diff --git a/Food_classification_refined.ipynb b/Food_classification_refined.ipynb
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+++ b/Food_classification_refined.ipynb
@@ -0,0 +1,6086 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "l7sfIHbf8L6Z",
+        "outputId": "1a6352b7-ba7a-45ac-cf7f-2a0344a9abac"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
+            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
+            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2023.7.22)\n",
+            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.1)\n",
+            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n",
+            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.6)\n",
+            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.0.0)\n",
+            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
+            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.0)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4)\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install kaggle"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "files.upload()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 91
+        },
+        "id": "IIrMuseu8VIA",
+        "outputId": "ee5d9a55-7c57-4580-dba2-7e70c407a2e1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-41a86aa2-c8a1-43ec-a4df-a175c60c98e4\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-41a86aa2-c8a1-43ec-a4df-a175c60c98e4\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving kaggle.json to kaggle.json\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'kaggle.json': b'{\"username\":\"sudeeshmuthum\",\"key\":\"1eac2ce73f26d9866670a76863a6a79f\"}'}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Move the API token to the correct location\n",
+        "!mkdir -p ~/.kaggle\n",
+        "!mv kaggle.json ~/.kaggle/\n",
+        "\n",
+        "# Set permissions for the API token\n",
+        "!chmod 600 ~/.kaggle/kaggle.json"
+      ],
+      "metadata": {
+        "id": "-qjBG84d8XtW"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!kaggle datasets download -d trolukovich/food11-image-dataset"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xs4aUfM78ZSX",
+        "outputId": "9558100b-b1e2-4b5a-8c0f-406724f7f753"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading food11-image-dataset.zip to /content\n",
+            "100% 1.08G/1.08G [00:13<00:00, 86.1MB/s]\n",
+            "100% 1.08G/1.08G [00:13<00:00, 84.8MB/s]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!unzip food11-image-dataset.zip"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "R0j0S8zD8bZR",
+        "outputId": "b57c4ebb-2712-468c-a2a4-d2a3210fb922"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "\n",
+        "%matplotlib inline\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import shutil\n",
+        "import random\n",
+        "import cv2\n",
+        "from glob import glob\n",
+        "import seaborn as sns\n",
+        "from PIL import Image\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "import itertools\n",
+        "from keras.utils import to_categorical\n",
+        "from keras.models import Sequential, Model\n",
+        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\n",
+        "from keras import backend as K\n",
+        "from keras.layers import BatchNormalization\n",
+        "from keras.optimizers import Adam, RMSprop\n",
+        "from keras.preprocessing.image import ImageDataGenerator\n",
+        "from keras.callbacks import ReduceLROnPlateau\n",
+        "import tensorflow_hub as hub\n",
+        "import tensorflow as tf\n",
+        "import pathlib"
+      ],
+      "metadata": {
+        "id": "alqcq_aL8j3L"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = tf.keras.applications.MobileNetV2(\n",
+        "    input_shape=(224,224,3),\n",
+        "    include_top=False,\n",
+        "    weights=\"imagenet\",\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JgJcmuef8wEa",
+        "outputId": "455bb316-92e1-4950-8a4c-438ebbb1f7f5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
+            "9406464/9406464 [==============================] - 0s 0us/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.trainable = False"
+      ],
+      "metadata": {
+        "id": "HAOfCos98-Yx"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = tf.keras.layers.GlobalAveragePooling2D()(model.output)"
+      ],
+      "metadata": {
+        "id": "8d_wazCW9Bp3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "output = tf.keras.layers.Dense(11, activation='softmax')(x)"
+      ],
+      "metadata": {
+        "id": "QDNQlw759DzF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = tf.keras.Model(inputs=model.input, outputs=output)"
+      ],
+      "metadata": {
+        "id": "NJ59UZRa9Fjl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
+      ],
+      "metadata": {
+        "id": "gSw7lDil9SO-"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "datagen = ImageDataGenerator(\n",
+        "rescale=1./255,\n",
+        "rotation_range=20,\n",
+        "width_shift_range=0.2,\n",
+        "height_shift_range=0.2,\n",
+        "horizontal_flip=False,\n",
