf-VYcJ_tWFy_vfxTjg/export.pkl

requirements.txt

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+pytorch
+fastai