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.ipynb_checkpoints/notebook-checkpoint.ipynb
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"cells": [
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"execution_count": 1,
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"id": "4b40cb3a-544a-4b23-8c00-431cb7133130",
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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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"Python 3.11.5\n"
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]
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}
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],
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"source": [
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"%%bash\n",
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"python --version"
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]
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "93e1afb8-f78c-4862-9d56-a06a3559b4d1",
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"metadata": {},
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"outputs": [],
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"source": [
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"#|default_exp app"
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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": 3,
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"id": "2adf2fa8-199b-48e4-a91c-9a093032480c",
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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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"from fastai.vision.all import *\n",
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"import gradio as gr\n",
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"import timm"
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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": 4,
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"id": "f0218bf1-1836-4d7a-8d47-33584471f28b",
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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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"learn = load_learner('cycle-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": 5,
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"id": "168ac2e4-f83b-4ce0-8f23-00999eb5d556",
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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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"categories = learn.dls.vocab\n",
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"\n",
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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)))"
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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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"id": "d343a0d3-40fd-4502-a86b-cb3bac9fdf7f",
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"metadata": {},
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"outputs": [
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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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"metadata": {},
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"data": {
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"metadata": {},
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"output_type": "display_data"
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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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"images/unicycle.jpeg is a tricycle\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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"\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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" }\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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"metadata": {},
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"metadata": {},
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"output_type": "display_data"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"images/bicycle.jpeg is a bicycle\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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"\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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" }\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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"images/tricycle.png is a tricycle\n"
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]
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}
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],
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"source": [
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"# Upload your own images and link them\n",
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"examples = ['images/unicycle.jpeg', 'images/bicycle.jpeg', 'images/tricycle.png']\n",
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"\n",
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"for example in examples:\n",
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" image = PILImage.create(example)\n",
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" res_dict = classify_image(image)\n",
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" top = max(res_dict, key=res_dict.get)\n",
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"\n",
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" print(example + ' is a '+ top)"
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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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"id": "156a1fa0-e124-4a18-b411-367e7926afa4",
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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/html": [
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"<div><iframe src=\"http://127.0.0.1:7860/\" 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": 7,
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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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"#|export\n",
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"\n",
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"image = gr.Image()\n",
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"label = gr.Label()\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()"
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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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"id": "2894f2be-e453-4795-8a16-2aa4770aa16d",
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"metadata": {},
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"outputs": [],
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"source": [
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"import nbdev"
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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": 9,
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"id": "10568397-2167-4c39-8120-436e577b452d",
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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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"Export successful\n"
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]
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}
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],
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"source": [
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"nbdev.export.nb_export('notebook.ipynb', '')\n",
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"print('Export successful')"
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]
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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 (ipykernel)",
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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": "3.11.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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