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first_deployment

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Files changed (11) hide show
  1. .gitignore +8 -0
  2. .ipynb_checkpoints/Runpod-checkpoint.ipynb +6 -0
  3. .ipynb_checkpoints/app-checkpoint.ipynb +159 -0
  4. .ipynb_checkpoints/load_data-checkpoint.ipynb +92 -0
  5. .ipynb_checkpoints/pre-process-checkpoint.ipynb +6 -0
  6. Runpod.ipynb +116 -0
  7. app.ipynb +164 -0
  8. app.py +34 -0
  9. data_loader.ipynb +0 -0
  10. load_data.ipynb +0 -0
  11. model.pkl +3 -0
.gitignore ADDED
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+ **/cache/
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+ *.tmp
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+ pre-processed.zip
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+ pre-process.ipynb
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+ model_pics/
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+ pre-processed/
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+ Runpod.ipynb/
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+ stars.csv
.ipynb_checkpoints/Runpod-checkpoint.ipynb ADDED
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+ {
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+ "cells": [],
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+ "metadata": {},
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+ "nbformat": 4,
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+ "nbformat_minor": 5
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+ }
.ipynb_checkpoints/app-checkpoint.ipynb ADDED
@@ -0,0 +1,159 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ "source": [
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+ "#|default_exp app2"
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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": 2,
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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 io\n",
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+ "from PIL import Image\n",
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+ "\n",
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+ "def pet_class(x): return x"
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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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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "#|export\n",
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+ "learn = load_learner('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": 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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+ "#categories = ('basketball ball','golf ball', 'rugby ball', 'soccer ball')\n",
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+ "\n",
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+ "def classify_image(img):\n",
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+ " pred_class, 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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+ "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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+ },
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+ "execution_count": 6,
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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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+ "image = gr.components.Image(shape=(192,192))\n",
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+ "label = gr.components.Label()\n",
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+ "#examples = ['basketball.png', 'golf_ball.jpg', 'rugby_ball.jpg', 'soccer_ball.jpg']\n",
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+ "\n",
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+ "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label)\n",
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+ "intf.launch(inline=False)"
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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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+ {
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+ "ename": "FileNotFoundError",
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+ "evalue": "[Errno 2] No such file or directory: 'basketball.png'",
