{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N3shQZoZPScM",
        "outputId": "63642e05-bd32-4fd9-f029-8f50148a1e8a"
      },
      "outputs": [],
      "source": [
        "!pip install -U sentence_transformers --q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rcBH0FzwVOk6",
        "outputId": "f5b4b762-9b30-4474-d1d0-7ba3ab68a2ef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip3 install --upgrade pip\u001b[0m\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "pip install datasets --q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-pDMu97XyVd",
        "outputId": "737160a3-2c34-4293-a129-bb053cd91117"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting sentence-transformers\n",
            "  Using cached sentence_transformers-3.4.1-py3-none-any.whl.metadata (10 kB)\n",
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            "Collecting torch\n",
            "  Downloading torch-2.6.0-cp312-none-macosx_11_0_arm64.whl.metadata (28 kB)\n",
            "Collecting transformers<5.0.0,>=4.41.0 (from sentence-transformers)\n",
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            "  Downloading tokenizers-0.21.0-cp39-abi3-macosx_11_0_arm64.whl.metadata (6.7 kB)\n",
            "Collecting safetensors>=0.4.1 (from transformers<5.0.0,>=4.41.0->sentence-transformers)\n",
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            "Using cached sentence_transformers-3.4.1-py3-none-any.whl (275 kB)\n",
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            "\u001b[?25hInstalling collected packages: mpmath, sympy, safetensors, regex, networkx, torch, tokenizers, transformers, sentence-transformers\n",
            "Successfully installed mpmath-1.3.0 networkx-3.4.2 regex-2024.11.6 safetensors-0.5.2 sentence-transformers-3.4.1 sympy-1.13.1 tokenizers-0.21.0 torch-2.6.0 transformers-4.48.3\n",
            "\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip3 install --upgrade pip\u001b[0m\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "pip install sentence-transformers scikit-learn pandas torch\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "m-tmgXuldd3C"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "1Z0mgYZEgjC4"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "aBmXLbZ4cc1U"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "LXkXdIWgUcWI"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset, Dataset\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "AFkI23ySgtkV"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ehvh1BJZWa_1",
        "outputId": "212a5f82-885d-4e61-a73f-94dcf12a3a39"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ab0344420d964f64a16c911f17aae057",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/515 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9f86cbde053f4b9e91cff137a924082f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "train-00000-of-00001.parquet:   0%|          | 0.00/5.89M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2f6d00487331444299405cc97d4b18ea",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split:   0%|          | 0/61199 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     answer                                      system_prompt  \\\n",
            "0   neutral  You are a financial sentiment analysis expert....   \n",
            "1   neutral  You are a financial sentiment analysis expert....   \n",
            "2  negative  You are a financial sentiment analysis expert....   \n",
            "3  positive  You are a financial sentiment analysis expert....   \n",
            "4  positive  You are a financial sentiment analysis expert....   \n",
            "\n",
            "                                         user_prompt           task_type  \n",
            "0  According to Gran , the company has no plans t...  sentiment_analysis  \n",
            "1  Technopolis plans to develop in stages an area...  sentiment_analysis  \n",
            "2  The international electronic industry company ...  sentiment_analysis  \n",
            "3  With the new production plant the company woul...  sentiment_analysis  \n",
            "4  According to the company 's updated strategy f...  sentiment_analysis  \n"
          ]
        }
      ],
      "source": [
        "df = load_dataset(\"NickyNicky/Finance_sentiment_and_topic_classification_En\")\n",
        "\n",
        "# Converting 'train' split to a Pandas DataFrame\n",
        "df = pd.DataFrame(df['train'])\n",
        "\n",
        "\n",
        "print(df.head())\n",
        "\n",
        "\n",
        "df.to_csv(\"train_data.csv\", index=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wS-PmD5WWnYC",
        "outputId": "36732946-2bb0-4f58-f784-5619d77698b9"
      },
      "outputs": [
        {
          "data": {
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            ],
            "text/plain": [
              "     answer                                      system_prompt  \\\n",
              "0   neutral  You are a financial sentiment analysis expert....   \n",
              "1   neutral  You are a financial sentiment analysis expert....   \n",
              "2  negative  You are a financial sentiment analysis expert....   \n",
              "3  positive  You are a financial sentiment analysis expert....   \n",
              "4  positive  You are a financial sentiment analysis expert....   \n",
              "\n",
              "                                         user_prompt           task_type  \n",
              "0  According to Gran , the company has no plans t...  sentiment_analysis  \n",
              "1  Technopolis plans to develop in stages an area...  sentiment_analysis  \n",
              "2  The international electronic industry company ...  sentiment_analysis  \n",
              "3  With the new production plant the company woul...  sentiment_analysis  \n",
              "4  According to the company 's updated strategy f...  sentiment_analysis  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "cQ3tjGFTW5kE"
      },
      "outputs": [],
      "source": [
        "df.drop(['system_prompt', 'task_type'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Va5867ATXAxD",
        "outputId": "c258f546-d8af-4ba5-8228-3a07f2283baf"
      },
      "outputs": [
        {
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              "  <thead>\n",
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              "      <th>1</th>\n",
              "      <td>neutral</td>\n",
              "      <td>Technopolis plans to develop in stages an area...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>negative</td>\n",
              "      <td>The international electronic industry company ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>positive</td>\n",
              "      <td>With the new production plant the company woul...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>positive</td>\n",
              "      <td>According to the company 's updated strategy f...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61194</th>\n",
              "      <td>Treasuries | Corporate Debt</td>\n",
              "      <td>KfW credit line for Uniper could be raised to ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61195</th>\n",
              "      <td>Treasuries | Corporate Debt</td>\n",
              "      <td>KfW credit line for Uniper could be raised to ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61196</th>\n",
