{
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>COUNTRY_KEY</th>\n",
       "      <th>ITEM_DESC</th>\n",
       "      <th>BARCODE</th>\n",
       "      <th>BEM_CLASS_KEY</th>\n",
       "      <th>BEM_CLASS_DESC_FR</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>FRA</td>\n",
       "      <td>150ML SECHE VERNIS VITRY</td>\n",
       "      <td>3538892526034</td>\n",
       "      <td>1964</td>\n",
       "      <td>DIVERS MAQUILLAGE</td>\n",
       "      <td>maquillage des ongles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>FRA</td>\n",
       "      <td>15COM.SOMMEIL TRIPLE ACTI.PURE</td>\n",
       "      <td>3701056803443</td>\n",
       "      <td>1964</td>\n",
       "      <td>DIVERS MAQUILLAGE</td>\n",
       "      <td>maquillage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>FRA</td>\n",
       "      <td>2 EPONG.DEMAQUILLER N°42 SANOD</td>\n",
       "      <td>3701509580426</td>\n",
       "      <td>1964</td>\n",
       "      <td>DIVERS MAQUILLAGE</td>\n",
       "      <td>autres produits maquillage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>FRA</td>\n",
       "      <td>200ML DIFF PFUM FIGUE CDP</td>\n",
       "      <td>3551780164033</td>\n",
       "      <td>1964</td>\n",
       "      <td>DIVERS MAQUILLAGE</td>\n",
       "      <td>maquillage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>FRA</td>\n",
       "      <td>2PCS EPONGE DEMAQUILLANTE</td>\n",
       "      <td>3276558220253</td>\n",
       "      <td>1964</td>\n",
       "      <td>DIVERS MAQUILLAGE</td>\n",
       "      <td>autres produits maquillage</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 COUNTRY_KEY                       ITEM_DESC        BARCODE  \\\n",
       "0           0         FRA        150ML SECHE VERNIS VITRY  3538892526034   \n",
       "1           1         FRA  15COM.SOMMEIL TRIPLE ACTI.PURE  3701056803443   \n",
       "2           2         FRA  2 EPONG.DEMAQUILLER N°42 SANOD  3701509580426   \n",
       "3           3         FRA       200ML DIFF PFUM FIGUE CDP  3551780164033   \n",
       "4           4         FRA       2PCS EPONGE DEMAQUILLANTE  3276558220253   \n",
       "\n",
       "   BEM_CLASS_KEY  BEM_CLASS_DESC_FR                  prediction  \n",
       "0           1964  DIVERS MAQUILLAGE       maquillage des ongles  \n",
       "1           1964  DIVERS MAQUILLAGE                  maquillage  \n",
       "2           1964  DIVERS MAQUILLAGE  autres produits maquillage  \n",
       "3           1964  DIVERS MAQUILLAGE                  maquillage  \n",
       "4           1964  DIVERS MAQUILLAGE  autres produits maquillage  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"dataset/data_maquillage.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from traning_zone.classe_prediction.prediction_classe import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\coulibab\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\spacy\\util.py:887: UserWarning: [W095] Model 'fr_pipeline' (0.0.0) was trained with spaCy v3.6 and may not be 100% compatible with the current version (3.5.3). If you see errors or degraded performance, download a newer compatible model or retrain your custom model with the current spaCy version. For more details and available updates, run: python -m spacy validate\n",
      "  warnings.warn(warn_msg)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_desc</th>\n",
       "      <th>classe</th>\n",
       "      <th>hyper classe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KID KITTY FEMME BETY NAILMATI</td>\n",
       "      <td>douches</td>\n",
       "      <td>{'Confiserie': 0.10790520906448364, 'Soins Che...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       item_desc   classe  \\\n",
       "0  KID KITTY FEMME BETY NAILMATI  douches   \n",
       "\n",
       "                                        hyper classe  \n",
       "0  {'Confiserie': 0.10790520906448364, 'Soins Che...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X = df.ITEM_DESC\n",
    "X = pd.DataFrame({\"X\" : [\"KID KITTY FEMME BETY NAILMATI\"]})\n",
    "X = X.X\n",
    "pred = PredictionV(X)\n",
    "data = pred.prediction(\"spacy_lm\")\n",
    "data"
   ]
  }
 ],
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