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+ "output_type": "error",
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+ "traceback": [
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+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
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+ "Cell \u001b[0;32mIn[5], line 6\u001b[0m\n\u001b[1;32m 3\u001b[0m label \u001b[38;5;241m=\u001b[39m gr\u001b[38;5;241m.\u001b[39mcomponents\u001b[38;5;241m.\u001b[39mLabel()\n\u001b[1;32m 4\u001b[0m examples \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbasketball.png\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgolf_ball.jpg\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrugby_ball.jpg\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msoccer_ball.jpg\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m----> 6\u001b[0m intf \u001b[38;5;241m=\u001b[39m \u001b[43mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mInterface\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclassify_image\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexamples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexamples\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m intf\u001b[38;5;241m.\u001b[39mlaunch(inline\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
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+ "File \u001b[0;32m~/mambaforge/lib/python3.10/site-packages/gradio/interface.py:475\u001b[0m, in \u001b[0;36mInterface.__init__\u001b[0;34m(self, fn, inputs, outputs, examples, cache_examples, examples_per_page, live, interpretation, num_shap, title, description, article, thumbnail, theme, css, allow_flagging, flagging_options, flagging_dir, flagging_callback, analytics_enabled, batch, max_batch_size, _api_mode, **kwargs)\u001b[0m\n\u001b[1;32m 467\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattach_interpretation_events(\n\u001b[1;32m 468\u001b[0m interpretation_btn,\n\u001b[1;32m 469\u001b[0m interpretation_set,\n\u001b[1;32m 470\u001b[0m input_component_column,\n\u001b[1;32m 471\u001b[0m interpret_component_column,\n\u001b[1;32m 472\u001b[0m )\n\u001b[1;32m 474\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattach_flagging_events(flag_btns, clear_btn)\n\u001b[0;32m--> 475\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrender_examples\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 476\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrender_article()\n\u001b[1;32m 478\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_config_file()\n",
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+ "File \u001b[0;32m~/mambaforge/lib/python3.10/site-packages/gradio/interface.py:791\u001b[0m, in \u001b[0;36mInterface.render_examples\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 785\u001b[0m non_state_inputs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 786\u001b[0m c \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_components \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, State)\n\u001b[1;32m 787\u001b[0m ]\n\u001b[1;32m 788\u001b[0m non_state_outputs \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 789\u001b[0m c \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_components \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(c, State)\n\u001b[1;32m 790\u001b[0m ]\n\u001b[0;32m--> 791\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexamples_handler \u001b[38;5;241m=\u001b[39m \u001b[43mExamples\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 792\u001b[0m \u001b[43m \u001b[49m\u001b[43mexamples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexamples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 793\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnon_state_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore\u001b[39;49;00m\n\u001b[1;32m 794\u001b[0m \u001b[43m \u001b[49m\u001b[43moutputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnon_state_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore\u001b[39;49;00m\n\u001b[1;32m 795\u001b[0m \u001b[43m \u001b[49m\u001b[43mfn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 796\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_examples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcache_examples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 797\u001b[0m \u001b[43m \u001b[49m\u001b[43mexamples_per_page\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexamples_per_page\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 798\u001b[0m \u001b[43m \u001b[49m\u001b[43m_api_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapi_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 799\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 800\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