              "      <td>Treasuries | Corporate Debt</td>\n",
              "      <td>Russian  https://t.co/R0iPhyo5p7 sells 1 bln r...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61197</th>\n",
              "      <td>Treasuries | Corporate Debt</td>\n",
              "      <td>Global ESG bond issuance posts H1 dip as supra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61198</th>\n",
              "      <td>Treasuries | Corporate Debt</td>\n",
              "      <td>Brazil's Petrobras says it signed a $1.25 bill...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61199 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                            answer  \\\n",
              "0                          neutral   \n",
              "1                          neutral   \n",
              "2                         negative   \n",
              "3                         positive   \n",
              "4                         positive   \n",
              "...                            ...   \n",
              "61194  Treasuries | Corporate Debt   \n",
              "61195  Treasuries | Corporate Debt   \n",
              "61196  Treasuries | Corporate Debt   \n",
              "61197  Treasuries | Corporate Debt   \n",
              "61198  Treasuries | Corporate Debt   \n",
              "\n",
              "                                             user_prompt  \n",
              "0      According to Gran , the company has no plans t...  \n",
              "1      Technopolis plans to develop in stages an area...  \n",
              "2      The international electronic industry company ...  \n",
              "3      With the new production plant the company woul...  \n",
              "4      According to the company 's updated strategy f...  \n",
              "...                                                  ...  \n",
              "61194  KfW credit line for Uniper could be raised to ...  \n",
              "61195  KfW credit line for Uniper could be raised to ...  \n",
              "61196  Russian  https://t.co/R0iPhyo5p7 sells 1 bln r...  \n",
              "61197  Global ESG bond issuance posts H1 dip as supra...  \n",
              "61198  Brazil's Petrobras says it signed a $1.25 bill...  \n",
              "\n",
              "[61199 rows x 2 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2PtcHIfeM5t",
        "outputId": "2214b201-c68d-4112-d224-855bd7103213"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(39641, 2)\n"
          ]
        }
      ],
      "source": [
        "# only want to keep rows where 'answer' is 'neutral', 'positive', or 'negative'\n",
        "df_filtered = df[df[\"answer\"].isin([\"neutral\", \"positive\", \"negative\"])]\n",
        "\n",
        "# Showing the shape of the new DataFrame\n",
        "print(df_filtered.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OzmsDZ-tZuGA",
        "outputId": "cb743649-521b-45da-8c9a-182fff7584bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(5946, 2)\n"
          ]
        }
      ],
      "source": [
        "df_sampled = df_filtered.sample(frac=0.15, random_state=42)  # 15% sample\n",
        "print(df_sampled.shape)  # Checking new size\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "kkaxAfyQdcgZ",
        "outputId": "1fa73eb8-c4ea-4b0b-8288-7dcb3c517798"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='answer', ylabel='count'>"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.countplot(x=df_sampled[\"answer\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2DP6ekqfNbe",
        "outputId": "3aacbc50-8554-40eb-9cdb-c949c30d634e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "answer\n",
            "negative    1236\n",
            "neutral     1236\n",
            "positive    1236\n",
            "Name: count, dtype: int64\n",
            "(3708, 2)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/var/folders/xc/v1l81vkx6fjc9wpqc0tsnl400000gn/T/ipykernel_11468/1830774783.py:5: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
            "  df_balanced = df_sampled.groupby(\"answer\").apply(lambda x: x.sample(min_class_count, random_state=42)).reset_index(drop=True)\n"
          ]
        }
      ],
      "source": [
        "# Undersampling each class to match the class with the smallest number of samples\n",
        "min_class_count = df_sampled[\"answer\"].value_counts().min()\n",
        "\n",
        "# Sampling an equal number of rows from each class\n",
        "df_balanced = df_sampled.groupby(\"answer\").apply(lambda x: x.sample(min_class_count, random_state=42)).reset_index(drop=True)\n",
        "\n",
        "# Showing the new class distribution\n",
        "print(df_balanced[\"answer\"].value_counts())\n",
        "print(df_balanced.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "dJosNJACYDCc"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "593c0e8a5f6b4b9495ff422cc2382975",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "850ce1db88c64b81802f2a60f45801d4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8cfd7d7217a24485818919eebbca3cb2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6aa8a1f649ec471ebe07ee374e80de62",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cb49e7e953654af8af5fd5d22f78ce59",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/612 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "12bb5d1510b1422582f091a49fa617a0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b9632a0063044733bd70e443fce6caed",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5d345282cdd6400d8bc229280df8766e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0473d385d77c406a94075d701d4565e1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "643a5bf87437468e8a168e55687814d5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4560c43f64884ba9b236962626a1f784",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "1_Pooling%2Fconfig.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Load model\n",
        "from sentence_transformers import SentenceTransformer # import the SentenceTransformer class\n",
        "model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
        "\n",
        "# Converting text to embeddings\n",
        "X = model.encode(df_balanced[\"user_prompt\"].tolist(), convert_to_numpy=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mH4_pI6YZa3E",
        "outputId": "667d2b0f-a60a-4afd-e2aa-270ef9d5b8de"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Label Mapping: {'negative': 0, 'neutral': 1, 'positive': 2}\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Encode labels\n",
        "label_encoder = LabelEncoder()\n",
        "y = label_encoder.fit_transform(df_balanced[\"answer\"])\n",
        "\n",
        "# Saving the mapping\n",
        "label_mapping = dict(zip(label_encoder.classes_, label_encoder.transform(label_encoder.classes_)))\n",
        "print(\"Label Mapping:\", label_mapping)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "I5gpynBmZe1h"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "jCUXUqgRgdeJ",
        "outputId": "8e27311c-e799-4a89-f319-f7b6f4e07e75"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "clf.fit(X_train, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.66      0.52      0.58       277\n",
            "           1       0.62      0.80      0.70       237\n",
            "           2       0.55      0.52      0.54       228\n",
            "\n",
