4. Classification_submission_preparing.ipynb

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   "metadata": {},
   "source": [
    "# Classification model loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6f55641c",
   "metadata": {},
   "outputs": [
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       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x222eb2fec88>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.models import model_from_json\n",
    "import numpy as np\n",
    "\n",
    "with open(\"./_models/emotion_classification/model.json\", 'r') as json_file:\n",
    "    loaded_model_json = json_file.read()\n",
    "model = model_from_json(loaded_model_json)\n",
    "model.load_weights(\"./_models/emotion_classification/saved_weights\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1f099dd",
   "metadata": {},
   "source": [
    "# Prediction for each file in test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2aab0f21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, glob, pandas as pd\n",
    "\n",
    "filenames, emotions = [], []\n",
    "\n",
    "from PIL import Image\n",
    "for filename in glob.glob('./_datasets/test_dataset/*.jpg'):\n",
    "  with open(filename, 'r') as f:\n",
    "    img = Image.open(filename)\n",
    "    img = img.resize((224,224))\n",
    "    img = np.array(img)\n",
    "    img = np.expand_dims(img, 0)\n",
    "    emotion = model.predict(img)\n",
    "    num_of_emotion = np.argmax(emotion)\n",
    "    list_emotions = ['anger', 'contempt',\n",
    "                    'disgust', 'fear',\n",
    "                    'happy', 'neutral',\n",
    "                    'sad', 'surprise', 'uncertain']\n",
    "    emotion = list_emotions[num_of_emotion]\n",
    "    filenames.append(filename)\n",
    "    emotions.append(emotion)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92289c4a",
   "metadata": {},
   "source": [
    "# Create dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c642a623",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\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>image_path</th>\n",
       "      <th>emotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.jpg</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.jpg</td>\n",
       "      <td>surprise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.jpg</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.jpg</td>\n",
       "      <td>fear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000.jpg</td>\n",
       "      <td>happy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  image_path    emotion\n",
       "0      0.jpg  uncertain\n",
       "1      1.jpg   surprise\n",
       "2     10.jpg    neutral\n",
       "3    100.jpg       fear\n",
       "4   1000.jpg      happy"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'image_path': filenames, 'emotion': emotions}\n",
    "df = pd.DataFrame(data)\n",
    "df['image_path'] = df['image_path'].apply(lambda x: x[25:])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a40b10",
   "metadata": {},
   "source": [
    "# Save submission file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "44f04510",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('class_submission.csv', index=False)"
   ]
  }
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