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+ "File \u001b[0;32m~/mambaforge/lib/python3.10/site-packages/gradio/helpers.py:54\u001b[0m, in \u001b[0;36mcreate_examples\u001b[0;34m(examples, inputs, outputs, fn, cache_examples, examples_per_page, _api_mode, label, elem_id, run_on_click, preprocess, postprocess, batch)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate_examples\u001b[39m(\n\u001b[1;32m 39\u001b[0m examples: List[Any] \u001b[38;5;241m|\u001b[39m List[List[Any]] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m 40\u001b[0m inputs: IOComponent \u001b[38;5;241m|\u001b[39m List[IOComponent],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 51\u001b[0m batch: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 52\u001b[0m ):\n\u001b[1;32m 53\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Top-level synchronous function that creates Examples. Provided for backwards compatibility, i.e. so that gr.Examples(...) can be used to create the Examples component.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 54\u001b[0m examples_obj \u001b[38;5;241m=\u001b[39m \u001b[43mExamples\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[43m \u001b[49m\u001b[43mexamples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexamples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 57\u001b[0m \u001b[43m \u001b[49m\u001b[43moutputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 58\u001b[0m \u001b[43m \u001b[49m\u001b[43mfn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 59\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_examples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_examples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 60\u001b[0m \u001b[43m \u001b[49m\u001b[43mexamples_per_page\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexamples_per_page\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 61\u001b[0m \u001b[43m \u001b[49m\u001b[43m_api_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_api_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 62\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 63\u001b[0m \u001b[43m \u001b[49m\u001b[43melem_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43melem_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 64\u001b[0m \u001b[43m \u001b[49m\u001b[43mrun_on_click\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_on_click\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreprocess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreprocess\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 66\u001b[0m \u001b[43m \u001b[49m\u001b[43mpostprocess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpostprocess\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 67\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 68\u001b[0m \u001b[43m \u001b[49m\u001b[43m_initiated_directly\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 70\u001b[0m utils\u001b[38;5;241m.\u001b[39msynchronize_async(examples_obj\u001b[38;5;241m.\u001b[39mcreate)\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m examples_obj\n",
100
+ "File \u001b[0;32m~/mambaforge/lib/python3.10/site-packages/gradio/helpers.py:201\u001b[0m, in \u001b[0;36mExamples.__init__\u001b[0;34m(self, examples, inputs, outputs, fn, cache_examples, examples_per_page, _api_mode, label, elem_id, run_on_click, preprocess, postprocess, batch, _initiated_directly)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch \u001b[38;5;241m=\u001b[39m batch\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m utils\u001b[38;5;241m.\u001b[39mset_directory(working_directory):\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocessed_examples \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 202\u001b[0m [\n\u001b[1;32m 203\u001b[0m component\u001b[38;5;241m.