+        "fill_mode='nearest'\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "o0wxmCQV9XDl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_generator = datagen.flow_from_directory(\n",
+        "'/content/training',\n",
+        "target_size=(224, 224),\n",
+        "batch_size=32,\n",
+        "class_mode='categorical',\n",
+        "shuffle=True\n",
+        ")\n",
+        "\n",
+        "# Create a validation generator from the validation folder\n",
+        "validation_generator = datagen.flow_from_directory(\n",
+        "'/content/validation',\n",
+        "target_size=(224, 224),\n",
+        "batch_size=32,\n",
+        "class_mode='categorical',\n",
+        "shuffle=False\n",
+        ")\n",
+        "\n",
+        "# Create a evaluation generator from the evaluation folder\n",
+        "evaluation_generator = datagen.flow_from_directory(\n",
+        "'/content/evaluation',\n",
+        "target_size=(224, 224),\n",
+        "batch_size=32,\n",
+        "class_mode='categorical',\n",
+        "shuffle=False\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "japPCACd9alb",
+        "outputId": "e2451857-18f0-4b80-e1b7-e710e50aa238"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 9866 images belonging to 11 classes.\n",
+            "Found 3430 images belonging to 11 classes.\n",
+            "Found 3347 images belonging to 11 classes.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "num_classes = 11\n",
+        "class_names = ['Bread', 'Dairy product', 'Dessert', 'Egg', 'Fried food', 'Meat', 'Noodles-Pasta', 'Rice', 'Seafood', 'Soup', 'Vegetable-Fruit']\n",
+        "total = 0\n",
+        "data_dir_train = pathlib.Path(\"/content/training\")\n",
+        "all_count = []\n",
+        "class_name = []\n",
+        "count = []\n",
+        "for i in range(num_classes):\n",
+        "  count.append(len(list(data_dir_train.glob(class_names[i]+'/*.jpg'))))\n",
+        "  total += count[i]\n",
+        "print(\"total training image count = {} \\n\".format(total))\n",
+        "print(\"-------------------------------------\")\n",
+        "for i in range(num_classes):\n",
+        "  countnew = len(list(data_dir_train.glob(class_names[i]+'/*.jpg')))\n",
+        "  print(\"Class name = \",class_names[i])\n",
+        "  print(\"count      = \",countnew)\n",
+        "  print(\"proportion = \",countnew/total)\n",
+        "  print(\"-------------------------------------\")\n",
+        "  all_count.append(countnew)\n",
+        "  class_name.append(class_names[i])\n",
+        "\n",
+        "temp_df = pd.DataFrame(list(zip(all_count, class_name)), columns = ['count', 'class_name'])\n",
+        "sns.barplot(data=temp_df, y=\"count\", x=\"class_name\")\n",
+        "plt.xticks(rotation=90)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "gjotS6ELxsMf",
+        "outputId": "15288e3c-7186-4920-9a9a-3b003cec6229"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "total training image count = 9866 \n",
+            "\n",
+            "-------------------------------------\n",
+            "Class name =  Bread\n",
+            "count      =  994\n",
+            "proportion =  0.10075005067909994\n",
+            "-------------------------------------\n",
+            "Class name =  Dairy product\n",
+            "count      =  429\n",
+            "proportion =  0.0434826677478208\n",
+            "-------------------------------------\n",
+            "Class name =  Dessert\n",
+            "count      =  1500\n",
+            "proportion =  0.15203729981755523\n",
+            "-------------------------------------\n",
+            "Class name =  Egg\n",
+            "count      =  986\n",
+            "proportion =  0.09993918508007298\n",
+            "-------------------------------------\n",
+            "Class name =  Fried food\n",
+            "count      =  848\n",
+            "proportion =  0.0859517534968579\n",
+            "-------------------------------------\n",
+            "Class name =  Meat\n",
+            "count      =  1325\n",
+            "proportion =  0.13429961483884045\n",
+            "-------------------------------------\n",
+            "Class name =  Noodles-Pasta\n",
+            "count      =  440\n",
+            "proportion =  0.04459760794648287\n",
+            "-------------------------------------\n",
+            "Class name =  Rice\n",
+            "count      =  280\n",
+            "proportion =  0.028380295965943644\n",
+            "-------------------------------------\n",
+            "Class name =  Seafood\n",
+            "count      =  855\n",
+            "proportion =  0.08666126089600648\n",
+            "-------------------------------------\n",
+            "Class name =  Soup\n",
+            "count      =  1500\n",
+            "proportion =  0.15203729981755523\n",
+            "-------------------------------------\n",
+            "Class name =  Vegetable-Fruit\n",
+            "count      =  709\n",
+            "proportion =  0.07186296371376444\n",
+            "-------------------------------------\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "count"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vEfqFp3e0msl",
