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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import tensorflow as tf\n",
        "from transformers import AutoTokenizer,AutoModelForSeq2SeqLM, pipeline,BertTokenizer,logging\n",
        "from huggingface_hub import from_pretrained_keras\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "logging.set_verbosity_error()"
      ],
      "metadata": {
        "id": "T0WCBU9jYLKm"
      },
      "execution_count": 34,
      "outputs": []
    },
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      "source": [
        "topics = pd.read_csv('data/topics.csv')\n",
        "conclusions = pd.read_csv('data/conclusions.csv')\n",
        "opinions = pd.read_csv('data/opinions.csv')\n",
        "\n",
        "df = pd.merge(topics[['topic_id','text']],conclusions[['topic_id','text']],on='topic_id')\n",
        "df = pd.merge(df, opinions[['topic_id','text','type','effectiveness']], on='topic_id')\n",
        "df.rename({'text_x':'topics','text_y':'conclusions','text':'opinions'}, axis='columns',inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "VekVpLzjirfC",
        "outputId": "3282495a-8394-42a6-9350-958cf44f0394"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
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              "           topic_id                                             topics  \\\n",
              "0      007ACE74B050  On my perspective, I think that the face is a ...   \n",
              "1      007ACE74B050  On my perspective, I think that the face is a ...   \n",
              "2      007ACE74B050  On my perspective, I think that the face is a ...   \n",
              "3      007ACE74B050  On my perspective, I think that the face is a ...   \n",
              "4      007ACE74B050  On my perspective, I think that the face is a ...   \n",
              "...             ...                                                ...   \n",
              "23349  FF9E0379CD98  I think is not benefit for student, that take ...   \n",
              "23350  FF9E0379CD98  I think is not benefit for student, that take ...   \n",
              "23351  FFA381E58FC6  Advice is something that can impact a persons ...   \n",
              "23352  FFA381E58FC6  Advice is something that can impact a persons ...   \n",
              "23353  FFA381E58FC6  Advice is something that can impact a persons ...   \n",
              "\n",
              "                                             conclusions  \\\n",
              "0      Though people were not satified about how the ...   \n",
              "1      Though people were not satified about how the ...   \n",
              "2      Though people were not satified about how the ...   \n",
              "3      Though people were not satified about how the ...   \n",
              "4      Though people were not satified about how the ...   \n",
              "...                                                  ...   \n",
              "23349  Conclusion: you dont are prepared for take the...   \n",
              "23350  Conclusion: you dont are prepared for take the...   \n",
              "23351  In conclusion asking for an opinion can be ben...   \n",
              "23352  In conclusion asking for an opinion can be ben...   \n",
              "23353  In conclusion asking for an opinion can be ben...   \n",
              "\n",
              "                                                opinions          type  \\\n",
              "0      I think that the face is a natural landform be...         Claim   \n",
              "1      If life was on Mars, we would know by now. The...      Evidence   \n",
              "2      People thought that the face was formed by ali...  Counterclaim   \n",
              "3      though some say that life on Mars does exist, ...      Rebuttal   \n",
              "4      It says in paragraph 7, on April 5, 1998, Mars...      Evidence   \n",
              "...                                                  ...           ...   \n",
              "23349  you cant work or cant study after school with ...      Evidence   \n",
              "23350  in the end you cant take the class because is ...  Counterclaim   \n",
              "23351  For many people they don't like only asking on...         Claim   \n",
              "23352  also people have different views and opinions ...         Claim   \n",
              "23353  someone can use everything that many people sa...      Evidence   \n",
              "\n",
              "      effectiveness  \n",
              "0          Adequate  \n",
              "1          Adequate  \n",
              "2          Adequate  \n",
              "3       Ineffective  \n",
              "4          Adequate  \n",
              "...             ...  \n",
              "23349      Adequate  \n",
              "23350      Adequate  \n",
              "23351      Adequate  \n",
              "23352      Adequate  \n",
              "23353   Ineffective  \n",
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              "[23354 rows x 6 columns]"
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
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              "    <style>\n",
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              "      }\n",
              "\n",
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              "        fill: #174EA6;\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        background-color: #434B5C;\n",
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              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
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          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "-----------------------------\n",
