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Upload appy.ipynb

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appy.ipynb ADDED
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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": 1,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "C:\\Users\\RAVI\\AppData\\Roaming\\Python\\Python39\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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+ " from .autonotebook import tqdm as notebook_tqdm\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "from fastai.vision.all import *\n",
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+ "import gradio as gr"
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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": 5,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import pathlib"
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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": 6,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "\n",
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+ "def new_path(cls, *args, **kwargs):\n",
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+ " \n",
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+ " cls = pathlib.WindowsPath\n",
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+ " self = cls._from_parts(args)\n",
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+ " if not self._flavour.is_supported:\n",
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+ " raise NotImplementedError(\"cannot instantiate %r on your system\"\n",
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+ " % (cls.__name__,))\n",
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+ " return self\n",
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+ "Path.__new__=new_path"
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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": 7,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "def is_cat(x): return x[0].isupper() \n",
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+ "\n",
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+ "learn = load_learner('model.pkl')\n",
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+ "categories = ('Dog', 'Cat')"
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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": 8,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "def classify_image(img):\n",
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+ " pred,idx,probs = learn.predict(img)\n",
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+ " return dict(zip(categories, map(float, probs)))\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": 10,
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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://127.0.0.1:7860/\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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+ },
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+ {
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+ "data": {
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+ "text/plain": [
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+ "(<gradio.routes.App at 0x1be650336a0>, 'http://127.0.0.1:7860/', None)"
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+ ]
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+ },
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+ "execution_count": 10,
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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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+ "source": [
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+ "\n",
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+ "image = gr.Image(shape=(192,192))\n",
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+ "label = gr.Label()\n",
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+ "examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']\n",
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+ "\n",
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+ "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
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+ "intf.launch(inline=False)\n",
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+ "\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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+ "metadata": {
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+ "kernelspec": {
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+ "display_name": "Python 3.9.4 64-bit",
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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": "3.9.4"
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+ },
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+ "orig_nbformat": 4,
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+ "vscode": {
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+ "interpreter": {
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+ "hash": "11938c6bc6919ae2720b4d5011047913343b08a43b18698fd82dedb0d4417594"
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+ }
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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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+ }