\u001b[39mpostprocess(sample)\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m component, sample \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(inputs, example)\n\u001b[1;32m 205\u001b[0m ]\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m example \u001b[38;5;129;01min\u001b[39;00m examples\n\u001b[1;32m 207\u001b[0m ]\n\u001b[1;32m 208\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnon_none_processed_examples \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 209\u001b[0m [ex \u001b[38;5;28;01mfor\u001b[39;00m (ex, keep) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(example, input_has_examples) \u001b[38;5;28;01mif\u001b[39;00m keep]\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m example \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocessed_examples\n\u001b[1;32m 211\u001b[0m ]\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache_examples:\n",
101
+ "File \u001b[0;32m~/mambaforge/lib/python3.10/site-packages/gradio/helpers.py:202\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch \u001b[38;5;241m=\u001b[39m batch\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m utils\u001b[38;5;241m.\u001b[39mset_directory(working_directory):\n\u001b[1;32m 201\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocessed_examples \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m--> 202\u001b[0m [\n\u001b[1;32m 203\u001b[0m component\u001b[38;5;241m.\u001b[39mpostprocess(sample)\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m component, sample \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(inputs, example)\n\u001b[1;32m 205\u001b[0m ]\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m example \u001b[38;5;129;01min\u001b[39;00m examples\n\u001b[1;32m 207\u001b[0m ]\n\u001b[1;32m 208\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnon_none_processed_examples \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 209\u001b[0m [ex \u001b[38;5;28;01mfor\u001b[39;00m (ex, keep) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(example, input_has_examples) \u001b[38;5;28;01mif\u001b[39;00m keep]\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m example \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocessed_examples\n\u001b[1;32m 211\u001b[0m ]\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache_examples:\n",
102
+ "File \u001b[0;32m~/mambaforge/lib/python3.10/site-packages/gradio/helpers.py:203\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch \u001b[38;5;241m=\u001b[39m batch\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m utils\u001b[38;5;241m.\u001b[39mset_directory(working_directory):\n\u001b[1;32m 201\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocessed_examples \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 202\u001b[0m [\n\u001b[0;32m--> 203\u001b[0m \u001b[43mcomponent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpostprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43msample\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m component, sample \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(inputs, example)\n\u001b[1;32m 205\u001b[0m ]\n\u001b[1;32m 206\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m example \u001b[38;5;129;01min\u001b[39;00m examples\n\u001b[1;32m 207\u001b[0m ]\n\u001b[1;32m 208\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnon_none_processed_examples \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 209\u001b[0m [ex \u001b[38;5;28;01mfor\u001b[39;00m (ex, keep) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(example, input_has_examples) \u001b[38;5;28;01mif\u001b[39;00m keep]\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m example \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprocessed_examples\n\u001b[1;32m 211\u001b[0m ]\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache_examples:\n",
103
+ "File \u001b[0;32m~/mambaforge/lib/python3.10/site-packages/gradio/components.py:1634\u001b[0m, in \u001b[0;36mImage.postprocess\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m 1632\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m processing_utils\u001b[38;5;241m.\u001b[39mencode_pil_to_base64(y)\n\u001b[1;32m 1633\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(y, (\u001b[38;5;28mstr\u001b[39m, Path)):\n\u001b[0;32m-> 1634\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprocessing_utils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode_url_or_file_to_base64\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1635\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1636\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot process this value as an Image\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