            "    accuracy                           0.61       742\n",
            "   macro avg       0.61      0.61      0.61       742\n",
            "weighted avg       0.61      0.61      0.60       742\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# Load model (already done)\n",
        "model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
        "\n",
        "# Converting text to embeddings\n",
        "X = model.encode(df_balanced[\"user_prompt\"].tolist(), convert_to_numpy=True)\n",
        "\n",
        "# Encode labels (already done)\n",
        "label_encoder = LabelEncoder()\n",
        "y = label_encoder.fit_transform(df_balanced[\"answer\"])\n",
        "\n",
        "# Train-test split (already done)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initialize and train RandomForestClassifier\n",
        "clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Print classification report to evaluate performance\n",
        "print(classification_report(y_test, y_pred))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best Parameters: {'max_depth': 20, 'min_samples_split': 5, 'n_estimators': 200}\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "param_grid = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'max_depth': [10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10]\n",
        "}\n",
        "\n",
        "grid_search = GridSearchCV(estimator=clf, param_grid=param_grid, cv=3, n_jobs=-1)\n",
        "grid_search.fit(X_train, y_train)\n",
        "print(\"Best Parameters:\", grid_search.best_params_)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Random Forest Hyperparameters:\n",
            "Number of Estimators (n_estimators): 200\n",
            "Max Depth (max_depth): 20\n",
            "Min Samples Split (min_samples_split): 5\n",
            "Min Samples Leaf (min_samples_leaf): 1\n",
            "Random State (random_state): 42\n",
            "Max Features (max_features): sqrt\n"
          ]
        }
      ],
      "source": [
        "# Print the hyperparameters of the trained model\n",
        "print(\"Random Forest Hyperparameters:\")\n",
        "print(f\"Number of Estimators (n_estimators): {clf.n_estimators}\")\n",
        "print(f\"Max Depth (max_depth): {clf.max_depth}\")\n",
        "print(f\"Min Samples Split (min_samples_split): {clf.min_samples_split}\")\n",
        "print(f\"Min Samples Leaf (min_samples_leaf): {clf.min_samples_leaf}\")\n",
        "print(f\"Random State (random_state): {clf.random_state}\")\n",
        "print(f\"Max Features (max_features): {clf.max_features}\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=20, min_samples_split=5, n_estimators=200,\n",
              "                       random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(max_depth=20, min_samples_split=5, n_estimators=200,\n",
              "                       random_state=42)</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier(max_depth=20, min_samples_split=5, n_estimators=200,\n",
              "                       random_state=42)"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Updated parameters based on your best values\n",
        "clf = RandomForestClassifier(\n",
        "    n_estimators=200,          # Set number of trees to 200\n",
        "    max_depth=20,              # Set max depth to 20\n",
        "    min_samples_split=5,       # Set min samples split to 5\n",
        "    random_state=42,           # Keep random state for reproducibility\n",
        "    max_features='sqrt',       # Default 'sqrt' for Random Forest (best practice)\n",
        "    min_samples_leaf=1         # Default value is 1, change if needed\n",
        ")\n",
        "\n",
        "# Train the model with the new parameters\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# You can now use clf for prediction and evaluation as usual\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.66      0.52      0.58       277\n",
            "           1       0.62      0.80      0.70       237\n",
            "           2       0.55      0.52      0.54       228\n",
            "\n",
            "    accuracy                           0.61       742\n",
            "   macro avg       0.61      0.61      0.61       742\n",
            "weighted avg       0.61      0.61      0.60       742\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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AHBwc0K1bN2WbkZERhg8fjuTkZJw4cUJl/06dOsHW1jbP/detWxdHjhxRefTs2fODr+nSpQuKFSumfJ7z+Xz7M2lqaqr8d3p6Op49e4Z69eoBQK7n/mOfjV27dkGhUGDChAmiuZIfWvbEwMBAOf9LoVAgISEBr1+/Rq1atdT6DOSwt7fH1atXMWjQILx48QIhISHo3r077OzsMGXKFAiCAAC4cuUKoqKi0L17dzx//hzPnj3Ds2fPkJKSgubNm+PkyZOi37WC+P14l4eHBzw9PZXP69atCwBo1qwZSpUqJWrP+RkmJCTg6NGj6Ny5M169eqWM//nz5/Dx8UFUVBQeP36scqwBAwao/CwaNWqE7OxsPHz4MF+xq/vdmpu3P4cvX77Es2fP4OXlhfv37+Ply5cA/vf9t2/fPmRlZeXaj6WlJVJSUnDkyJF8x/IhW7duhbu7OypUqKA818+ePUOzZs0AAMeOHVPZ38vLq0DnL5P+YJL4mVlYWAAAXr16laf9Hz58CKlUqnKlGwA4ODjA0tJS9IX69hd5jmLFiuHFixf5jFisS5cuaNCgAfr16wd7e3t07doVW7Zs+WDCmBNn+fLlRdvc3d2V/0N827vvJSfRUOe9+Pr6omjRoti8eTM2bNiA2rVri85lDoVCgTlz5sDNzQ0ymQw2NjawtbXFtWvXlP+DyIvixYurNdF85syZsLKywpUrVzB//nzY2dnl+bXvevjwIdzc3ESJkbu7u3L721xcXNTq38bGBt7e3iqPMmXKfPA1efk5JiQkYMSIEbC3t4epqSlsbW2VseV27j/W57179yCVSvP1P8Y1a9agSpUqMDExgbW1NWxtbbF//361PgNvc3R0xOLFixEbG4vIyEjMnz8ftra2mDBhAlasWAHgzbJMwJulqWxtbVUey5cvR0ZGhuj4BfH78a53+5TL5QCAkiVL5tqec6y7d+9CEASMHz9eFH/O1fc5F2VoKn51v1tzc/r0aXh7e8PMzAyWlpawtbXFzz//DOB/n0MvLy906tQJkydPho2NDdq1a4dVq1YhIyND2c/333+PcuXKoVWrVihRogT69OmDgwcP5juud0VFReHmzZuic12uXDkA4nOt7u85UQ7OSfzMLCws4OTkhBs3bqj1urwu+vq+KxZzKhb5OUZ2drbKc1NTU5w8eRLHjh3D/v37cfDgQWzevBnNmjXD4cOHC+yqyU95LzlkMhk6duyINWvW4P79+x9c/Hj69OkYP348+vTpgylTpsDKygpSqRQjR45Ua2mXt6sReXH58mXll/r169dVqoCapm6s+ZGXn2Pnzp1x5swZjBkzBtWqVYO5uTkUCgVatmyZ67kviM9GbtavX49evXqhffv2GDNmDOzs7GBgYIDg4GDcu3fvk/qWSCQoV64cypUrh9atW8PNzQ0bNmxAv379lO/x999/f++8x3fnDmviHLyvz48dKyf+0aNHw8fHJ9d93/3jrKDjr1ChAoA3v0Nvj6bk1b1799C8eXNUqFABs2fPRsmSJWFsbIwDBw5gzpw5yvcokUiwbds2nD17Fnv37sWhQ4fQp08fzJo1C2fPnoW5uTns7Oxw5coVHDp0CH/99Rf++usvrFq1Cj179sSaNWvy9f7eplAoULlyZcyePTvX7e8m9Z/j95y+TEwSteDrr7/G0qVLERYWpjK0kxtnZ2coFApERUUpq0EAEB8fj8TERDg7OxdYXMWKFUNiYqKoPbfhH6lUiubNm6N58+aYPXs2pk+fjnHjxuHYsWPw9vbO9X0AQGRkpGjb7du3YWNjAzMzs09/E7no3r07Vq5cCalUiq5du753v23btqFp06bK6k6OxMRE2NjYKJ8X5F0aUlJS0Lt3b3h4eKB+/fqYMWMGOnTogNq1a+erP2dnZ1y7dg0KhUKlmnj79m3ldl3z4sULhIaGYvLkyZgwYYKyPae6lh+urq5QKBS4deuWWhebbNu2DWXKlMGOHTtUfs4FvRZlmTJlUKxYMcTGxirjBd78EZnb709+fa47iuRUk42MjLQWf5s2bRAcHIz169fnK0ncu3cvMjIysGfPHpUq57tDtznq1auHevXqYdq0adi4cSP8/PywadMm9OvXD8CbZWvatGmDNm3aQKFQ4Pvvv8eSJUswfvz4945m5JWrqyuuXr2K5s2ba/WuMfTl43CzFvz4448wMzNDv379EB8fL9p+7949zJs3D8Cb4VLgzZVvb8v5C7J169YFFperqytevnyJa9euKdtiY2NFV1AnJCSIXpvzP+K3h1ze5ujoiGrVqmHNmjUqieiNGzdw+PBh5fvUhKZNm2LKlCn4448/4ODg8N79DAwMRFWMrVu3iuZS5SSzuSXU6ho7dixiYmKwZs0azJ49G6VLl4a/v/97z+PH+Pr6Ii4uTnmFKPBm/cMFCxbA3NwcXl5enxxzQcupKL177t/9zKujffv2kEqlCAoKElUiP1Spyi2W8PBwhIWF5SuO8PBw0TQKADh37hyeP3+unH5Rs2ZNuLq6YubMmbnehSVnbVN1mZmZFcjn9GPs7OzQpEkTLFmyRJn4vu1zxO/p6YmWLVti+fLlud5lJjMzE6NHj37v63P72b98+RKrVq1S2e/Fixeiz9C733/Pnz9X2S6VSpVXKuf3d/ttnTt3xuPHj7Fs2TLRtrS0tFw/c0T5wUqiFri6umLjxo3o0qUL3N3dVe64cubMGeWSJQBQtWpV+Pv7Y+nSpUhMTISXlxfOnTuHNWvWoH379mjatGmBxdW1a1eMHTsWHTp0wPDhw5GamorFixejXLlyKpP2g4KCcPLkSbRu3RrOzs548uQJFi1ahBIlSqBhw4bv7f/3339Hq1at4Onpib59+yqXwJHL5Rq9B65UKsUvv/zy0f2+/vprBAUFoXfv3qhfvz6uX7+ODRs2iObcubq6wtLSEiEhIShatCjMzMxQt25dtef9HD16FIsWLcLEiROVS/KsWrUKTZo0wfjx4zFjxgy1+gPeXAywZMkS9OrVCxcvXkTp0qWxbds2nD59GnPnzv2kSf2aYmFhgcaNG2PGjBnIyspC8eLFcfjwYURHR+e7z7Jly2LcuHGYMmUKGjVqhI4dO0Imk+H8+fNwcnJCcHBwrq/7+uuvsWPHDnTo0AGtW7dGdHQ0QkJC4OHhka9b6K1btw4bNmxAhw4dULNmTRgbGyMiIgIrV66EiYmJcr6bVCrF8uXL0apVK1SsWBG9e/dG8eLF8fjxYxw7dgwWFhbYu3ev2sevWbMm/v77b8yePRtOTk5wcXFRXnRS0BYuXIiGDRuicuXK6N+/P8qUKYP4+HiEhYXh33//xdWrV9Xus2bNmli8eDGmTp2KsmXLws7OTnlxRm7Wrl2LFi1aoGPHjmjTpg2aN28OMzMzREVFYdOmTYiNjX3vrThbtGihrP4NHDgQycnJWLZsGezs7FQS3zVr1mDRokXo0KEDXF1d8erVKyxbtgwWFhbKP3b79euHhIQENGvWDCVKlMDDhw+xYMECVKtWTWVEKL++++47bNmyBYMGDcKxY8fQoEEDZGdn4/bt29iyZQsOHTr0Rd1qlbSHSaKWtG3bFteuXcPvv/+O3bt3Y/HixZDJZKhSpQpmzZqF/v37K/ddvnw5ypQpg9WrV2Pnzp1wcHBAYGBggQ+BWVtbY+fOnQgICMCPP/6oXIMtKipKJUls27YtHjx4gJUrV+LZs2ewsbGBl5cXJk+erJzQnhtvb28cPHgQEydOxIQJE2BkZAQvLy/89ttvOjGx+ueff0ZKSgo2btyIzZs3o0aNGti/fz9++uknlf2MjIywZs0aBAYGYtCgQXj9+jVWrVql1nt49eoV+vTpg+rVq6vcBaJRo0YYMWIEZs2ahY4dOyqv8M0rU1NTHD9+HD/99BPWrFmDpKQklC9fHqtWrcrzrc20YePGjRg2bBgWLlwIQRDQokUL/PXXX+9dKzIvgoKC4OLiggULFmDcuHEoUqQIqlSp8sF19Hr16oW4uDgsWbIEhw4dgoeHB9avX4+tW7fi+PHjascwcOBAFClSBKGhodi9ezeSkpJga2uLFi1aIDAwENWrV1fu26RJE4SFhSmr3snJyXBwcEDdunUxcODA/JwCzJ49GwMGDMAvv/yCtLQ0+Pv7ayxJ9PDwwIULFzB58mSsXr0az58/h52dHapXr64yjUAdEyZMwMOHDzFjxgy8evUKXl5eH0wSbW1tcebMGSxatAibN2/GuHHjkJmZCWdnZ7Rt2/a9axQCby6q27ZtG3755ReMHj0aDg4OGDx4MGxtbZXrrAJQ/qG+adMmxMfHQy6Xo06dOtiwYYPyO6BHjx5YunQpFi1ahMTERDg4OKBLly6YNGlSgdyZSCqVYteuXZgzZ45ybcUiRYqgTJkyGDFihPICFqJPJRE+daY3EREREX1xOCeRiIiIiESYJBIRERGRCJNEIiIiIhJhkkhEREREIkwSiYiIiEiESSIRERERiTBJJCIiIiKRL3Ix7cazT2s7BCKRuR0razsEIhVhj8W32CTSpiENSmvt2KbVh2qs77TLf2isb01iJZGIiIiIRL7ISiIRERGRWiSsm72LSSIRERGRRKLtCHQO02YiIiIiEmElkYiIiIjDzSI8I0REREQkwkoiEREREeckirCSSEREREQirCQSERERcU6iCM8IEREREYmwkkhERETEOYkiTBKJiIiIONwswjNCRERERCKsJBIRERFxuFmElUQiIiIiEmElkYiIiIhzEkV4RoiIiIhIhJVEIiIiIs5JFGElkYiIiIhEWEkkIiIi4pxEESaJRERERBxuFmHaTEREREQirCQSERERcbhZhGeEiIiIiERYSSQiIiJiJVGEZ4SIiIhIh5w8eRJt2rSBk5MTJBIJdu3apbI9OTkZQ4cORYkSJWBqagoPDw+EhISo7JOeno4hQ4bA2toa5ubm6NSpE+Lj49WKg0kiERERkVSiuYeaUlJSULVqVSxcuDDX7QEBATh48CDWr1+PiIgIjBw5EkOHDsWePXuU+4waNQp79+7F1q1bceLECfz333/o2LGjWnFwuJmIiIhIh7Rq1QqtWrV67/YzZ87A398fTZo0AQAMGDAAS5Yswblz59C2bVu8fPkSK1aswMaNG9GsWTMAwKpVq+Du7o6zZ8+iXr16eYqDlUQiIiIiiVRjj4yMDCQlJak8MjIy8h1q/fr1sWfPHjx+/BiCIODYsWO4c+cOWrRoAQC4ePEisrKy4O3trXxNhQoVUKpUKYSFheX5OEwSiYiIiCQSjT2Cg4Mhl8tVHsHBwfkOdcGCBfDw8ECJEiVgbGyMli1bYuHChWjcuDEAIC4uDsbGxrC0tFR5nb29PeLi4vJ8HA43ExEREWlQYGAgAgICVNpkMlm++1uwYAHOnj2LPXv2wNnZGSdPnsSQIUPg5OSkUj38VEwSiYiIiDS4BI5MJvukpPBtaWlp+Pnnn7Fz5060bt0aAFClShVcuXIFM2fOhLe3NxwcHJCZmYnExESVamJ8fDwcHBzyfCwONxMREREVEllZWcjKyoJUqprCGRgYQKFQAABq1qwJIyMjhIaGKrdHRkYiJiYGnp6eeT4WK4lEREREEvWXqtGU5ORk3L17V/k8OjoaV65cgZWVFUqVKgUvLy+MGTMGpqamcHZ2xokTJ7B27VrMnj0bACCXy9G3b18EBATAysoKFhYWGDZsGDw9PfN8ZTPAJJGIiIhIp1y4cAFNmzZVPs+Zz+jv74/Vq1dj06ZNCAwMhJ+fHxISEuDs7Ixp06Zh0KBBytfMmTMHUqkUnTp1QkZGBnx8fLBo0SK14pAIgiAUzFvSHY1nn9Z2CEQicztW1nYIRCrCHidoOwQiFUMalNbasU1b/K6xvtMOj9FY35rEOYlEREREJMLhZiIiIiIdmpOoK5gkEhEREWlwCZzCimeEiIiIiERYSSQiIiLicLMIK4lEREREJMJKIhERERHnJIrwjBARERGRCCuJRERERJyTKMJKIhERERGJsJJIRERExDmJIkwSiYiIiJgkivCMEBEREZGITiWJmZmZiIyMxOvXr7UdChEREekTiURzj0JKJ5LE1NRU9O3bF0WKFEHFihURExMDABg2bBh+/fVXLUdHREREpH90IkkMDAzE1atXcfz4cZiYmCjbvb29sXnzZi1GRkRERHpBItXco5DSiQtXdu3ahc2bN6NevXqQvFWWrVixIu7du6fFyIiIiIj0k04kiU+fPoWdnZ2oPSUlRSVpJCIiItII5hsiOlEDrVWrFvbv3698npMYLl++HJ6entoKi4iIiEhv6UQlcfr06WjVqhVu3bqF169fY968ebh16xbOnDmDEydOaDs8IiIi+tIV4rmDmqITZ6Rhw4a4cuUKXr9+jcqVK+Pw4cOws7NDWFgYatasqe3wiIiI6EvHJXBEdKKSCACurq5YtmyZtsMgIiIiIuhIJdHb2xurV69GUlKStkMhIiIiPSSRSDT2KKx0IkmsWLEiAgMD4eDggG+//Ra7d+9GVlaWtsMiIiIi0ls6kSTOmzcPjx8/xq5du2BmZoaePXvC3t4eAwYM4IUrREREpHGsJIrpRJIIAFKpFC1atMDq1asRHx+PJUuW4Ny5c2jWrJm2QyMiIiLSOzpz4UqOuLg4bNq0CevXr8e1a9dQp04dbYdEREREX7rCW/DTGJ2oJCYlJWHVqlX46quvULJkSSxevBht27ZFVFQUzp49q+3wiIiIiPSOTlQS7e3tUaxYMXTp0gXBwcGoVauWtkMiIiIiPVKY5w5qik4kiXv27EHz5s0hlepEYZOIiIj0DJNEMZ1IEr/66itth0BEREREb9FaklijRg2EhoaiWLFiqF69+gcz+EuXLn3GyIiIiEjfsJIoprUksV27dpDJZMp/84dDREREpDu0liROnDhR+e9JkyZpKwwiIiIiFqtyoRNzEsuUKYPz58/D2tpapT0xMRE1atTA/fv3tRTZl6dqcQt0rVUc5e3NYWNujJ93R+DUvQTl9t6eJdGsvA3sisrwOltAZHwylp1+iIi4ZOU+RU0MMbJpGdQvUwwKATh59znmH7uPtCyFNt4SfYG2rVuK7euXqbQ5lXDGrBXbAACJCc+wYfl8XL8UjvTUVDiWdEb7rn1QtxEX3yfNUCiyEb5rPSLPhiLl5QuYWVrDo8FXqN2muzK5SH35Aqe3rUDMjYvISEuBU7lKaOI3BJb2xbUcPVH+6ESS+ODBA2RnZ4vaMzIy8O+//2ohoi+XiZEU956m4MDNeExr6y7a/uhFGuYevY//XqZDZihF5xrFMatTRXRbeREv014DAMa3KgdrMyMEbL8JQ6kEgT5uGP1VWUw5cOdzvx36gpVwLoNxvy5UPpca/O/ratHvk5Ca/AqjJ81GUbkcp48dwrzpgZi2YC1cypbXRrj0hbt4YAuuH9+Hr/qOhnVxZ8Q/iMLfK2bB2NQM1b5qD0EQsO+PyZAaGODr4ZNgbFIElw/vwM6ZP6HH1GUwkplo+y3Qx7CQKKLVJHHPnj3Kfx86dAhyuVz5PDs7G6GhoXBxcdFGaF+s8AeJCH+Q+N7tf99+pvL8jxPR+LqyPVxtzHDp0Us4W5minksx9N9wFZHxb6qLc4/dx4wOHlh04gGep2RqMnzSIwYGBrC0ssl1251b19B32E8oW6EiAKBj9774a8efiI6KYJJIGhF79xbKVPOES9W6AAALGwfcCT+G+OhIAEBi/GPE3YuA35QlsC5eGgDQ9LthWD6qKyLDj6FS41baCp0o37SaJLZv3x7Am3kA/v7+KtuMjIxQunRpzJo1SwuREQAYSiVoW9ker9Jf497TFABARceieJX+WpkgAsDFh4lQCICHozn+uZvwvu6I1BL3+BEGd2sFY2NjuLlXRtc+Q2Fj5wAAKOdRBWEnjqB6nQYoYl4UZ0/+jazMDHhUqanlqOlL5VjWAzdO/IUXcf+imEMJPI25h/+ibqJRl4EAgOzXWQAAAyNj5WskUikMDI0QG3WTSWIhwDmJYlpNEhWKN3PYXFxccP78edjY5F41oM/L06UYJrYuDxMjKZ6nZOKH7TfxMv3NULOVmTFepGap7J8tAK/Ss2BVxDi37ojUVrZCRQwaPRGOJZyRmPAM29cvw+Qf+mPGkk0wLWKGEeOCMX/6z+j/rTcMDAxgLDNBwMTf4VC8pLZDpy9ULd8uyExLxbpx/SCVSqFQKODZsRcqeL6ZB1vMoSSKWtvhzLaVaOY/AkYyE1w+vAPJL54hJZF/PFPhpBNzEqOjo/P92oyMDGRkZKi0KV5nQmrIhCW/Lj96ib7rr0Buaog2lR0w+evyGLjxGhLTsj7+YqICUK12A+W/ncu4oWyFShj2XRucPfk3mrZshy1rQpCS/Arjfl2IohaWOB92AvOmBWLirGUo5VJWi5HTlyrq/ElEnj2KlgN+glVxZzyNuYd//gyBuaU13Bt8BQNDQ7QeMgF/r5qNpcO+gUQqRUmP6nCuXBsQBG2HT3nASqKYTiSJAJCSkoITJ04gJiYGmZmq89qGDx/+3tcFBwdj8uTJKm2lWvSGs09fjcSpD9JfK/A4MR2PE4FbsXexsXcNtK5khw3nHyMhJRPFihip7G8gAYqaGCEhlfMRSTPMzIvCsUQpxP33CPH//YvDe7ZgxpJNKFnaFQDg7FoOkdcv4/Cereg3IlDL0dKX6NSWZajp2wXl6jYBANiUcMGr509wYf8muDd4c9cwu9Ju6D55MTJSU5D9OgtFLCyxecpw2JUup8XIKa+YJIrpRJJ4+fJl+Pr6IjU1FSkpKbCyssKzZ89QpEgR2NnZfTBJDAwMREBAgEqbb8hFTYesVyQSwNjwzX21b8a+QlETQ5SzM8OdJ2/mKdYoZQmpBLgVm/yhbojyLT0tFfH/PUaj5jbIyEgHANG93qUGBhAELsNEmvE6MwMSqWoSIZFKIeRSJZQVMQPw5mKWJw+iUK+Dv2gfosJAJ5LEUaNGoU2bNggJCYFcLsfZs2dhZGSEHj16YMSIER98rUwmU965JQeHmt/P1EiK4pamyueOchOUtTVDUnoWktJe47u6JXD6fgKeJ2dBbmqIDtUcYWMuw7E7b656fpiQhrPRL/DjV2UxK/QeDKQSjGxWBqGRz3hlMxWY9Uvnoka9RrC1c8SL50+xdd1SSA2kqN/EB0XMi8LBqSSWzwuGX/8RKGohx/kzx3H9UjjGBM3Rduj0hXKpVg/n921CUSs7WBd3xtOH93D50A5UbNRCuU/U+ZMwLSpHUSs7PHscjZMbQ1CmhiecK/GCqsKAlUQxiZDbn0GfmaWlJcLDw1G+fHlYWloiLCwM7u7uCA8Ph7+/P27fvq1Wf41nn9ZQpIVftRIWmN+5sqj9r5vxmPX3PUzwLQ93R3PITYyQlP4at+NeYW34v7gd/85i2s3KoEEZKygEASeiuJh2XsztKD7vlLv5039GxPXLSH71EhbyYihfsSq69Poe9k4lAACxj2OwacUfuH3zKjLSUmHvVBJff9MDjbx9tRx54RL2mBdU5FVmWirO7lyDe5fPIDUpEWaW1ihftwnqtPWDgeGbKThXjuzCpYNb/3+7FSp4eqNO2+7K7fRxQxqU1tqxrXv+qbG+n6/tptb+J0+exO+//46LFy8iNjYWO3fuVK4IkyMiIgJjx47FiRMn8Pr1a3h4eGD79u0oVaoUACA9PR0//PADNm3ahIyMDPj4+GDRokWwt7fPcxw6kSTa2trizJkzcHNzQ7ly5bBgwQL4+Pjg9u3bqFmzJlJSUtTqj0ki6SImiaRrmCSSrtFqkuivwSRxjXpJ4l9//YXTp0+jZs2a6NixoyhJvHfvHurUqYO+ffuiW7dusLCwwM2bN1GvXj3Y2dkBAAYPHoz9+/dj9erVkMvlGDp0KKRSKU6fznuOpBPDzdWrV8f58+fh5uYGLy8vTJgwAc+ePcO6detQqVIlbYdHRERE9Nm0atUKrVq9f23NcePGwdfXFzNmzFC2ubq6Kv/98uVLrFixAhs3bkSzZm+WaVq1ahXc3d1x9uxZ1KtXL09xSD++i+ZNnz4djo6OAIBp06ahWLFiGDx4MJ4+fYqlS5dqOToiIiL60kkkEo09MjIykJSUpPJ4d/m+vFIoFNi/fz/KlSsHHx8f2NnZoW7duti1a5dyn4sXLyIrKwve3t7KtgoVKqBUqVIICwvL87F0IkmsVasWmjZtCgCws7PDwYMHkZSUhIsXL6Jq1apajo6IiIgo/4KDgyGXy1UewcHB+erryZMnSE5Oxq+//oqWLVvi8OHD6NChAzp27IgTJ04AAOLi4mBsbAxLS0uV19rb2yMuLi7Px9KJ4WYiIiIibdLk1c25Ldf37soseZVzt7p27dph1KhRAIBq1arhzJkzCAkJgZeX16cF+xadSBKrV6+e6w9HIpHAxMQEZcuWRa9evZTVRiIiIqKCpMkkMbfl+vLLxsYGhoaG8PDwUGl3d3fHqVOnAAAODg7IzMxEYmKiSjUxPj4eDg4OeT6WTgw3t2zZEvfv34eZmRmaNm2Kpk2bwtzcHPfu3UPt2rURGxsLb29v7N69W9uhEhEREWmNsbExateujcjISJX2O