upload of the data

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+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 = "Pet Breed Classifier"
+description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
+article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
+examples = ['bengal.jpg']
+interpretation='default'
+enable_queue=True
+
+gr.Interface(
+            fn=predict,
+            inputs=gr.inputs.Image(shape=(512, 512)),
+            outputs=gr.outputs.Label(num_top_classes=3),
+            title=title,description=description,article=article,
+            examples=examples,
+            interpretation=interpretation,
+            enable_queue=enable_queue
+            ).launch()

export.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:795e213c4511b32a9eb9e43811d8ffb35233d93802a8fe7692ab83f35f1de8b3
+size 103050069

requirements.rtf

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+{\rtf1\ansi\ansicpg1252\cocoartf2638
+\cocoatextscaling0\cocoaplatform0{\fonttbl\f0\fnil\fcharset0 Menlo-Regular;}
+{\colortbl;\red255\green255\blue255;\red27\green31\blue34;\red234\green233\blue255;}
+{\*\expandedcolortbl;;\cssrgb\c14118\c16078\c18039;\cssrgb\c93333\c93333\c100000;}
+\paperw11900\paperh16840\margl1440\margr1440\vieww11520\viewh8400\viewkind0
+\deftab720
+\pard\pardeftab720\partightenfactor0
+
+\f0\fs24 \cf2 \cb3 \expnd0\expndtw0\kerning0
+fastai\
+scikit-image}