Spaces:
Sleeping
Sleeping
nkhimin
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Browse files- .DS_Store +0 -0
- .gitattributes +2 -0
- README.md +3 -4
- app.py +44 -0
- examples/.DS_Store +0 -0
- examples/10.png +3 -0
- examples/11.png +3 -0
- examples/2.png +3 -0
- examples/British_Shorthair.png +3 -0
- examples/dachshund.jpg +3 -0
- examples/pomeranian.png +3 -0
- examples/siamese.png +3 -0
- examples/toy_poodle.png +3 -0
- model-export.pkl +3 -0
- requirements.txt +3 -0
.DS_Store
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.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.png filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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title: Pet Classifier
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emoji:
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colorFrom:
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sdk: gradio
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sdk_version: 4.27.0
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app_file: app.py
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pinned: false
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license: apache-2.0
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Pet Classifier
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emoji: 📉
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colorFrom: purple
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colorTo: gray
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sdk: gradio
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sdk_version: 4.27.0
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import gradio as gr
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from pathlib import Path
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# from fastai.vision.all import * # noqa: F403
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from fastai.learner import load_learner
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from fastai.vision.core import PILImage
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import os
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# import skimage
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# Define any custom functions or classes that the model depends on
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def is_cat(x): # Make sure to define this correctly as it was used during training
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return x[0].isupper()
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print(os.path.abspath("model-export.pkl"))
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learn = load_learner("model-export.pkl")
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labels = learn.dls.vocab
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def predict(img):
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img = PILImage.create(img)
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pred, pred_idx, probs = learn.predict(img)
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return {labels[i]: float(probs[i]) for i in range(len(labels))}
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title = "Pet Breed Classifier"
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description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
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article = "<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
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path = Path('examples')
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examples = list(path.iterdir())
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gr.Interface(
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fn=predict,
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inputs="image",
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outputs="label",
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title=title,
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description=description,
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article=article,
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examples=examples
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).launch()
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examples/.DS_Store
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Binary file (6.15 kB). View file
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examples/10.png
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Git LFS Details
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examples/11.png
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Git LFS Details
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examples/2.png
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Git LFS Details
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examples/British_Shorthair.png
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Git LFS Details
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examples/dachshund.jpg
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Git LFS Details
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examples/pomeranian.png
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Git LFS Details
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examples/siamese.png
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Git LFS Details
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examples/toy_poodle.png
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Git LFS Details
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model-export.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:31ce20b2b77e13cc4cbf4014dab953ea42f8c3a626a6983f62a76f3b3a55ff2c
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size 47072792
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requirements.txt
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gradio==4.27
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fastai==2.7.14
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scikit-image==0.22
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