+        "outputId": "7b9ff340-9354-479f-84cc-731696e47cd9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[994, 429, 1500, 986, 848, 1325, 440, 280, 855, 1500, 709]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install Augmentor\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mrWEMq7-xu_q",
+        "outputId": "ec9a1820-cebd-4153-a25e-cf4f21d02b65"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting Augmentor\n",
+            "  Downloading Augmentor-0.2.12-py2.py3-none-any.whl (38 kB)\n",
+            "Requirement already satisfied: Pillow>=5.2.0 in /usr/local/lib/python3.10/dist-packages (from Augmentor) (9.4.0)\n",
+            "Requirement already satisfied: tqdm>=4.9.0 in /usr/local/lib/python3.10/dist-packages (from Augmentor) (4.66.1)\n",
+            "Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from Augmentor) (1.23.5)\n",
+            "Installing collected packages: Augmentor\n",
+            "Successfully installed Augmentor-0.2.12\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import Augmentor\n",
+        "\n",
+        "path_to_training_dataset = '/content/training'\n",
+        "output_directory = '/content/augmented_data/'\n",
+        "os.makedirs(output_directory, exist_ok=True)\n",
+        "i =0\n",
+        "for class_name in class_names:\n",
+        "    class_path = os.path.join(path_to_training_dataset, class_name)\n",
+        "    p = Augmentor.Pipeline(class_path, output_directory=os.path.join(output_directory, class_name))\n",
+        "    p.rotate(probability=0.7, max_left_rotation=10, max_right_rotation=10)\n",
+        "    p.sample(2000)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dgKsEYthxw4k",
+        "outputId": "4c1ce28b-404a-4be0-b185-1c755059ba95"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initialised with 994 image(s) found.\n",
+            "Output directory set to /content/augmented_data/Bread."
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Processing <PIL.Image.Image image mode=RGB size=512x289 at 0x7E1A7C8027A0>: 100%|██████████| 2000/2000 [01:05<00:00, 30.76 Samples/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initialised with 429 image(s) found.\n",
+            "Output directory set to /content/augmented_data/Dairy product."
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Processing <PIL.Image.Image image mode=RGB size=512x512 at 0x7E19D433B010>: 100%|██████████| 2000/2000 [02:08<00:00, 15.51 Samples/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initialised with 1500 image(s) found.\n",
+            "Output directory set to /content/augmented_data/Dessert."
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Processing <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=382x512 at 0x7E19D5D1BF70>: 100%|██████████| 2000/2000 [01:01<00:00, 32.36 Samples/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initialised with 986 image(s) found.\n",
+            "Output directory set to /content/augmented_data/Egg."
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Processing <PIL.Image.Image image mode=RGB size=512x512 at 0x7E19D6D23730>: 100%|██████████| 2000/2000 [01:02<00:00, 32.23 Samples/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initialised with 848 image(s) found.\n",
+            "Output directory set to /content/augmented_data/Fried food."
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Processing <PIL.Image.Image image mode=RGB size=512x341 at 0x7E19D6D271C0>: 100%|██████████| 2000/2000 [00:59<00:00, 33.68 Samples/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initialised with 1325 image(s) found.\n",
+            "Output directory set to /content/augmented_data/Meat."
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Processing <PIL.Image.Image image mode=RGB size=512x512 at 0x7E19D6D24670>: 100%|██████████| 2000/2000 [01:02<00:00, 31.78 Samples/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initialised with 440 image(s) found.\n",
+            "Output directory set to /content/augmented_data/Noodles-Pasta."
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Processing <PIL.Image.Image image mode=RGB size=512x512 at 0x7E19D6E961D0>: 100%|██████████| 2000/2000 [01:01<00:00, 32.39 Samples/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initialised with 280 image(s) found.\n",
+            "Output directory set to /content/augmented_data/Rice."
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Processing <PIL.Image.Image image mode=RGB size=382x512 at 0x7E1A8BD4B910>: 100%|██████████| 2000/2000 [00:55<00:00, 35.83 Samples/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initialised with 855 image(s) found.\n",
+            "Output directory set to /content/augmented_data/Seafood."
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Processing <PIL.Image.Image image mode=RGB size=382x512 at 0x7E19D5CD6050>: 100%|██████████| 2000/2000 [01:05<00:00, 30.71 Samples/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initialised with 1500 image(s) found.\n",
+            "Output directory set to /content/augmented_data/Soup."
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Processing <PIL.Image.Image image mode=RGB size=512x512 at 0x7E1A8BD4B910>: 100%|██████████| 2000/2000 [00:59<00:00, 33.44 Samples/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Initialised with 709 image(s) found.\n",
+            "Output directory set to /content/augmented_data/Vegetable-Fruit."