3fuwNnZGQBQs2ZNGBkZITQ0VLk9MjISMTEx8PT0zPOxdKKS+OzZM/zwww8YP368SvvUqVPx8OFDHD58GBMnTsSUKVPQrl07LUVJREREXywdWks7OTkZd+/eVT6Pjo7GlStXYGVlhVKlSmHMmDHo0qULGjdujKZNm+LgwYPYu3cvjh8/DgCQy+Xo27cvAgICYGVlBQsLCwwbNgyenp55vrIZ0JF1EuVyOS5evIiyZcuqtN+9exc1a9bEy5cvcfv2bdSuXRuvXr36aH9cJ5F0EddJJF3DdRJJ12hznUS7vls01veTFZ3V2v/48eO5TrHz9/fH6tWrAQArV65EcHAw/v33X5QvXx6TJ09WKaTlLKb9559/qiymrc5ws05UEk1MTHDmzBlRknjmzBmYmJgAeDNRM+ffRERERAVJl27L16RJk1zvC/62Pn36oE+fPu/dbmJigoULF2LhwoX5jkMnksRhw4Zh0KBBuHjxImrXrg0AOH/+PJYvX46ff/4ZAHDo0CFUq1ZNi1ESERER6Q+dSBJ/+eUXuLi44I8//sC6desAAOXLl8eyZcvQvXt3AMCgQYMwePBgbYZJREREXyhdqiTqCp1IEgHAz88Pfn5+791uamr6GaMhIiIi0m86sQQOACQmJiqHlxMS3kymvnTpEh4/fqzlyIiIiOhLp8nb8hVWOlFJvHbtGry9vSGXy/HgwQP069cPVlZW2LFjB2JiYrB27Vpth0hERERfsMKczGmKTlQSAwIC0KtXL0RFRalcwezr64uTJ09qMTIiIiIi/aQTlcTz589jyZIlovbixYurdSNqIiIionxhIVFEJyqJMpkMSUlJovY7d+7A1tZWCxERERER6TedSBLbtm2LoKAgZGVlAXgzLyAmJgZjx45Fp06dtBwdERERfel44YqYTiSJs2bNQnJyMuzs7JCWlgYvLy+ULVsW5ubmmDZtmrbDIyIiItI7OjEnUS6X48iRIzh9+jSuXr2K5ORk1KhRA97e3toOjYiIiPRAYa74aYpOJIkAEBoaitDQUDx58gQKhQK3b9/Gxo0bAby5iTURERERfT46kSROnjwZQUFBqFWrFhwdHZnNExER0WfF3ENMJ5LEkJAQrF69Gt999522QyEiIiJ9xBxRRCcuXMnMzET9+vW1HQYRERER/T+dSBL79eunnH9IRERE9LlxCRwxnRhuTk9Px9KlS/H333+jSpUqMDIyUtk+e/ZsLUVGREREpJ90Ikm8du0aqlWrBgC4ceOGyrbCnIETERFR4cB8Q0wnksRjx45pOwQiIiIieotOJIlERERE2sRKophOXLhCRERERLqFlUQiIiLSe6wkijFJJCIiImKOKMLhZiIiIiISYSWRiIiI9B6Hm8VYSSQiIiIiEVYSiYiISO+xkijGSiIRERERibCSSERERHqPhUQxVhKJiIiISISVRCIiItJ7nJMoxiSRiIiI9B5zRDEONxMRERGRCCuJREREpPc43CzGSiIRERERibCSSERERHqPhUQxVhKJiIiISISVRCIiItJ7UilLie9iJZGIiIiIRFhJJCIiIr3HOYliTBKJiIhI73EJHDEONxMRERGRCCuJREREpPdYSBRjJZGIiIhIh5w8eRJt2rSBk5MTJBIJdu3a9d59Bw0aBIlEgrlz56q0JyQkwM/PDxYWFrC0tETfvn2RnJysVhxMEomIiEjvSSQSjT3UlZKSgqpVq2LhwoUf3G/nzp04e/YsnJycRNv8/Pxw8+ZNHDlyBPv27cPJkycxYMAAteLgcDMRERGRDmnVqhVatWr1wX0eP36MYcOG4dChQ2jdurXKtoiICBw8eBDnz59HrVq1AAALFiyAr68vZs6cmWtSmRtWEomIiEjvabKSmJGRgaSkJJVHRkZGvmNVKBT47rvvMGbMGFSsWFG0PSwsDJaWlsoEEQC8vb0hlUoRHh6e5+MwSSQiIiLSoODgYMjlcpVHcHBwvvv77bffYGhoiOHDh+e6PS4uDnZ2dipthoaGsLKyQlxcXJ6Pw+FmIiIi0nuavLo5MDAQAQEBKm0ymSxffV28eBHz5s3DpUuXNL62I5NEIiIi0nuaTLhkMlm+k8J3/fPPP3jy5AlKlSqlbMvOzsYPP/yAuXPn4sGDB3BwcMCTJ09UXvf69WskJCTAwcEhz8dikkhERERUSHz33Xfw9vZWafPx8cF3332H3r17AwA8PT2RmJiIixcvombNmgCAo0ePQqFQoG7dunk+FpNEIiIi0nu6tJh2cnIy7t69q3weHR2NK1euwMrKCqVKlYK1tbXK/kZGRnBwcED58uUBAO7u7mjZsiX69++PkJAQZGVlYejQoejatWuer2wGeOEKERERkU65cOECqlevjurVqwMAAgICUL16dUyYMCHPfWzYsAEVKlRA8+bN4evri4YNG2Lp0qVqxcFKIhEREek9TV8Eoo4mTZpAEIQ87//gwQNRm5WVFTZu3PhJcbCSSEREREQirCQSERGR3tOhQqLOYCWRiIiIiERYSSQiIiK9p0tzEnUFK4lEREREJMJKIhEREek9FhLFmCQSERGR3uNwsxiHm4mIiIhIhJVEIiIi0nssJIp9kUni4eENtB0CkUix2kO1HQKRiidn52s7BCLSYV9kkkhERESkDs5JFOOcRCIiIiISYSWRiIiI9B4LiWKsJBIRERGRCCuJREREpPc4J1GMSSIRERHpPeaIYhxuJiIiIiIRVhKJiIhI73G4WYyVRCIiIiISYSWRiIiI9B4riWKsJBIRERGRCCuJREREpPdYSBRjJZGIiIiIRFhJJCIiIr3HOYliTBKJiIhI7zFHFONwMxERERGJsJJIREREeo/DzWKsJBIRERGRCCuJREREpPdYSBRjJZGIiIiIRFhJJCIiIr0nZSlRhJVEIiIiIhJhJZGIiIj0HguJYkwSiYiISO9xCRwxDjcTERERkQgriURERKT3pCwkirCSSEREREQirCQSERGR3uOcRDFWEomIiIhIhJVEIiIi0nssJIqxkkhEREREIqwkEhERkd6TgKXEdzFJJCIiIr3HJXDEONxMRERERCJMEomIiEjvSSQSjT3UdfLkSbRp0wZOTk6QSCTYtWuXcltWVhbGjh2LypUrw8zMDE5OTujZsyf+++8/lT4SEhLg5+cHCwsLWFpaom/fvkhOTlYrDiaJRERERDokJSUFVatWxcKFC0XbUlNTcenSJYwfPx6XLl3Cjh07EBkZibZt26rs5+fnh5s3b+LIkSPYt28fTp48iQEDBqgVB+ckEhERkd7TpSVwWrVqhVatWuW6TS6X48iRIyptf/zxB+rUqYOYmBiUKlUKEREROHjwIM6fP49atWoBABYsWABfX1/MnDkTTk5OeYqDlUQiIiIiDcrIyEBSUpLKIyMjo8D6f/nyJSQSCSwtLQEAYWFhsLS0VCaIAODt7Q2pVIrw8PA898skkYiIiPSeVCLR2CM4OBhyuVzlERwcXCBxp6enY+zYsejWrRssLCwAAHFxcbCzs1PZz9DQEFZWVoiLi8tz3xxuJiIiItKgwMBABAQEqLTJZLJP7jcrKwudO3eGIAhYvHjxJ/f3LiaJREREpPc0OSdRJpMVSFL4tpwE8eHDhzh69KiyiggADg4OePLkicr+r1+/RkJCAhwcHPJ8DA43ExERkd7TpSVwPiYnQYyKisLff/8Na2trle2enp5ITEzExYsXlW1Hjx6FQqFA3bp183ycPFUSr127lucOq1Spkud9iYiIiEhVcnIy7t69q3weHR2NK1euwMrKCo6Ojvjmm29w6dIl7Nu3D9nZ2cp5hlZWVjA2Noa7uztatmyJ/v37IyQkBFlZWRg6dCi6du2a5yubAUAiCILwsZ2kUikkEgnet2vONolEguzs7DwfXFPSX2s7AiKxYrWHajsEIhVPzs7XdghEKorKtDfA+e3qSxrre2uvGmrtf/z4cTRt2lTU7u/vj0mTJsHFxSXX1x07dgxNmjQB8GYx7aFDh2Lv3r2QSqXo1KkT5s+fD3Nz8zzHkadKYnR0dJ47JCIiIqL8a9KkyXsLcwA+uC2HlZUVNm7c+Elx5ClJdHZ2/qSDEBEREekyqS6tpq0j8lXXXbduHRo0aAAnJyc8fPgQADB37lzs3r27QIMjIiIiIu1QO0lcvHgxAgIC4Ovri8TEROUcREtLS8ydO7eg4yMiIiLSOIkGH4WV2kniggULsGzZMowbNw4GBgbK9lq1auH69esFGhwRERERaYfai2lHR0ejevXqonaZTIaUlJQCCYqIiIjoc9LEeoaFndqVRBcXF1y5ckXUfvDgQbi7uxdETERERESflVSiuUdhpXYlMSAgAEOGDEF6ejoEQcC5c+fw559/Ijg4GMuXL9dEjERERET0mamdJPbr1w+mpqb45ZdfkJqaiu7du8PJyQnz5s1D165dNREjERERkUZxuFlM7SQRAPz8/ODn54fU1FQkJyfDzs6uoOMiIiIiIi3KV5IIAE+ePEFkZCSAN9m3ra1tgQVFRERE9DmxkCimdpL46tUrfP/99/jzzz+hUCgAAAYGBujSpQsWLlwIuVyep37mz8/7PUOHDx+ubphERERE9AnyNSfx8uXL2L9/Pzw9PQEAYWFhGDFiBAYOHIhNmzblqZ85c+bkaT+JRMIkkYiIiDSKcxLF1E4S9+3bh0OHDqFhw4bKNh8fHyxbtgwtW7bMcz/R0dHqHpqIiIiIPhO1k0Rra+tch5TlcjmKFStWIEERERERfU6FeT1DTVE7Sfzll18QEBCAdevWwcHBAQAQFxeHMWPGYPz48fkO5N9//8WePXsQExODzMxMlW2zZ8/Od79EREREH8PhZrE8JYnVq1dXOXlRUVEoVaoUSpUqBQCIiYmBTCbD06dPMXDgQLWDCA0NRdu2bVGmTBncvn0blSpVwoMHDyAIAmrUqKF2f0RERET0afKUJLZv316jQQQGBmL06NGYPHkyihYtiu3bt8POzg5+fn5qzXMkIiIiyg/WEcXylCROnDhRo0FERETgzz//fBOQoSHS0tJgbm6OoKAgtGvXDoMHD9bo8YmIiIhIlVTbAQCAmZmZch6io6Mj7t27p9z27NkzbYVFREREekIqkWjsUVipfeFKdnY25syZgy1btuR6kUlCQoLaQdSrVw+nTp2Cu7s7fH198cMPP+D69evYsWMH6tWrp3Z/RERERPRp1K4kTp48GbNnz0aXLl3w8uVLBAQEoGPHjpBKpZg0aVK+gpg9ezbq1q2r7L958+bYvHkzSpcujRUrVuSrTyIiIqK8kkg09yis1K4kbtiwAcuWLUPr1q0xadIkdOvWDa6urqhSpQrOnj2r9t1RsrOz8e+//6JKlSoA3gw9h4SEqBsWERERERUgtSuJcXFxqFy5MgDA3NwcL1++BAB8/fXX2L9/v9oBGBgYoEWLFnjx4oXaryUiIiIqCBKJRGOPwkrtJLFEiRKIjY0FALi6uuLw4cMAgPPnz0Mmk+UriEqVKuH+/fv5ei0RERERFTy1k8QOHTogNDQUADBs2DCMHz8ebm5u6NmzJ/r06ZOvIKZOnYrRo0dj3759iI2NRVJSksqDiIiISJM4J1FMIgiC8CkdnD17FmfOnIGbmxvatGmTrz6k0v/lqm+XZQVBgEQiQXZ2tlr9pb/OVxh6acWyJQg9chjR0fchMzFBtWrVMTJgNEq7lFHus23LZvx1YB8ibt1ESkoK/gk7DwsLCy1GXTgVqz1U2yHorAY1XDGqpzdqeJSCo60cnUctxd7j15Tb7ayKYuqIdvD2dIfc3BSnLt1FwIytuBfzVLlPn44N0KVVLVSrUAIW5qZwaDQGL5PTtPF2Co0nZ+drO4RCY9XypTgWegQPou9DJjNBlWrVMWzkDyjt4qLcZ0Cfnrh04bzK6zp+2wU/j5/0maMtvIrKtLcy3+DttzTW9+JOHhrrW5PUvnDlXfXq1UO9evXw5MkTTJ8+HT///LPafRw7duxTw6B8unD+HLp080PFypWR/TobC+bNxqD+fbFjz34UKVIEAJCenob6DRqhfoNGmD93lpYjpi+RmakM1+88xtrdYdg8e4Bo+5Y5A5D1OhvfjlyCpJR0DO/RDAdChqF6x6lITX+zDFcREyMcOXMLR87cwpTh7T73W6Av3KUL5/Ft1+7wqFgJ2dnZWDh/DoYO6outO/fB9P+/KwGgQ6dvMXDIMOVzExNTbYRLVCA+OUnMERsbi/Hjx+crSXRxcUHJkiVFkzsFQcCjR48KKkTKxeKlqksMBU37FU0beSLi1k3UrFUbANCjZy8AwPlz4Z87PNITh0/fwuHTuf8VX7aUHepWcUGNTlMRcT8OADB8+mY8+Hs6OreqidU7wwAAf2w8DgBoVNPts8RM+mVByDKV55OmBOOrJg0Qcesmavz/dyUAmJiYwMbG9nOHRwWgMA8La4pO3HHFxcUFT58+FbUnJCTA5a1SPmle8qtXAAALuVzLkRC9ITN+87dseub/5pEIgoDMzNeoX81VW2GRnktOzv278q8D+9C8sSc6d2iDP+bNRnoapzxQ4VVglcRPkTP38F3JyckwMTHRQkT6SaFQYMZv01Gteg24uZXTdjhEAIDIB3GIiU3AlGFtMXTqn0hJy8TwHk1RwqEYHGz4xwx9fgqFArNmBKNq9Roo+9Z3ZUvfr+Ho6ARbWztERUViwZxZePggGr/PWaDFaCmvCvNSNZqi1SQxICAAwJsfzPjx45Vz4IA3i2yHh4ejWrVqH+wjIyMDGRkZKm2CgSzfy/Hos+lTJ+NeVBRWr9uo7VCIlF6/VqDrD8uweKIfYk/+jtevs3E0PBIHT93k8BBpxW/TgnDvbhSWr96g0t7xm87Kf5ctVw42NrYY3L83/n0UgxIlS33uMIk+WZ6TxJyE7n1yGy7+mMuXLwN4U0m8fv06jI2NlduMjY1RtWpVjB49+oN9BAcHY/LkySpt48ZPxC8TJqkdjz6bPjUIJ08cx8o162Hv4KDtcIhUXI54hHpdf4WFuQmMjQzx7EUyTq4djYu3YrQdGumZ36ZPwamTJ7B01bqPfldWqvzmTmKPYpgkFgY6Mf9Ox+Q5ScxJ6D6kcePGah0856rm3r17Y968eflaViUwMFCUwAoGrCLmlSAICJ42BUdDj2DF6nUoUaKktkMieq+k5HQAgGspW9TwKIXJi/ZpOSLSF4IgYEbwVBw/+jeWrFiD4iVKfPQ1kZG3AQA2tryQhQqnPCeJmlymZtWqVfl+rUwmHlrmOol5N33KZPx1YB/mLlgEsyJmePb/FWHzokWV80GfPX2KZ8+e4VHMm6rN3ag7KFLEDI6OjpBbWmordPqCmJkaw7Xk//5HWrq4NaqUK44XSal4FPcCHb2r4+mLZDyKS0AlNyfMHPMN9h6/htCzt5WvsbcuCntrC7iWsgEAVHJzwquUdDyKe4EXSamf/T3Rl+W3aUE4+Nd+zJr3B4qYmeHZs///rjR/813576MYHDywDw0aeUEut0TUnUjM/v1X1KhZC27lyms5esoLzkkU