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Utkarsh736
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369a7ae
1
Parent(s):
2282c97
[Fix] for working in windows sys
Browse files- Bearify_nb.ipynb +34 -16
- app.py +11 -3
Bearify_nb.ipynb
CHANGED
@@ -35,7 +35,7 @@
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},
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"PILImage mode=RGB size=192x128"
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]
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},
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"execution_count":
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"metadata": {},
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"output_type": "execute_result"
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}
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"cell_type": "code",
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"execution_count":
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"metadata": {},
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"outputs": [],
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"source": [
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"import pathlib\n",
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"temp = pathlib.PosixPath\n",
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"pathlib.PosixPath = pathlib.WindowsPath"
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"id": "Ko1vxtuzACNo"
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},
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"learn = load_learner('bear_model.pkl')"
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]
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},
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"execution_count": 7,
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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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"data": {
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"('
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"execution_count": 7,
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"id": "k8MzL29fm5wO"
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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@@ -224,7 +242,7 @@
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" 'Teddy': 4.94215839808021e-07}"
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]
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"execution_count":
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"metadata": {},
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"output_type": "execute_result"
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}
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"cell_type": "code",
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"execution_count":
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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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:
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"\n",
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"To create a public link, set `share=True` in `launch()`.\n"
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]
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"data": {
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"text/plain": []
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"execution_count":
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"metadata": {},
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"output_type": "execute_result"
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"cell_type": "code",
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"execution_count":
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"metadata": {},
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"outputs": [
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{
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"provenance": []
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},
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"kernelspec": {
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"display_name": "
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"language": "python",
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"name": "
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},
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"language_info": {
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"codemirror_mode": {
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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.
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}
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},
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"nbformat": 4,
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"PILImage mode=RGB size=192x128"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"import pathlib\n",
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"temp = pathlib.PosixPath\n",
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"pathlib.PosixPath = pathlib.WindowsPath"
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},
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"id": "Ko1vxtuzACNo"
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},
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"learn = load_learner('bear_model.pkl')"
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"#|export\n",
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"pathlib.PosixPath = temp"
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]
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},
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"cell_type": "code",
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"execution_count": 7,
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"data": {
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"text/html": [
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"\n",
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" <div>\n",
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" <progress value='0' class='' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
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" 0.00% [0/1 00:00<?]\n",
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" </div>\n",
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" "
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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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"data": {
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"text/plain": [
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"('black', tensor(0), tensor([9.9997e-01, 2.5549e-05, 4.9422e-07]))"
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"execution_count": 7,
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"id": "k8MzL29fm5wO"
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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" 'Teddy': 4.94215839808021e-07}"
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]
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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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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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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://127.0.0.1:7860\n",
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"\n",
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"To create a public link, set `share=True` in `launch()`.\n"
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]
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"data": {
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"text/plain": []
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"execution_count": 10,
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"metadata": {},
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"provenance": []
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},
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"kernelspec": {
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"display_name": "bear_gh_env",
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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": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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}
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},
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"nbformat": 4,
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app.py
CHANGED
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# AUTOGENERATED! DO NOT EDIT! File to edit: ../Bearify_nb.ipynb.
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# %% auto 0
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__all__ = ['learn', 'categories', 'image', 'labels', 'examples', 'intf', 'classify_image']
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# %% ../Bearify_nb.ipynb 2
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from fastai.vision.all import *
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import gradio as gr
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# %% ../Bearify_nb.ipynb 5
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learn = load_learner('bear_model.pkl')
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# %% ../Bearify_nb.ipynb
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categories = ('Black', 'Grizzly', 'Teddy')
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def classify_image(img):
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pred, idx, probs = learn.predict(img)
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return dict(zip(categories, map(float, probs)))
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# %% ../Bearify_nb.ipynb
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image = gr.Image()
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labels = gr.Label()
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examples = ['Images/teddy.jpg', 'Images/grizzly.jpg', 'Images/black.jpeg']
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# AUTOGENERATED! DO NOT EDIT! File to edit: ../Bearify_nb.ipynb.
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# %% auto 0
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__all__ = ['temp', 'learn', 'categories', 'image', 'labels', 'examples', 'intf', 'classify_image']
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# %% ../Bearify_nb.ipynb 2
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from fastai.vision.all import *
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import gradio as gr
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# %% ../Bearify_nb.ipynb 4
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import pathlib
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temp = pathlib.PosixPath
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pathlib.PosixPath = pathlib.WindowsPath
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# %% ../Bearify_nb.ipynb 5
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learn = load_learner('bear_model.pkl')
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# %% ../Bearify_nb.ipynb 6
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pathlib.PosixPath = temp
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# %% ../Bearify_nb.ipynb 8
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categories = ('Black', 'Grizzly', 'Teddy')
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def classify_image(img):
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pred, idx, probs = learn.predict(img)
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return dict(zip(categories, map(float, probs)))
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# %% ../Bearify_nb.ipynb 10
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image = gr.Image()
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labels = gr.Label()
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examples = ['Images/teddy.jpg', 'Images/grizzly.jpg', 'Images/black.jpeg']
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