add pil image
Browse files- app.py +2 -0
- is_cat.ipynb +143 -0
app.py
CHANGED
@@ -1,5 +1,7 @@
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import gradio as gr
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from fastai.learner import load_learner
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def label_func(f): return f[0].isupper()
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learn = load_learner('export.pkl')
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import gradio as gr
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from fastai.learner import load_learner
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from fastai.vision.all import PILImage
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def label_func(f): return f[0].isupper()
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learn = load_learner('export.pkl')
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is_cat.ipynb
CHANGED
@@ -0,0 +1,143 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"* Running on local URL: http://127.0.0.1:7865\n",
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"* Running on public URL: https://b83dd3f618e0e3e8c5.gradio.live\n",
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"\n",
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"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div><iframe src=\"https://b83dd3f618e0e3e8c5.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": []
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"text/html": [
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"\n",
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"<style>\n",
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" /* Turns off some styling */\n",
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" progress {\n",
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" /* gets rid of default border in Firefox and Opera. */\n",
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" border: none;\n",
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" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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" background-size: auto;\n",
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" }\n",
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" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
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" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
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" }\n",
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" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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" background: #F44336;\n",
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" }\n",
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"</style>\n"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import gradio as gr\n",
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"from fastai.learner import load_learner\n",
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"from fastai.vision.all import PILImage\n",
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"\n",
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"def label_func(f): return f[0].isupper()\n",
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"learn = load_learner('export.pkl')\n",
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"\n",
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"labels = learn.dls.vocab\n",
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"\n",
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"\n",
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"def predict(img):\n",
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" img = PILImage.create(img)\n",
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" pred, pred_idx, probs = learn.predict(img)\n",
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" return {labels[i]: float(probs[i]) for i in range(len(labels))}\n",
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"\n",
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"\n",
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"title = \"Pet Breed Classifier\"\n",
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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.\"\n",
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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>\"\n",
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"examples = ['siamese.jpg']\n",
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"interpretation = 'default'\n",
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"enable_queue = True\n",
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"\n",
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"gr.Interface(\n",
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" fn=predict,\n",
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" inputs=gr.Image(type=\"filepath\"),\n",
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" outputs=gr.Label(num_top_classes=3)\n",
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").launch(share=True)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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