The_Big_Cats_Classifier_Inference_Setup.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook contains only the inference setup for deployment, this jupyter notebook will served as a webserver using Voila."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision.all import *\n",
    "from fastai.vision.widgets import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = Path()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn_inf  = load_learner(path/'big_cat_classifier.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample inference test on an image.\n",
    "learn_inf.predict('cheetah_2.jpeg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Notebook App for the model"
   ]
  },
  {
   "source": [
    "btn_upload = widgets.FileUpload()"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "img = PILImage.create(btn_upload.data[-1])"
   ]
  },
  {
   "source": [
    "# Widgets for displaying the image with the predicted labels.\n",
    "out_pl = widgets.Output()\n",
    "lbl_pred = widgets.Label()"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pred, pred_idx, probs = learn_inf.predict(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d00ec51bd3da40e5878a98812622c465",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Guess the Big Cat !', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "btn_run = widgets.Button(description='Guess the Big Cat !')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def on_click_classify(change):\n",
    "    img = PILImage.create(btn_upload.data[-1])\n",
    "    out_pl.clear_output()\n",
    "    with out_pl: display(img.to_thumb(224,224))\n",
    "    pred,pred_idx,probs = learn_inf.predict(img)\n",
    "    lbl_pred.value = f\"Prediction is a {pred} with a Probability of {probs[pred_idx] * 100:.02f} %\"\n",
    "    display(VBox([lbl_pred]))\n",
    "\n",
    "btn_run.on_click(on_click_classify)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "caution_label = widgets.Label(\"Note : Probability greater than 90% indicates a confident guess \\\n",
    "                              whereas a lower probability indicates the model is not very sure of the guess. :)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61720844ea374590bf6c7046119c61f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Label(value='Upload the Big Cat Image here!'), FileUpload(value={'cheetah.jpeg': {'metadata': {…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "VBox([widgets.Label('Upload the Big Cat Image here!'), \n",
    "      btn_upload, btn_run, out_pl, lbl_pred])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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