{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Buah Alpukat',\n",
       " 'Buah Apel',\n",
       " 'Buah Belimbing',\n",
       " 'Buah Naga',\n",
       " 'Buah Lemon',\n",
       " 'Buah Nanas',\n",
       " 'Buah Pir',\n",
       " 'Buah Pisang',\n",
       " 'Buah Salak',\n",
       " 'Buah Stroberi']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fastai.vision.all import *\n",
    "\n",
    "learn = load_learner('model.pkl')\n",
    "\n",
    "\n",
    "def format_name(name):\n",
    "    if name != 'buah naga':\n",
    "        return f'Buah {name.title()}'\n",
    "    \n",
    "    return name.title()\n",
    "\n",
    "\n",
    "names = list(map(format_name, learn.dls.vocab))\n",
    "names\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import firebase_admin\n",
    "from firebase_admin import credentials\n",
    "from firebase_admin import firestore\n",
    "\n",
    "cred = credentials.Certificate('firebase_key.json')\n",
    "app = firebase_admin.initialize_app(cred)\n",
    "db = firestore.client()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_ref = db.collection(u'preds').add({\n",
    "    u'image': u'Lovelace',\n",
    "    u'prediction': 1815,\n",
    "    u'time_added': firestore.SERVER_TIMESTAMP\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_ref = db.collection(u'preds')\n",
    "docs = preds_ref.stream()\n",
    "\n",
    "for doc in docs:\n",
    "    data = base64.b64decode(doc.to_dict()['image'])\n",
    "    buff = BytesIO(data)\n",
    "    img = Image.open(buff)\n",
    "    img.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"900\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<gradio.routes.App at 0x7f3279fb6af0>, 'http://127.0.0.1:7860/', None)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "from fastai.vision.all import *\n",
    "import firebase_admin\n",
    "from firebase_admin import credentials\n",
    "from firebase_admin import firestore\n",
    "import base64\n",
    "from io import BytesIO\n",
    "\n",
    "cred = credentials.Certificate('firebase_key.json')\n",
    "app = firebase_admin.initialize_app(cred)\n",
    "db = firestore.client()\n",
    "\n",
    "learn = load_learner('model.pkl')\n",
    "names = list(map(lambda name: name.title(), learn.dls.vocab))\n",
    "\n",
    "\n",
    "def classify(image):\n",
    "    buffered = BytesIO()\n",
    "    image.save(buffered, format=\"JPEG\")\n",
    "    img_str = base64.b64encode(buffered.getvalue())\n",
    "\n",
    "    pred, idx, probs = learn.predict(np.asarray(image))\n",
    "\n",
    "    db.collection(u'preds').add({\n",
    "        u'image': img_str,\n",
    "        u'prediction': pred.title(),\n",
    "        u'time_added': firestore.SERVER_TIMESTAMP\n",
    "    })\n",
    "\n",
    "    return dict(zip(names, map(float, probs)))\n",
    "\n",
    "\n",
    "with gr.Blocks() as demo:\n",
    "    with gr.Row():\n",
    "        with gr.Column():\n",
    "            image_input = gr.Image(label=\"Gambar\", shape=(200, 200), type='pil')\n",
    "            predict_btn = gr.Button(\"Klasifikasi\", variant='primary')\n",
    "        with gr.Column():\n",
    "            chart = gr.Label(label=\"Hasil\")\n",
    "\n",
    "    predict_btn.click(fn=classify, inputs=image_input,\n",
    "                      outputs=chart, api_name='classify_image')\n",
    "\n",
    "demo.launch()\n"
   ]
  }
 ],
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   "display_name": "Python 3.9.12 ('base')",
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