++TFtAtCs2bNPrj96NGjavXHJDHvqlbM/csraGow2nXoCABYvHABQhb98cF96OO4mPb7NarphsPLR4ja1+05iwET1+P7bl4Y1dMbdtZFEfcsCRv2hSN46UFkvf7fQvvjBvril0G+oj76T1iH9Xu5fFNuuJh23tWq4p5r+8Qp09GmXQfExcViQuCPuHc3CmlpabB3cECTZt7oO2AwzM3NP3O0hZc2F9Meufv2x3fKp7ntKmisb03SiSRx1KhRKs+zsrJw5coV3LhxA/7+/pg3b55a/TFJJF3EJJF0DZNE0jVMEnWLTiyBM2fOnFzbJ02ahOTk5M8cDREREekbKUebRXT6Yp4ePXpg5cqV2g6DiIiISO/oRCXxfcLCwriYNhEREWkcL1wRy1eS+M8//2DJkiW4d+8etm3bhuLFi2PdunVwcXFBw4YN1e6vY0fVix8EQUBsbCwuXLiA8ePH5ydEIiIiIvoEag83b9++HT4+PjA1NcXly5eVdzt5+fIlpk+fnq8g5HK5ysPKygpNmjTBgQMHMHHixHz1SURERJRXUonmHoWV2pXEqVOnIiQkBD179sSmTZuU7Q0aNMDUqVPzFcSnrJNIRERERAVP7UpiZGRkrndWkcvlSExMzHcgiYmJWL58OQIDA5GQkAAAuHTpEh4/fpzvPomIiIjyQiLR3KOwUjtJdHBwwN27d0Xtp06dQpkyZfIVxLVr1+Dm5obffvsNM2fOVCabO3bsQGBgYL76JCIiIsorqUSisYe6Tp48iTZt2sDJyQkSiQS7du1S2S4IAiZMmABHR0eYmprC29sbUVFRKvskJCTAz88PFhYWsLS0RN++fdVeVlDtJLF///4YMWIEwsPDIZFI8N9//2HDhg0YPXo0Bg8erG53AICAgAD07t0bUVFRKlcz+/r64uTJk/nqk4iIiKgwSklJQdWqVbFw4cJct8+YMQPz589HSEgIwsPDYWZmBh8fH6Snpyv38fPzw82bN3HkyBHs27cPJ0+exIABA9SKQ+05iT/99BMUCgWaN2+O1NRUNG7cGDKZDKNHj8awYcPU7Q4AcP78eSxZskTUXrx4ccTFxeWrTyIiIqK80qWFo1u1aoVWrVrluk0QBMydOxe//PIL2rVrBwBYu3Yt7O3tsWvXLnTt2hURERE4ePAgzp8/j1q1agEAFixYAF9fX8ycORNOTk55ikPtcyKRSDBu3DgkJCTgxo0bOHv2LJ4+fYopU6ao25WSTCZDUlKSqP3OnTuwtbXNd79ERERE2paRkYGkpCSVR87qMOqKjo5GXFwcvL29lW1yuRx169ZFWFgYgDfrTFtaWioTRADw9vaGVCpFeHje72Wf78TZ2NgYHh4eqFOnziffvLxt27YICgpCVlYWgDeJaExMDMaOHYtOnTp9Ut9EREREH6PJC1eCg4NFy/0FBwfnK86cEVZ7e3uVdnt7e+W2uLg42NnZqWw3NDSElZWVWiO0ag83N23a9IOrkh89elTdLjFr1ix88803sLOzQ1paGry8vBAXF4d69eph2rRpavdHREREpCsCAwMREBCg0iaTybQUTd6pnSRWq1ZN5XlWVhauXLmCGzduwN/fP19ByOVyHDlyBKdPn8bVq1eRnJyMGjVqqJRSiYiIiDQlP1ch55VMJiuwpNDBwQEAEB8fD0dHR2V7fHy8MkdzcHDAkydPVF73+vVrJCQkKF+fF2oniXPmzMm1fdKkSWpfWv220NBQhIaG4smTJ1AoFLh9+zY2btwIAFi5cmW++yUiIiL6Uri4uMDBwQGhoaHKpDApKQnh4eHKVWY8PT2RmJiIixcvombNmgDejPQqFArUrVs3z8fK172bc9OjRw/UqVMHM2fOVPu1kydPRlBQEGrVqgVHR0feZJuIiIg+K11KPZKTk1XWpI6OjsaVK1dgZWWFUqVKYeTIkZg6dSrc3Nzg4uKC8ePHw8nJCe3btwcAuLu7o2XLlujfvz9CQkKQlZWFoUOHomvXrnm+shkowCQxLCxMZY1DdYSEhGD16tX47rvvCiocIiIiojzTpXssX7hwAU2bNlU+z5nP6O/vj9WrV+PHH39ESkoKBgwYgMTERDRs2BAHDx5UycM2bNiAoUOHonnz5pBKpejUqRPmz5+vVhxqJ4kdO3ZUeS4IAmJjY3HhwgWMHz9e3e4AAJmZmahfv36+XktERET0JWnSpAkEQXjvdolEgqCgIAQFBb13HysrK+W0vfxSewmcdy/htrKyQpMmTXDgwAFMnDgxX0H069fvk98IERERUX7p0m35dIValcTs7Gz07t0blStXRrFixQosiPT0dCxduhR///03qlSpAiMjI5Xts2fPLrBjEREREdHHqZUkGhgYoEWLFoiIiCjQJPHatWvKK3Ru3Lihso0XsRAREZGmMd0QU3tOYqVKlXD//n24uLgUWBDHjh0rsL6IiIiI6NOpPSdx6tSpGD16NPbt24fY2FjRvQiJiIiIChupRHOPwirPlcSgoCD88MMP8PX1BfDmfstvDwULggCJRILs7OyCj5KIiIiIPqs8J4mTJ0/GoEGDODRMREREXxwJCnHJT0PynCTmrNfj5eWlsWCIiIiItKEwDwtrilpzEnmlMREREZF+UOvq5nLlyn00UUxISPikgIiIiIg+N1YSxdRKEidPngy5XK6pWIiIiIhIR6iVJHbt2hV2dnaaioWIiIhIKzilTizPcxJ58oiIiIj0h9pXNxMRERF9aTgnUSzPSaJCodBkHERERESkQ9S+dzMRERHRl4az6sSYJBIREZHekzJLFFFrMW0iIiIi0g+sJBIREZHe44UrYqwkEhEREZEIK4lERESk9zglUYyVRCIiIiISYSWRiIiI9J4ULCW+i5VEIiIiIhJhJZGIiIj0HuckijFJJCIiIr3HJXDEONxMRERERCKsJBIREZHe4235xFhJJCIiIiIRVhKJiIhI77GQKMZKIhERERGJsJJIREREeo9zEsVYSSQiIiIiEVYSiYiISO+xkCjGJJGIiIj0HodWxXhOiIiIiEiElUQiIiLSexKON4uwkkhEREREIqwkEhERkd5jHVGMlUQiIiIiEmElkYiIiPQeF9MWYyWRiIiIiERYSSQiIiK9xzqiGCuJREREpPckEs091JGdnY3x48fDxcUFpqamcHV1xZQpUyAIgnIfQRAwYcIEODo6wtTUFN7e3oiKiirgM8IkkYiIiEhn/Pbbb1i8eDH++OMPRERE4LfffsOMGTOwYMEC5T4zZszA/PnzERISgvDwcJiZmcHHxwfp6ekFGguHm4mIiEjv6cpi2mfOnEG7du3QunVrAEDp0qXx559/4ty5cwDeVBHnzp2LX375Be3atQMArF27Fvb29ti1axe6du1aYLGwkkhERESkQRkZGUhKSlJ5ZGRk5Lpv/fr1ERoaijt37gAArl69ilOnTqFVq1YAgOjoaMTFxcHb21v5Grlcjrp16yIsLKxA42aSSERERHpPqsFHcHAw5HK5yiM4ODjXOH766Sd07doVFSpUgJGREapXr46RI0fCz88PABAXFwcAsLe3V3mdvb29cltB4XAzERERkQYFBgYiICBApU0mk+W675YtW7BhwwZs3LgRFStWxJUrVzBy5Eg4OTnB39//c4SrxCSRiIiI9J4m5yTKZLL3JoXvGjNmjLKaCACVK1fGw4cPERwcDH9/fzg4OAAA4uPj4ejoqHxdfHw8qlWrVqBxc7iZiIiISEekpqZCKlVNzwwMDKBQKAAALi4ucHBwQGhoqHJ7UlISwsPD4enpWaCxsJJIREREek83rm0G2rRpg2nTpqFUqVKoWLEiLl++jNmzZ6NPnz4A3lQ8R44cialTp8LNzQ0uLi4YP348nJyc0L59+wKNhUkiERERkY5YsGABxo8fj++//x5PnjyBk5MTBg4ciAkTJij3+fHHH5GSkoIBAwYgMTERDRs2xMGDB2FiYlKgsUiEt5fw/kKkv9Z2BERixWoP1XYIRCqenJ2v7RCIVBSVaW8W3LarsRrr+5uqjh/fSQd9kZXER8/TtB0CkcjpXdO1HQKRim9WnNd2CEQqDn1fV2vH5kUaYjwnRERERCTyRVYSiYiIiNShK7fl0yWsJBIRERGRCCuJREREpPdYRxRjJZGIiIiIRFhJJCIiIr3HKYlirCQSERERkQgriURERKT3pJyVKMIkkYiIiPQeh5vFONxMRERERCKsJBIREZHek3C4WYSVRCIiIiISYSWRiIiI9B7nJIqxkkhEREREIqwkEhERkd7jEjhirCQSERERkQgriURERKT3OCdRjEkiERER6T0miWIcbiYiIiIiEVYSiYiISO9xMW0xVhKJiIiISISVRCIiItJ7UhYSRVhJJCIiIiIRVhKJiIhI73FOohgriUREREQkwkoiERER6T2ukyjGJJGIiIj0HoebxTjcTEREREQirCQSERGR3uMSOGKsJBIRERGRCCuJREREpPc4J1GMlUQiIiIiEmElkYiIiPQel8ARYyWRiIiIiERYSSQiIiK9x0KiGJNEIiIi0ntSjjeLcLiZiIiIiERYSSQiIiK9xzqiGCuJRERERCTCSiIRERERS4kirCQSERERkQgriURERKT3eFs+MVYSiYiIiEiESSIRERHpPYlEcw91PX78GD169IC1tTVMTU1RuXJlXLhwQbldEARMmDABjo6OMDU1hbe3N6KiogrwbLzBJJGIiIj0nkSDD3W8ePECDRo0gJGREf766y/cunULs2bNQrFixZT7zJgxA/Pnz0dISAjCw8NhZmYGHx8fpKen5/ft54pzEomIiIh0xG+//YaSJUti1apVyjYXFxflvwVBwNy5c/HLL7+gXbt2AIC1a9fC3t4eu3btQteuXQssFlYSiYiIiDRYSszIyEBSUpLKIyMjI9cw9uzZg1q1auHbb7+FnZ0dqlevjmXLlim3R0dHIy4uDt7e3so2uVyOunXrIiwsrABPCJNEIiIiIo0KDg6GXC5XeQQHB+e67/3797F48WK4ubnh0KFDGDx4MIYPH441a9YAAOLi4gAA9vb2Kq+zt7dXbisoHG4mIiIivafJJXACAwMREBCg0iaTyXLdV6FQoFatWpg+fToAoHr16rhx4wZCQkLg7++vsRhzw0oiERERkQbJZDJYWFioPN6XJDo6OsLDw0Olzd3dHTExMQAABwcHAEB8fLzKPvHx8cptBYVJIhEREek9XVkCp0GDBoiMjFRpu3PnDpydnQG8uYjFwcEBoaGhyu1JSUkIDw+Hp6fnJ5+Ht3G4mYiIiEhHjBo1CvXr18f06dPRuXNnnDt3DkuXLsXSpUsBABKJBCNHjsTUqVPh5uYGFxcXjB8/Hk5OTmjfvn2BxsIkkYiIiPSertyUr3bt2ti5cycCAwMRFBQEFxcXzJ07F35+fsp9fvzxR6SkpGDAgAFITExEw4YNcfDgQZiYmBRoLBJBEIQC7TGf/vnnHyxZsgT37t3Dtm3bULx4caxbtw4uLi5o2LChWn1FxadpKEqi/HuVnqXtEIhUBO6P0HYIRCoOfV9Xa8e+9DBJY33XcLbQWN+apBNzErdv3w4fHx+Ympri8uXLyrWDXr58qby6h4iIiIg+H51IEqdOnYqQkBAsW7YMRkZGyvYGDRrg0qVLWoyMiIiI9IFEg/8VVjqRJEZGRqJx48aidrlcjsTExM8fEBEREZGe04kk0cHBAXfv3hW1nzp1CmXKlNFCRERERKRPdGUJHF2iE0li//79MWLECISHh0MikeC///7Dhg0bMHr0aAwePFjb4RERERHpHZ1YAuenn36CQqFA8+bNkZqaisaNG0Mmk2H06NEYNmyYtsMjIiKiL1whLvhpjE4kiRKJBOPGjcOYMWNw9+5dJCcnw8PDA+bm5toOjYiIiEgv6cRw8/r165GamgpjY2N4eHigTp06TBCJiIjo85Fo8FFI6USSOGrUKNjZ2aF79+44cOAAsrOztR0SERER6REugSOmE0libGwsNm3aBIlEgs6dO8PR0RFDhgzBmTNntB0aERERkV7SiSTR0NAQX3/9NTZs2IAnT55gzpw5ePDgAZo2bQpXV1dth0dERERfOC6BI6YTF668rUiRIvDx8cGLFy/w8OFDRETw3qJEREREn5tOVBIBIDU1FRs2bICvry+KFy+OuXPnokOHDrh586a2QyMiIqIvHK9bEdOJSmLXrl2xb98+FClSBJ07d8b48ePh6emp7bCIiIiI9JZOJIkGBgbYsmULfHx8YGBgoO1wiIiISN8U5pKfhuhEkrhhwwZth0BEREREb9Fakjh//nwMGDAAJiYmmD9//gf3HT58+GeKirauX4k1S+ej7TfdMWD4j8r2iBtXsW7ZH4iMuA6p1ABlypZH0KxFkMlMtBgtfam2rV2K7euXqbQ5lXDGrJXb8DTuPwzv2S7X1434JRj1Gnt/jhDpC1fJsSi+re4IN1szWJsZY9JfdxAW/QIAYCCVoFedEqjtbAlHCxlSMrNx+d+XWBH2CAmpWco+1vSoBgcLmUq/K8JisOVy7Gd9L5Q3hXk9Q03RWpI4Z84c+Pn5wcTEBHPmzHnvfhKJhEniZ3In4gYO7tmG0q7lVNojblzFxDFD8K1fHwwcORYGBoaIvhsJqURnrnuiL1AJ5zIY99tC5XOpwZuvK2tbeyze9JfKvqEHdmLf1vWoVrv+Z42RvlwmRlLcf5aKQxFPMbGV6neizFCKsrZm2HjhMe4/T4W5zBCDGzpjsm85DNumerHlmvBH+OvWU+Xz1CzeLIIKD60lidHR0bn+m7QjLTUVM6f8jGE/TsCmtaoVnOV/zESbTt3wbY8+yrYSpUp/5ghJ3xgYGMDSykbULs2l/fzp46jX2BsmpkU+V3j0hbsQ8xIXYl7mui01MxuBe2+rtC385wEWfFMJtubGeJqcqWxPy1LgRVrWu12QDirM6xlqik6UgoKCgpCamipqT0tLQ1BQkBYi0j+L50xHbc9GqFarnkp74osERN66DstiVhg9uCd6tGuGn4b1xc1rl7UUKemLuMePMLhrK4zo2Q5/BP+CZ0/ict3v/p0IPLx3B01btv3MERL9j5mxARSCgJQM1Uph5xqO2NqnBhZ+WwnfVHOElImIzuISOGI6kSROnjwZycnJovbU1FRMnjxZCxHplxOhB3Hvzm34DxAP68f99y8AYOOqEPi06YjJvy+Ca7kKGDdqAB4/evi5QyU9UbZCRQwaMxE/TZ+PPsN/wpP4/zA5oD/SUlNE+x47uBvFS7mgXMWqWoiUCDAykKBvvVI4HvVcZTh59/U4BB++ix93R+DArSfoWsMJ/TxLaTFSIvXoxNXNgiBAkkud9+rVq7CysvrgazMyMpCRkaHSlpmhgLFM9p5X0Nuexsdh2fwZmDI7JNdzJigUAICWbTvhK9/2AADXchVw9eI5HDmwG70Gcr4oFbxqdRoo/+1cxg1lK1TCsB5tcPbE32ja6n8XrWRmpOPMsUPo4NdXG2ESwUAqwbgWboAEWHDigcq2HVf/V/2Ofp6GrGwBI7xKY9XZR8hSCJ85Uvqowlzy0xCtJonFihWDRCKBRCJBuXLlVBLF7OxsJCcnY9CgQR/sIzg4WFRtHPrDzxg+5heNxPyluXvnFhJfJGBEv27KNkV2Nm5evYR9OzdjyfpdAIBSpVXvoV3S2QVP43mFHn0eZuZF4ViiFOL+e6TSHv7PUWRkpKOxd2stRUb67E2CWBb2RY3x4+7bH70oJTI+GYYGUthbyPBvYvpnipIo/7SaJM6dOxeCIKBPnz6YPHky5HK5cpuxsTFKly790TuvBAYGIiAgQKXtUaJCI/F+iarWrIs/Vm9TaZv36wSUKOWCTt17w8GpBKxsbPFvzAOVfR7/+xA16zYA0eeQnpaK+NjHaNRc9YKVYwd3o2a9xrCwLKalyEhf5SSIxeUm+HF3BF5lvP7oa8rYFEG2QkAiL2TRSVwCR0yrSaK/vz8AwMXFBfXr14eRkZHafchkMsjeGSY1TksrkPj0QZEiZihdpqxKm8zEFEUt5Mr2Tl39sWFVCFzKlkOZsuURenAv/n34AIFBM7URMumB9Uvnoka9RrC1c8SL50+xde1SSKVS1G/qo9wn7vEj3L5+GT9Onau9QOmLZWIohZP8f+vAOhSVoYx1EbzKeI2E1CyM93FDWdsimLD/DqQSCYqZvvn/16uM13itEOBub44K9ua4+jgJqVnZcLc3x6AGzjh65xmSM7gMDhUOWksSk5KSYGFhAQCoXr060tLSkPae5C5nP9KOdp17IDMzE8sXzMSrVy/h4loOU2aHwLF4SW2HRl+ohKdPsGD6L0h+9RIW8mIoX7EqpsxbpVIxPH5oD6xs7FClZr0P9ESUP+XszPB7ew/l80ENnQEAh28/xfrz/8LT5c1ncXGXyiqvG7PrFq799wpZ2Qp4lbVGj9rFYWQgRVxSBnZci8OOK5ymo6u4BI6YRBAErcyeNTAwQGxsLOzs7CCVSnO9cCXngpbsbPX+6oqKZyWRdM+rdA4xkW4J3B+h7RCIVBz6vq7Wjh0ZJ16Kr6CUdyica7hqrZJ49OhR5ZXLx44d01YYRERERJyRmAutJYleXl65/puIiIjos2OWKKITi2kfPHgQp06dUj5fuHAhqlWrhu7du+PFixdajIyIiIhIP+lEkjhmzBgkJSUBAK5fv46AgAD4+voiOjpatLwNERERUUGTaPC/wkon7rgSHR0ND483V5Ft374dbdq0wfTp03Hp0iX4+vpqOToiIiIi/aMTlURjY2Okpr65qujvv/9GixYtAABWVlbKCiMRERGRpkgkmnsUVjpRSWzYsCECAgLQoEEDnDt3Dps3bwYA3LlzByVKlNBydERERET6RycqiX/88QcMDQ2xbds2LF68GMWLFwcA/PXXX2jZsqWWoyMiIqIvnUSDj8JKJyqJpUqVwr59+0Ttc+bM0UI0RERERKQTSSIAZGdnY9euXYiIeHMHgIoVK6Jt27YwMDDQcmRERET0xSvMJT8N0Ykk8e7du/D19cXjx49Rvnx5AEBwcDBKliyJ/fv3w9XVVcsREhER0ZesMC9Voyk6MSdx+PDhcHV1xaNHj3Dp0iVcunQJMTExcHFxwfDhw7UdHhEREZHe0YlK4okTJ3D27FnlvZwBwNraGr/++isaNGigxciIiIhIHxTmpWo0RScqiTKZDK9evRK1Jycnw9jYWAsREREREek3nUgSv/76awwYMADh4eEQBAGCIODs2bMYNGgQ2rZtq+3wiIiI6AvHJXDEdCJJnD9/PlxdXeHp6QkTExOYmJigfv36KFu2LObNm6ft8IiIiIj0jk4kiZaWlti9ezfu3LmDrVu3YuvWrbhz5w527twJuVyu7fCIiIjoS6ejpcRff/0VEokEI0eOVLalp6djyJAhsLa2hrm5OTp16oT4+PhPO1AudCJJBIAVK1agffv2+Pbbb/Htt9+iffv2WL58ubbDIiIiItKK8+fPY8mSJahSpYpK+6hRo7B3715s3boVJ06cwH///YeOHTsW+PF14urmCRMmYPbs2Rg2bBg8PT0BAGFhYRg1ahRiYmIQFBSk5QiJiIjoS6Zr6yQmJyfDz88Py5Ytw9SpU5XtL1++xIoVK7Bx40Y0a9YMALBq1Sq4u7vj7NmzqFevXoHFoBNJ4uLFi7Fs2TJ069ZN2da2bVtUqVIFw4YNY5JIREREGqXJJXAyMjKQkZGh0iaTySCTyd77miFDhqB169bw9vZWSRIvXryIrKwseHt7K9sqVKiAUqVKISwsrECTRJ0Ybs7KykKtWrVE7TVr1sTr16+1EBERERFRwQgODoZcLld5BAcHv3f/TZs24dKlS7nuExcXB2NjY1haWqq029vbIy4urkDj1okk8bvvvsPixYtF7UuXLoWfn58WIiIiIiJ9osnrVgIDA/Hy5UuVR2BgYK5xPHr0CCNGjMCGDRtgYmKiqbebJzox3Ay8uXDl8OHDyjJpeHg4YmJi0LNnTwQEBCj3mz17trZCJCIiIlLbx4aW33bx4kU8efIENWrUULZlZ2fj5MmT+OOPP3Do0CFkZmYiMTFRpZoYHx8PBweHAo1bJ5LEGzduKE/GvXv3AAA2NjawsbHBjRs3lPtJeM8cIiIi0gBdSTGaN2+O69evq7T17t0bFSpUwNixY1GyZEkYGRkhNDQUnTp1AgBERkYiJiZGefFvQdGJJPHYsWPaDoGIiIhI64oWLYpKlSqptJmZmcHa2lrZ3rdvXwQEBMDKygoWFhbK1WEK8qIVQEeSRCIiIiLt0pFSYh7MmTMHUqkUnTp1QkZGBnx8fLBo0aICP45EEAShwHvVsqj4NG2HQCTyKj1L2yEQqQjcH6HtEIhUHPq+rtaO/e+LTI31XaKYscb61iRWEomIiEjv6cqcRF3CJJGIiIj0HnNEMZ1YJ5GIiIiIdAsriURERKT3ONwsxkoiEREREYmwkkhERER6T8JZiSKsJBIRERGRCCuJRERERCwkirCSSEREREQirCQSERGR3mMhUYxJIhEREek9LoEjxuFmIiIiIhJhJZGIiIj0HpfAEWMlkYiIiIhEWEkkIiIiYiFRhJVEIiIiIhJhJZGIiIj0HguJYqwkEhEREZEIK4lERESk97hOohiTRCIiItJ7XAJHjMPNRERERCTCSiIRERHpPQ43i7GSSEREREQiTBKJiIiISIRJIhERERGJcE4iERER6T3OSRRjJZGIiIiIRFhJJCIiIr3HdRLFmCQSERGR3uNwsxiHm4mIiIhIhJVEIiIi0nssJIqxkkhEREREIqwkEhEREbGUKMJKIhERERGJsJJIREREeo9L4IixkkhEREREIqwkEhERkd7jOolirCQSERERkQgriURERKT3WEgUY5JIRERExCxRhMPNRERERCTCSiIRERHpPS6BI8ZKIhERERGJsJJIREREeo9L4IixkkhEREREIhJBEARtB0G6KSMjA8HBwQgMDIRMJtN2OET8TJJO4ueSvlRMEum9kpKSIJfL8fLlS1hYWGg7HCJ+Jkkn8XNJXyoONxMRERGRCJNEIiIiIhJhkkhEREREIkwS6b1kMhkmTpzIidikM/iZJF3EzyV9qXjhChERERGJsJJIRERERCJMEomIiIhIhEkiEREREYkwSaQCMWnSJFSrVk3bYRDlW+nSpTF37lxth0GFyPHjxyGRSJCYmPjB/fjZosKKSSKpTSKRYNeuXSpto0ePRmhoqHYCIr3UpEkTjBw5UtthkB6rX78+YmNjIZfLAQCrV6+GpaWlaL/z589jwIABnzk6ok9nqO0A6Mtgbm4Oc3NzbYdBpEIQBGRnZ8PQkF91VPCMjY3h4ODw0f1sbW0/QzREBY+VxEKkSZMmGD58OH788UdYWVnBwcEBkyZNUm5PTExEv379YGtrCwsLCzRr1gxXr15V6WPq1Kmws7ND0aJF0a9fP/z0008qw8Tnz5/HV199BRsbG8jlcnh5eeHSpUvK7aVLlwYAdOjQARKJRPn87eHmw4cPw8TERDQEM2LECDRr1kz5/NSpU2jUqBFMTU1RsmRJDB8+HCkpKZ98nkj7PvWz2qtXL7Rv316lz5EjR6JJkybK7SdOnMC8efMgkUggkUjw4MED5fDfX3/9hZo1a0Imk+HUqVO4d+8e2rVrB3t7e5ibm6N27dr4+++/P8OZIG1r0qQJhg4diqFDh0Iul8PGxgbjx49HzupvL168QM+ePVGsWDEUKVIErVq1QlRUlPL1Dx8+RJs2bVCsWDGYmZmhYsWKOHDgAADV4ebjx4+jd+/eePnypfIzmfOZf3u4uXv37ujSpYtKjFlZWbCxscHatWsBAAqFAsHBwXBxcYGpqSmqVq2Kbdu2afhMEYkxSSxk1qxZAzMzM4SHh2PGjBkICgrCkSNHAADffvstnjx5gr/++gsXL15EjRo10Lx5cyQkJAAANmzYgGnTpuG3337DxYsXUapUKSxevFil/1evXsHf3x+nTp3C2bNn4ebmBl9fX7x69QrAmyQSAFatWoXY2Fjl87c1b94clpaW2L59u7ItOzsbmzdvhp+fHwDg3r17aNmyJTp16oRr165h8+bNOHXqFIYOHVrwJ4204lM+qx8zb948eHp6on///oiNjUVsbCxKliyp3P7TTz/h119/RUREBKpUqYLk5GT4+voiNDQUly9fRsuWLdGmTRvExMRo5L2TblmzZg0MDQ1x7tw5zJs3D7Nnz8by5csBvPmD48KFC9izZw/CwsIgCAJ8fX2RlZUFABgyZAgyMjJw8uRJXL9+Hb/99luuoyb169fH3LlzYWFhofxMjh49WrSfn58f9u7di+TkZGXboUOHkJqaig4dOgAAgoODsXbtWoSEhODmzZsYNWoUevTogRMnTmji9BC9n0CFhpeXl9CwYUOVttq1awtjx44V/vnnH8HCwkJIT09X2e7q6iosWbJEEARBqFu3rjBkyBCV7Q0aNBCqVq363mNmZ2cLRYsWFfbu3atsAyDs3LlTZb+JEyeq9DNixAihWbNmyueHDh0SZDKZ8OLFC0EQBKFv377CgAEDVPr4559/BKlUKqSlpb03HiocPvWz6u/vL7Rr105l+4gRIwQvLy+VY4wYMUJln2PHjgkAhF27dn00xooVKwoLFixQPnd2dhbmzJnz8TdHhYqXl5fg7u4uKBQKZdvYsWMFd3d34c6dOwIA4fTp08ptz549E0xNTYUtW7YIgiAIlStXFiZNmpRr3zmft5zvtVWrVglyuVy039ufraysLMHGxkZYu3atcnu3bt2ELl26CIIgCOnp6UKRIkWEM2fOqPTRt29foVu3bmq/f6JPwUpiIVOlShWV546Ojnjy5AmuXr2K5ORkWFtbK+cHmpubIzo6Gvfu3QMAREZGok6dOiqvf/d5fHw8+vfvDzc3N8jlclhYWCA5OVntioufnx+OHz+O//77D8CbKmbr1q2Vk7qvXr2K1atXq8Tq4+MDhUKB6OhotY5FuulTPqufqlatWirPk5OTMXr0aLi7u8PS0hLm5uaIiIhgJVFP1KtXDxKJRPnc09MTUVFRuHXrFgwNDVG3bl3lNmtra5QvXx4REREAgOHDh2Pq1Klo0KABJk6ciGvXrn1SLIaGhujcuTM2bNgAAEhJScHu3buVoyx3795FamoqvvrqK5Xfj7Vr1xbY7wdRXnE2dyFjZGSk8lwikUChUCA5ORmOjo44fvy46DW5XW33Pv7+/nj+/DnmzZsHZ2dnyGQyeHp6IjMzU604a9euDVdXV2zatAmDBw/Gzp07sXr1auX25ORkDBw4EMOHDxe9tlSpUmodi3TTp3xWpVKpcs5Yjpzhv7wwMzNTeT569GgcOXIEM2fORNmyZWFqaopvvvlG7c816Z9+/frBx8cH+/fvx+HDhxEcHIxZs2Zh2LBh+e7Tz88PXl5eePLkCY4cOQJTU1O0bNkSAJTD0Pv370fx4sVVXsd7Q9PnxiTxC1GjRg3ExcXB0NBQeTHJu8qXL4/z58+jZ8+eyrZ35xSePn0aixYtgq+vLwDg0aNHePbsmco+RkZGyM7O/mhMfn5+2LBhA0qUKAGpVIrWrVurxHvr1i2ULVs2r2+RvhB5+aza2trixo0bKm1XrlxRSTyNjY3z9DkE3nyue/XqpZzzlZycjAcPHuQrfip8wsPDVZ7nzLf28PDA69evER4ejvr16wMAnj9/jsjISHh4eCj3L1myJAYNGoRBgwYhMDAQy5YtyzVJzOtnsn79+ihZsiQ2b96Mv/76C99++63ys+3h4QGZTIaYmBh4eXl9ytsm+mQcbv5CeHt7w9PTE+3bt8fhw4fx4MEDnDlzBuPGjcOFCxcAAMOGDcOKFSuwZs0aREVFYerUqbh27ZrKMIybmxvWrVuHiIgIhIeHw8/PD6ampirHKl26NEJDQxEXF4cXL168NyY/Pz9cunQJ06ZNwzfffKPyV/DYsWNx5swZDB06FFeuXEFUVBR2797NC1f0QF4+q82aNcOFCxewdu1aREVFYeLEiaKksXTp0ggPD8eDBw/w7NkzKBSK9x7Tzc0NO3bswJUrV3D16lV07979g/vTlyUmJgYBAQGIjIzEn3/+iQULFmDEiBFwc3NDu3bt0L9/f5w6dQpXr15Fjx49ULx4cbRr1w7Am6vqDx06hOjoaFy6dAnHjh2Du7t7rscpXbo0kpOTERoaimfPniE1NfW9MXXv3h0hISE4cuSIcqgZAIoWLYrRo0dj1KhRWLNmDe7du4dLly5hwYIFWLNmTcGeGKKPYJL4hZBIJDhw4AAaN26M3r17o1y5cujatSsePnwIe3t7AG+StsDAQIwePRo1atRAdHQ0evXqBRMTE2U/K1aswIsXL1CjRg189913GD58OOzs7FSONWvWLBw5cgQlS5ZE9erV3xtT2bJlUadOHVy7dk3lSxB4M1/txIkTuHPnDho1aoTq1atjwoQJcHJyKsCzQrooL59VHx8fjB8/Hj/++CNq166NV69eqVTAgTdDyAYGBvDw8ICtre0H5xfOnj0bxYoVQ/369dGmTRv4+PigRo0aGn2fpDt69uyJtLQ01KlTB0OGDMGIESOUi1uvWrUKNWvWxNdffw1PT08IgoADBw4oK3vZ2dkYMmQI3N3d0bJlS5QrVw6LFi3K9Tj169fHoEGD0KVLF9ja2mLGjBnvjcnPzw+3bt1C8eLF0aBBA5VtU6ZMwfjx4xEcHKw87v79++Hi4lJAZ4QobyTCuxN/SK989dVXcHBwwLp167QdChFRgWvSpAmqVavG2+IR5QPnJOqR1NRUhISEwMfHBwYGBvjzzz/x999/K9euIyIiIsrBJFGP5AzzTZs2Denp6Shfvjy2b98Ob29vbYdGREREOobDzUREREQkwgtXiIiIiEiESSIRERERiTBJJCIiIiIRJolEREREJMIkkYiIiIhEmCQSUYHp1asX2rdvr3zepEkTjBw58rPHcfz4cUgkEiQmJmrsGO++1/z4HHESEeUXk0SiL1yvXr0gkUggkUhgbGyMsmXLIigoCK9fv9b4sXfs2IEpU6bkad/PnTCVLl2ad+EgIvoALqZNpAdatmyJVatWISMjAwcOHMCQIUNgZGSEwMBA0b6ZmZkwNjYukONaWVkVSD9ERPT5sZJIpAdkMhkcHBzg7OyMwYMHw9vbG3v27AHwv2HTadOmwcnJCeXLlwcAPHr0CJ07d4alpSWsrKzQrl07PHjwQNlndnY2AgICYGlpCWtra/z44494d23+d4ebMzIyMHbsWJQsWRIymQxly5bFihUr8ODBAzRt2hQAUKxYMUgkEvTq1QsAoFAoEBwcDBcXF5iamqJq1arYtm2bynEOHDiAcuXKwdTUFE2bNlWJMz+ys7PRt29f5THLly+PefPm5brv5MmTYWtrCwsLCwwaNAiZmZnKbXmJnYhIV7GSSKSHTE1N8fz5c+Xz0NBQWFhYKO/jnZWVBR8fH3h6euKff/6BoaEhpk6dipYtW+LatWswNjbGrFmzsHr1aqxcuRLu7u6YNWsWdu7ciWbNmr33uD179kRYWBjmz5+PqlWrIjo6Gs+ePUPJkiWxfft2dOrUCZGRkbCwsICpqSkAIDg4GOvXr0dISAjc3Nxw8uRJ9OjRA7a2tvDy8sKjR4/QsWNHDBkyBAMGDMCFCxfwww8/fNL5USgUKFGiBLZu3Qpra2ucOXMGAwYMgKOjIzp37qxy3kxMTHD8+HE8ePAAvXv3hrW1NaZNm5an2ImIdJpARF80f39/oV27doIgCIJCoRCOHDkiyGQyYfTo0crt9vb2QkZGhvI169atE8qXLy8oFAplW0ZGhmBqaiocOnRIEARBcHR0FGbMmKHcnpWVJZQoUUJ5LEEQBC8vL2HEiBGCIAhCZGSkAEA4cuRIrnEeO3ZMACC8ePFC2Zaeni4UKVJEOHPmjMq+ffv2Fbp16yYIgiAEBgYKHh4eKtvHjh0r6utdzs7Owpw5c967/V1DhgwROnXqpHzu7+8vWFlZCSkpKcq2xYsXC+bm5kJ2dnaeYs/tPRMR6QpWEon0wL59+2Bubo6srCwoFAp0794dkyZNUm6vXLmyyjzEq1ev4u7duyhatKhKP+np6bh37x5evnyJ2NhY1K1bV7nN0NAQtWrVEg0557hy5QoMDAzUqqDdvXsXqamp+Oqrr1TaMzMzUb16dQBARESEShwA4OnpmedjvM/ChQuxcuVKxMTEIC0tDZmZmahWrZrKPlWrVkWRIkVUjpucnIxHjx4hOTn5o7ETEekyJolEeqBp06ZYvHgxjI2N4eTkBEND1V99MzMzlefJycmoWbMmNmzYIOrL1tY2XzHkDB+rIzk5GQCwf/9+FC9eXGWbTCbLVxx5sWnTJowePRqzZs2Cp6cnihYtit9//x3h4eF57kNbsRMRFRQmiUR6wMzMDGXLls3z/jVq1MDmzZthZ2cHCwuLXPdxdHREeHg4GjduDAB4/fo1Ll68iBo1auS6f+XKlaFQKHDixAl4e3uLtudUMrOzs5VtHh4ekMlkiImJeW8F0t3dXXkRTo6zZ89+/E1+wOnTp1G/fn18//33yrZ79+6J9rt69SrS0tKUCfDZs2dhbm6OkiVLwsrK6qOxExHpMl7dTEQifn5+sLGxQbt27fDPP/8gOjoax48fx/Dhw/Hvv/8CAEaMGIFff/0Vu3btwu3bt/H9999/cI3D0qVLw9/fH3369MGuXbuUfW7ZsgUA4OzsDIlEgn379uHp06dITk5G0aJFMXr0aIwaNQpr1qzBvXv3cOnSJSxYsABr1qwBAAwaNAhRUVEYM2YMIiMjsXHjRqxevTpP7/Px48e4cuWKyuPFixdwc3PDhQsXcOjQIdy5cwfjx4/H+fPnRa/PzMxE3759cevWLRw4cAATJ07E0KFDIZVK8xQ7EZFO0/akSCLSrLcvXFFne2xsrNCzZ0/BxsZGkMlkQpkyZYT+/fsLL1++FAThzYUqI0aMECwsLARLS0shICBA6Nmz53svXBEEQUhLSxNGjRolODo6CsbGxkLZsmWFlStXKrcHBQUJDg4OgkQiEfz9/QVBeHOxzdy5c4Xy5csLRkZGgq2treDj4yOcOHFC+bq9e/cKZcuWFWQymdCoUSNh5cqVebpwBYDosW7dOiE9PV3o1auXIJfLBUtLS2Hw4MHCTz/9JFStWlV03iZMmCBYW1sL5ubmQv/+/YX09HTlPh+LnReuEJEukwjCe2aZExEREZHe4nAzEREREYkwSSQiIiIiESaJRERERCTCJJGIiIiIRJgkEhEREZEIk0QiIiIiEmGSSEREREQiTBKJiIiISIRJIhERERGJMEkkIiIiIhEmiUREREQk8n/21Ge62JKP1QAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Generate predictions on the test data\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Create the confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\n",
        "plt.title(\"Confusion Matrix for Financial Sentiment Classifier\")\n",
        "plt.xlabel(\"Predicted Label\")\n",
        "plt.ylabel(\"True Label\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v8DE8aAzg4jQ",
        "outputId": "5ce78149-c53b-45f3-994f-5f6c7d21b819"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted Label: neutral\n"
          ]
        }
      ],
      "source": [
        "new_texts = [\"The company is doing OK\"]\n",
        "new_embeddings = model.encode(new_texts, convert_to_numpy=True)\n",
        "predicted_label = clf.predict(new_embeddings)\n",
        "\n",
        "# Convert back to original label names\n",
        "decoded_label = label_encoder.inverse_transform(predicted_label)\n",
        "print(\"Predicted Label:\", decoded_label[0])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['random_forest_classifier.pkl']"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Save SBERT model\n",
        "\n",
        "\n",
        "# Save RandomForest model\n",
        "import joblib\n",
        "joblib.dump(clf, \"random_forest_classifier.pkl\")  # Save the trained classifier model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: transformers in /opt/anaconda3/lib/python3.12/site-packages (4.45.2)\n",
            "Requirement already satisfied: datasets in /opt/anaconda3/lib/python3.12/site-packages (3.1.0)\n",
            "Requirement already satisfied: huggingface_hub in /opt/anaconda3/lib/python3.12/site-packages (0.26.2)\n",
            "Requirement already satisfied: filelock in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (3.13.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (2.0.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (2023.10.3)\n",
            "Requirement already satisfied: requests in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (0.4.5)\n",
            "Requirement already satisfied: tokenizers<0.21,>=0.20 in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (0.20.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /opt/anaconda3/lib/python3.12/site-packages (from transformers) (4.66.4)\n",
            "Requirement already satisfied: pyarrow>=15.0.0 in /opt/anaconda3/lib/python3.12/site-packages (from datasets) (17.0.0)\n",
            "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /opt/anaconda3/lib/python3.12/site-packages (from datasets) (0.3.8)\n",
            "Requirement already satisfied: pandas in /opt/anaconda3/lib/python3.12/site-packages (from datasets) (2.2.3)\n",
            "Requirement already satisfied: xxhash in /opt/anaconda3/lib/python3.12/site-packages (from datasets) (3.5.0)\n",
            "Requirement already satisfied: multiprocess<0.70.17 in /opt/anaconda3/lib/python3.12/site-packages (from datasets) (0.70.16)\n",
            "Requirement already satisfied: fsspec<=2024.9.0,>=2023.1.0 in /opt/anaconda3/lib/python3.12/site-packages (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets) (2024.3.1)\n",
            "Requirement already satisfied: aiohttp in /opt/anaconda3/lib/python3.12/site-packages (from datasets) (3.9.5)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/anaconda3/lib/python3.12/site-packages (from huggingface_hub) (4.12.2)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /opt/anaconda3/lib/python3.12/site-packages (from aiohttp->datasets) (1.2.0)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /opt/anaconda3/lib/python3.12/site-packages (from aiohttp->datasets) (23.1.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /opt/anaconda3/lib/python3.12/site-packages (from aiohttp->datasets) (1.4.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/anaconda3/lib/python3.12/site-packages (from aiohttp->datasets) (6.0.4)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/anaconda3/lib/python3.12/site-packages (from aiohttp->datasets) (1.9.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/anaconda3/lib/python3.12/site-packages (from requests->transformers) (2.0.4)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /opt/anaconda3/lib/python3.12/site-packages (from requests->transformers) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/anaconda3/lib/python3.12/site-packages (from requests->transformers) (2.2.2)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /opt/anaconda3/lib/python3.12/site-packages (from requests->transformers) (2024.8.30)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/anaconda3/lib/python3.12/site-packages (from pandas->datasets) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/lib/python3.12/site-packages (from pandas->datasets) (2024.1)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /opt/anaconda3/lib/python3.12/site-packages (from pandas->datasets) (2023.3)\n",
            "Requirement already satisfied: six>=1.5 in /opt/anaconda3/lib/python3.12/site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers datasets huggingface_hub\n",
        "\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}