104
+ "File \u001b[0;32m~/mambaforge/lib/python3.10/site-packages/gradio/processing_utils.py:70\u001b[0m, in \u001b[0;36mencode_url_or_file_to_base64\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m encode_url_to_base64(path)\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 70\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mencode_file_to_base64\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n",
105
+ "File \u001b[0;32m~/mambaforge/lib/python3.10/site-packages/gradio/processing_utils.py:94\u001b[0m, in \u001b[0;36mencode_file_to_base64\u001b[0;34m(f)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mencode_file_to_base64\u001b[39m(f):\n\u001b[0;32m---> 94\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m file:\n\u001b[1;32m 95\u001b[0m encoded_string \u001b[38;5;241m=\u001b[39m base64\u001b[38;5;241m.\u001b[39mb64encode(file\u001b[38;5;241m.\u001b[39mread())\n\u001b[1;32m 96\u001b[0m base64_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(encoded_string, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
106
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'basketball.png'"
107
+ ]
108
+ }
109
+ ],
110
+ "source": [
111
+ "#|export\n",
112
+ "image = gr.components.Image(shape=(192,192))\n",
113
+ "label = gr.components.Label()\n",
114
+ "examples = ['basketball.png', 'golf_ball.jpg', 'rugby_ball.jpg', 'soccer_ball.jpg']\n",
115
+ "\n",
116
+ "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
117
+ "intf.launch(inline=False)"
118
+ ]
119
+ },
120
+ {
121
+ "cell_type": "code",
122
+ "execution_count": 4,
123
+ "metadata": {},
124
+ "outputs": [],
125
+ "source": [
126
+ "import nbdev\n",
127
+ "nbdev.export.nb_export('app.ipynb', 'app2')"
128
+ ]
129
+ },
130
+ {
131
+ "cell_type": "code",
132
+ "execution_count": null,
133
+ "metadata": {},
134
+ "outputs": [],
135
+ "source": []
136
+ }
137
+ ],
138
+ "metadata": {
139
+ "kernelspec": {
140
+ "display_name": "Python 3 (ipykernel)",
141
+ "language": "python",
142
+ "name": "python3"
143
+ },
144
+ "language_info": {
145
+ "codemirror_mode": {
146
+ "name": "ipython",
147
+ "version": 3
148
+ },
149
+ "file_extension": ".py",
150
+ "mimetype": "text/x-python",
151
+ "name": "python",
152
+ "nbconvert_exporter": "python",
153
+ "pygments_lexer": "ipython3",
154
+ "version": "3.10.9"
155
+ }
156
+ },
157
+ "nbformat": 4,
158
+ "nbformat_minor": 2
159
+ }
.ipynb_checkpoints/load_data-checkpoint.ipynb ADDED
@@ -0,0 +1,92 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 4,
6
+ "id": "c168f24a",
7
+ "metadata": {},
8
+ "outputs": [],
9
+ "source": [
10
+ "!pip install -Uqq fastai duckduckgo_search pandas"
11
+ ]
12
+ },
13
+ {
14
+ "cell_type": "code",
15
+ "execution_count": 5,
16
+ "id": "c84aa916",
17
+ "metadata": {},
18
+ "outputs": [],
19
+ "source": [
20
+ "from duckduckgo_search import ddg_images\n",
21
+ "from fastcore.all import *\n",
22
+ "from time import sleep\n",
23
+ "import pandas as pd\n",
24
+ "from io import StringIO\n",
25
+ "\n",
26
+ "\n",
27
+ "def search_images(term, max_images=50):\n",
28
+ " print(f\"Searching for '{term}'\")\n",
29
+ " return L(ddg_images(term, max_results=max_images)).itemgot('image')"
30
+ ]
31
+ },
32
+ {
33
+ "cell_type": "code",
34
+ "execution_count": null,
35
+ "id": "fef32513",
36
+ "metadata": {},
37
+ "outputs": [],
38
+ "source": [
39
+ "df = pd.read_csv('')"
40
+ ]
41
+ },
42
+ {
43
+ "cell_type": "code",
44
+ "execution_count": null,
45
+ "id": "a17948bf",
46
+ "metadata": {},
47
+ "outputs": [],
48
+ "source": [
49
+ "searches = 'basketball ball','rugby ball', 'soccer ball', 'golf ball'\n",
50
+ "path = Path('model_pics')"
51
+ ]
52
+ },
53
+ {
54
+ "cell_type": "code",
55
+ "execution_count": null,
56
+ "id": "a78f7280",
57
+ "metadata": {},
58
+ "outputs": [],
59
+ "source": [
60
+ "\n",
61
+ "\n",
62
+ "\n",
63
+ "for o in searches:\n",
64
+ " dest = (path/o)\n",
65
+ " dest.mkdir(exist_ok=True, parents=True)\n",
66
+ " download_images(dest, urls=search_images(f'{o} photo'))\n",
67
+ " sleep(10) # Pause between searches to avoid over-loading server"
68
+ ]
69
+ }
70
+ ],
71
+ "metadata": {
72
+ "kernelspec": {
73
+ "display_name": "Python 3 (ipykernel)",
74
+ "language": "python",
75
