{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai import *\n",
    "from fastbook import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!kaggle datasets download -d gpiosenka/musical-instruments-image-classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!ls -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!unzip -d images musical-instruments-image-classification.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = Path('images')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path.absolute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision.all import *\n",
    "from fastai.vision.widgets import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_path = Path('instruments.csv')\n",
    "csv_path.absolute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dls = ImageDataLoaders.from_csv(path=path,csv_fname='instruments.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner = vision_learner(dls=dls,arch=resnet18,metrics=error_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner.fine_tune(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "interp = ClassificationInterpretation.from_learner(learner)\n",
    "interp.plot_confusion_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "interp.plot_top_losses(10,nrows=5, figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaner =  ImageClassifierCleaner(learner)\n",
    "cleaner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx in cleaner.delete(): cleaner.fns[idx].unlink()\n",
    "for idx,cat in cleaner.change(): shutil.move(str(cleaner.fns[idx]),path/changed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "uploader=SimpleNamespace(data=['images/6 test samples/1.jpg'])\n",
    "img = (PILImage.create(uploader.data[0])).to_thumb(224)\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner.predict(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner.export('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 46032\n",
      "-rwxrwxrwx 1 tux tux      243 Jun 22 21:11 README.md\n",
      "-rwxrwxrwx 1 tux tux     3162 Jun 29 16:56 app.ipynb\n",
      "-rwxrwxrwx 1 tux tux      970 Jun 22 21:45 app.py\n",
      "-rwxrwxrwx 1 tux tux    37139 Jun 22 21:35 banjo.jpg\n",
      "-rwxrwxrwx 1 tux tux 47081297 Jun 22 21:08 model.pkl\n",
      "-rwxrwxrwx 1 tux tux        6 Jun 22 23:35 requirements.txt\n",
      "-rwxrwxrwx 1 tux tux     3774 Jun 29 16:56 train.ipynb\n"
     ]
    }
   ],
   "source": [
    "!ls -l"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}