{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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   "source": [
    "from fastai.vision.all import *\n",
    "import gradio as gr\n",
    "import pathlib\n",
    "pathlib.PosixPath = pathlib.WindowsPath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_x(r): return path/'train'/r['fname']\n",
    "def get_y(r): return r['labels'].split(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = load_learner(\"export.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = learn.dls.vocab\n",
    "def infer(img):\n",
    "    img = PILImage.create(img)\n",
    "    _pred, _pred_w_idx, probs = learn.predict(img)\n",
    "    # gradio doesn't support tensors, so converting to float\n",
    "    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}\n",
    "    return labels_probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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       "{'aeroplane': 0.00040097636519931257,\n",
       " 'bicycle': 0.003079020883888006,\n",
       " 'bird': 0.007594174239784479,\n",
       " 'boat': 0.0019870696123689413,\n",
       " 'bottle': 0.0077136121690273285,\n",
       " 'bus': 0.0002228342927992344,\n",
       " 'car': 0.002796210814267397,\n",
       " 'cat': 0.011337166652083397,\n",
       " 'chair': 0.027877626940608025,\n",
       " 'cow': 0.0004105104599148035,\n",
       " 'diningtable': 0.0014102141140028834,\n",
       " 'dog': 0.9442074298858643,\n",
       " 'horse': 0.00014107774768490344,\n",
       " 'motorbike': 0.0004384420462884009,\n",
       " 'person': 0.9861327409744263,\n",
       " 'pottedplant': 0.002472719643265009,\n",
       " 'sheep': 0.015349175781011581,\n",
       " 'sofa': 0.008290301077067852,\n",
       " 'train': 0.0043441057205200195,\n",
       " 'tvmonitor': 0.007222974672913551}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infer(\"000001.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Loc\\anaconda3\\lib\\site-packages\\gradio\\inputs.py:256: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
      "  warnings.warn(\n",
      "c:\\Users\\Loc\\anaconda3\\lib\\site-packages\\gradio\\deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
      "  warnings.warn(value)\n",
      "c:\\Users\\Loc\\anaconda3\\lib\\site-packages\\gradio\\outputs.py:196: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
      "  warnings.warn(\n",
      "c:\\Users\\Loc\\anaconda3\\lib\\site-packages\\gradio\\deprecation.py:40: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
      "  warnings.warn(value)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7861\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<gradio.routes.App at 0x21cd0e54970>, 'http://127.0.0.1:7861/', None)"
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   "source": [
    "# get the inputs\n",
    "inputs = gr.inputs.Image(shape=(192, 192))\n",
    "\n",
    "# the app outputs two segmented images\n",
    "outputs = gr.outputs.Label(num_top_classes=3)\n",
    "\n",
    "EXAMPLES_PATH = Path('./examples')\n",
    "examples = [f'{EXAMPLES_PATH}/{f.name}' for f in EXAMPLES_PATH.iterdir()]\n",
    "\n",
    "# it's good practice to pass examples, description and a title to guide users\n",
    "title = 'Multiple Object Detector'\n",
    "description = 'This app detects objects that appear in the image'\n",
    "article = \"Author: <a href=\\\"https://huggingface.co/archietram\\\">Archie Tram</a>. \"\n",
    "intf = gr.Interface(fn=infer, inputs=inputs, outputs=outputs, examples=examples, title=title, description=description, article=article)\n",
    "intf.launch(inline=False)"
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