+ "name": "python3"
76
+ },
77
+ "language_info": {
78
+ "codemirror_mode": {
79
+ "name": "ipython",
80
+ "version": 3
81
+ },
82
+ "file_extension": ".py",
83
+ "mimetype": "text/x-python",
84
+ "name": "python",
85
+ "nbconvert_exporter": "python",
86
+ "pygments_lexer": "ipython3",
87
+ "version": "3.10.9"
88
+ }
89
+ },
90
+ "nbformat": 4,
91
+ "nbformat_minor": 5
92
+ }
.ipynb_checkpoints/pre-process-checkpoint.ipynb ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [],
3
+ "metadata": {},
4
+ "nbformat": 4,
5
+ "nbformat_minor": 5
6
+ }
Runpod.ipynb ADDED
@@ -0,0 +1,116 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": null,
6
+ "id": "fccb1a4c",
7
+ "metadata": {},
8
+ "outputs": [],
9
+ "source": [
10
+ "!pip install -Uqq fastai pandas gdown zipfile"
11
+ ]
12
+ },
13
+ {
14
+ "cell_type": "code",
15
+ "execution_count": null,
16
+ "id": "a07a5ad4",
17
+ "metadata": {},
18
+ "outputs": [],
19
+ "source": [
20
+ "from fastcore.all import *\n",
21
+ "from fastai.vision.all import *\n",
22
+ "from time import sleep\n",
23
+ "import pandas as pd\n",
24
+ "from io import StringIO\n",
25
+ "import gdown\n",
26
+ "import zipfile"
27
+ ]
28
+ },
29
+ {
30
+ "cell_type": "code",
31
+ "execution_count": null,
32
+ "id": "5ace4a07",
33
+ "metadata": {},
34
+ "outputs": [],
35
+ "source": [
36
+ "file_id = \"YOUR_FILE_ID\"\n",
37
+ "url = f'https://drive.google.com/uc?id={file_id}'\n",
38
+ "output = 'images.zip'\n",
39
+ "gdown.download(url, output, quiet=False)"
40
+ ]
41
+ },
42
+ {
43
+ "cell_type": "code",
44
+ "execution_count": null,
45
+ "id": "95f78949",
46
+ "metadata": {},
47
+ "outputs": [],
48
+ "source": [
49
+ "with zipfile.ZipFile('images.zip', 'r') as zip_ref:\n",
50
+ " zip_ref.extractall('images')"
51
+ ]
52
+ },
53
+ {
54
+ "cell_type": "code",
55
+ "execution_count": null,
56
+ "id": "8558997b",
57
+ "metadata": {},
58
+ "outputs": [],
59
+ "source": [
60
+ "path = Path('images')\n",
61
+ "\n",
62
+ "dls = DataBlock(\n",
63
+ " blocks=(ImageBlock, CategoryBlock), \n",
64
+ " get_items=get_image_files, \n",
65
+ " splitter=RandomSplitter(valid_pct=0.2, seed=42),\n",
66
+ " get_y=parent_label,\n",
67
+ " item_tfms=[Resize(192, method='squish')]\n",
68
+ ").dataloaders(path, bs=64)\n",
69
+ "\n",
70
+ "dls.show_batch(max_n=6)"
71
+ ]
72
+ },
73
+ {
74
+ "cell_type": "code",
75
+ "execution_count": null,
76
+ "id": "b6d7f347",
77
+ "metadata": {},
78
+ "outputs": [],
79
+ "source": [
80
+ "learn = vision_learner(dls, resnet18, metrics=error_rate)\n",
81
+ "learn.fine_tune(5)"
82
+ ]
83
+ },
84
+ {
85
+ "cell_type": "code",
86
+ "execution_count": null,
87
+ "id": "ca176cad",
88
+ "metadata": {},
89
+ "outputs": [],
90
+ "source": [
91
+ "learn.export('model.pkl')"
92
+ ]
93
+ }
94
+ ],
95
+ "metadata": {
96
+ "kernelspec": {
97
+ "display_name": "Python 3 (ipykernel)",
98
+ "language": "python",
99
+ "name": "python3"
100
+ },
101
+ "language_info": {
102
+ "codemirror_mode": {
103
+ "name": "ipython",
104
+ "version": 3
105
+ },
106
+ "file_extension": ".py",
107
+ "mimetype": "text/x-python",
108
+ "name": "python",
109
+ "nbconvert_exporter": "python",
110
+ "pygments_lexer": "ipython3",
111
+ "version": "3.10.9"
112
+ }
113
+ },
114
+ "nbformat": 4,
115
+ "nbformat_minor": 5
116
+ }
app.ipynb ADDED
@@ -0,0 +1,164 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 1,
6
+ "metadata": {},
7
+ "outputs": [],
8
+ "source": [
9
+ "#|default_exp app2"
10
+ ]
11
+ },
12
+ {
13
+ "cell_type": "code",
14
+ "execution_count": 2,
15
+ "metadata": {},
16
+ "outputs": [],
17
+ "source": [
18
+ "#|export\n",
19
+ "from fastai.vision.all import *\n",
20
+ "import gradio as gr\n",
21
+ "import io\n",
22
+ "from PIL import Image\n",
23
+ "\n",
24
+ "def pet_class(x): return x"
25
+ ]
26
+ },
27
+ {
28
+ "cell_type": "code",
29
+ "execution_count": 3,
30
+ "metadata": {},
31
+ "outputs": [],
32
+ "source": [
33
+ "#|export\n",
34
+ "learn = load_learner('model.pkl')"
35
+ ]
36
+ },
37
+ {
38
+ "cell_type": "code",
39
+ "execution_count": 12,
40
+ "metadata": {},
41
+ "outputs": [],
42
+ "source": [
43
+ "#|export\n",
44
+ "#categories = ('basketball ball','golf ball', 'rugby ball', 'soccer ball')\n",
45
+ "\n",
46
+ "def classify_image(img, top_k=5):\n",
47
+ " pred_class, pred_idx, probs = learn.predict(img)\n",
48
+ " categories = learn.dls.vocab\n",
49
+ " sorted_probs_indices = probs.argsort(descending=True)\n",