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Processing <PIL.Image.Image image mode=RGB size=1024x768 at 0x7E19C8777B80>: 100%|██████████| 2000/2000 [02:32<00:00, 13.15 Samples/s]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#folder_to_remove = '/content/augmented_data'\n",
+        "#!rm -r \"$folder_to_remove\""
+      ],
+      "metadata": {
+        "id": "GdK_sGPX0Hju"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "\n",
+        "directory_path = '/content/augmented_data'\n",
+        "\n",
+        "image_extensions = ['.jpg', '.jpeg', '.png']\n",
+        "total_images = 0\n",
+        "for dirpath, dirnames, filenames in os.walk(directory_path):\n",
+        "    for filename in filenames:\n",
+        "        if any(filename.endswith(ext) for ext in image_extensions):\n",
+        "            total_images += 1\n",
+        "print(f'Number of images in the directory: {total_images}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8j99Z-9AyP3_",
+        "outputId": "783f5890-2156-4a9a-f01b-fbe9e9d5fa9a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of images in the directory: 22000\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "datagen = ImageDataGenerator(\n",
+        "rescale=1./255,\n",
+        "rotation_range=20,\n",
+        "width_shift_range=0.2,\n",
+        "height_shift_range=0.2,\n",
+        "horizontal_flip=False,\n",
+        "fill_mode='nearest',\n",
+        "validation_split = 0.2\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "-udmhcHeLkHK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_generator = datagen.flow_from_directory(\n",
+        "'/content/augmented_data',\n",
+        "target_size=(224, 224),\n",
+        "batch_size=32,\n",
+        "class_mode='categorical',\n",
+        "subset = 'training',\n",
+        "shuffle=True\n",
+        ")\n",
+        "\n",
+        "validation_generator = datagen.flow_from_directory(\n",
+        "'/content/augmented_data',\n",
+        "target_size=(224, 224),\n",
+        "batch_size=32,\n",
+        "class_mode='categorical',\n",
+        "subset = 'validation',\n",
+        "shuffle=False\n",
+        ")\n",
+        "\n",
+        "evaluation_generator = datagen.flow_from_directory(\n",
+        "'/content/evaluation',\n",
+        "target_size=(224, 224),\n",
+        "batch_size=32,\n",
+        "class_mode='categorical',\n",
+        "shuffle=False\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GalhvXA8HlBf",
+        "outputId": "61db9d72-abba-45e0-a83c-caea4e225ef8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 17600 images belonging to 11 classes.\n",
+            "Found 4400 images belonging to 11 classes.\n",
+            "Found 3347 images belonging to 11 classes.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = model.fit(train_generator, epochs=5, validation_data=validation_generator)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2g8PryxeL5hb",
+        "outputId": "ad14ace1-60c6-4d14-a9f0-377cd41718f5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/5\n",
+            "550/550 [==============================] - 1188s 2s/step - loss: 0.7957 - accuracy: 0.7398 - val_loss: 0.6686 - val_accuracy: 0.7773\n",
+            "Epoch 2/5\n",
+            "550/550 [==============================] - 1203s 2s/step - loss: 0.5080 - accuracy: 0.8330 - val_loss: 0.6335 - val_accuracy: 0.7909\n",
+            "Epoch 3/5\n",
+            "550/550 [==============================] - 1201s 2s/step - loss: 0.4420 - accuracy: 0.8553 - val_loss: 0.6094 - val_accuracy: 0.7941\n",
+            "Epoch 4/5\n",
+            "550/550 [==============================] - 1180s 2s/step - loss: 0.4034 - accuracy: 0.8672 - val_loss: 0.6421 - val_accuracy: 0.7911\n",
+            "Epoch 5/5\n",
+            "550/550 [==============================] - 1148s 2s/step - loss: 0.3693 - accuracy: 0.8767 - val_loss: 0.6152 - val_accuracy: 0.8011\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "prediction = model.predict(evaluation_generator)"
+      ],
+      "metadata": {
+        "id": "2-X7oUL-MxHV",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ff4fe402-4373-47f2-9649-71c43617c769"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "105/105 [==============================] - 171s 2s/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_classes = np.argmax(prediction, axis=1)"
+      ],
+      "metadata": {
+        "id": "R5W2zugPN3D5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.save('Food_classification_model_smol.h5')"
+      ],
+      "metadata": {
+        "id": "tRWcPcMsxMbX",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "90528a25-ebf3-403d-ec1b-1fb75037db85"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
+            "  saving_api.save_model(\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file