50
+ " top_categories = [categories[i] for i in sorted_probs_indices[:top_k]]\n",
51
+ " top_probs = probs[sorted_probs_indices[:top_k]]\n",
52
+ " return dict(zip(top_categories, map(float, top_probs)))"
53
+ ]
54
+ },
55
+ {
56
+ "cell_type": "code",
57
+ "execution_count": 13,
58
+ "metadata": {},
59
+ "outputs": [
60
+ {
61
+ "name": "stdout",
62
+ "output_type": "stream",
63
+ "text": [
64
+ "Running on local URL: http://127.0.0.1:7863\n",
65
+ "\n",
66
+ "To create a public link, set `share=True` in `launch()`.\n"
67
+ ]
68
+ },
69
+ {
70
+ "data": {
71
+ "text/plain": []
72
+ },
73
+ "execution_count": 13,
74
+ "metadata": {},
75
+ "output_type": "execute_result"
76
+ },
77
+ {
78
+ "data": {
79
+ "text/html": [
80
+ "\n",
81
+ "<style>\n",
82
+ " /* Turns off some styling */\n",
83
+ " progress {\n",
84
+ " /* gets rid of default border in Firefox and Opera. */\n",
85
+ " border: none;\n",
86
+ " /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
87
+ " background-size: auto;\n",
88
+ " }\n",
89
+ " progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
90
+ " background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
91
+ " }\n",
92
+ " .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
93
+ " background: #F44336;\n",
94
+ " }\n",
95
+ "</style>\n"
96
+ ],
97
+ "text/plain": [
98
+ "<IPython.core.display.HTML object>"
99
+ ]
100
+ },
101
+ "metadata": {},
102
+ "output_type": "display_data"
103
+ },
104
+ {
105
+ "data": {
106
+ "text/html": [],
107
+ "text/plain": [
108
+ "<IPython.core.display.HTML object>"
109
+ ]
110
+ },
111
+ "metadata": {},
112
+ "output_type": "display_data"
113
+ }
114
+ ],
115
+ "source": [
116
+ "#|export\n",
117
+ "image = gr.components.Image(shape=(192,192))\n",
118
+ "label = gr.components.Label()\n",
119
+ "#examples = ['basketball.png', 'golf_ball.jpg', 'rugby_ball.jpg', 'soccer_ball.jpg']\n",
120
+ "\n",
121
+ "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label)\n",
122
+ "intf.launch(inline=False)"
123
+ ]
124
+ },
125
+ {
126
+ "cell_type": "code",
127
+ "execution_count": 4,
128
+ "metadata": {},
129
+ "outputs": [],
130
+ "source": [
131
+ "import nbdev\n",
132
+ "nbdev.export.nb_export('app.ipynb', 'app2')"
133
+ ]
134
+ },
135
+ {
136
+ "cell_type": "code",
137
+ "execution_count": null,
138
+ "metadata": {},
139
+ "outputs": [],
140
+ "source": []
141
+ }
142
+ ],
143
+ "metadata": {
144
+ "kernelspec": {
145
+ "display_name": "Python 3 (ipykernel)",
146
+ "language": "python",
147
+ "name": "python3"
148
+ },
149
+ "language_info": {
150
+ "codemirror_mode": {
151
+ "name": "ipython",
152
+ "version": 3
153
+ },
154
+ "file_extension": ".py",
155
+ "mimetype": "text/x-python",
156
+ "name": "python",
157
+ "nbconvert_exporter": "python",
158
+ "pygments_lexer": "ipython3",
159
+ "version": "3.10.9"
160
+ }
161
+ },
162
+ "nbformat": 4,
163
+ "nbformat_minor": 2
164
+ }
app.py ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
2
+
3
+ # %% auto 0
4
+ __all__ = ['learn', 'image', 'label', 'intf', 'pet_class', 'classify_image']
5
+
6
+ # %% ../app.ipynb 1
7
+ from fastai.vision.all import *
8
+ import gradio as gr
9
+ import io
10
+ from PIL import Image
11
+
12
+ def pet_class(x): return x
13
+
14
+ # %% ../app.ipynb 2
15
+ learn = load_learner('model.pkl')
16
+
17
+ # %% ../app.ipynb 3
18
+ #categories = ('basketball ball','golf ball', 'rugby ball', 'soccer ball')
19
+
20
+ def classify_image(img, top_k=5):
21
+ pred_class, pred_idx, probs = learn.predict(img)
22
+ categories = learn.dls.vocab
23
+ sorted_probs_indices = probs.argsort(descending=True)
24
+ top_categories = [categories[i] for i in sorted_probs_indices[:top_k]]
25
+ top_probs = probs[sorted_probs_indices[:top_k]]
26
+ return dict(zip(top_categories, map(float, top_probs)))
27
+
28
+ # %% ../app.ipynb 4
29
+ image = gr.components.Image(shape=(192,192))
30
+ label = gr.components.Label()
31
+ #examples = ['basketball.png', 'golf_ball.jpg', 'rugby_ball.jpg', 'soccer_ball.jpg']
32
+
33
+ intf = gr.Interface(fn=classify_image, inputs=image, outputs=label)
34
+ intf.launch(inline=False)
data_loader.ipynb ADDED
File without changes
load_data.ipynb ADDED
The diff for this file is too large to render. See raw diff
 
model.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1de42c0ea0bbe72b6a61d630f379d239ac3a915b353a0dbff4534e0f7dd0d680
3
+ size 244375721