        "\n",
        "## Summarization\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "iw72v0AZfAzP"
      }
    },
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      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.66k [00:00<?, ?B/s]"
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              "model.safetensors:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"
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        {
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        {
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        {
          "output_type": "display_data",
          "data": {
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              "tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "afab5e43cae14479adf38dd68f1c31c9"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "model_summarizer = AutoModelForSeq2SeqLM.from_pretrained(\"necover/fine_tuned_bartbasedmodel_concluions\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"necover/fine_tuned_bartbasedtokenizer_concluions\")\n",
        "\n",
        "summarization_pipeline = pipeline(\"summarization\", model=model_summarizer, tokenizer=tokenizer)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input =df['topics'][200]\n",
        "output = summarization_pipeline(input)[0]['summary_text']\n",
        "print(f'Input :{input}\\nOutput :{output}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TmPY8awgYffW",
        "outputId": "e41476bc-e3a6-4446-aeb9-f7eb7bdd00e7"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input :Emplimenting a computer system in schools that is capable of reading a students emotions is inappropriate, lazy, and dangerous. \n",
            "Output :In conclusion, computers in schools should not be able to read a student's emotions. Emplimenting a computer system in schools that is capable of reading a students emotions is inappropriate, lazy, and dangerous. It is a waste of money, time, and money that should be spent on other things. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input = 'Limiting our usage of any type of motorized vehicle, would not only decrease air pollution but it would also help with adults or children that may not exercise enough.'\n",
        "output = summarization_pipeline(input)[0]['summary_text']\n",
        "print(f'Input :{input}\\nOutput :{output}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3KiZgygvEr8",
        "outputId": "3c044433-45d9-4f79-bb16-0f63d1558dd5"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input :Limiting our usage of any type of motorized vehicle, would not only decrease air pollution but it would also help with adults or children that may not exercise enough.\n",
            "Output : would also help with adults or children that may not exercise enough. Limiting our usage of any type of motorized vehicle, would not only decrease air pollution but it would also improve our health. It would be a good idea to limit the amount of air pollution in the United States, and it would help with the health of adults and children.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "-------------------\n",
        "\n",
        "##**Classifier**"
      ],
      "metadata": {
        "id": "eH-h38zYf4tZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labels_similarity = ['Effective', 'Adequate', 'Ineffective']\n",
        "labels_type = ['Claim','Evidence','Counterclaim','Rebuttal']"
      ],
      "metadata": {
        "id": "LdedvAgdh6M5"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_effectiveness = from_pretrained_keras(\"necover/Bert_Based_effectiveness_classifier_V2\")\n",
        "model_type = from_pretrained_keras(\"necover/Bert_Based_type_classifier_V2\")"
      ],
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        {
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          "data": {
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            "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
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          "text": [
            "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
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    {
      "cell_type": "code",
      "source": [
        "class BertSemanticDataGenerator(tf.keras.utils.Sequence):\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        sentence_pairs,\n",
        "        labels,\n",
        "        batch_size=32,\n",
        "        shuffle=True,\n",
        "        include_targets=True,\n",
        "    ):\n",
        "        self.sentence_pairs = sentence_pairs\n",
        "        self.labels = labels\n",
        "        self.shuffle = shuffle\n",
        "        self.batch_size = batch_size\n",
        "        self.include_targets = include_targets\n",
        "        # Load our BERT Tokenizer to encode the text.\n",
        "        # We will use base-base-uncased pretrained model.\n",
        "        self.tokenizer = BertTokenizer.from_pretrained(\n",
        "            \"bert-base-uncased\", do_lower_case=True\n",
        "        )\n",
        "        self.indexes = np.arange(len(self.sentence_pairs))\n",
        "        self.on_epoch_end()\n",
        "\n",
        "    def __len__(self):\n",
        "        # Denotes the number of batches per epoch.\n",
        "        return len(self.sentence_pairs) // self.batch_size\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        # Retrieves the batch of index.\n",
        "        indexes = self.indexes[idx * self.batch_size : (idx + 1) * self.batch_size]\n",
        "        sentence_pairs = self.sentence_pairs[indexes]\n",
        "\n",
        "        # With BERT tokenizer's batch_encode_plus batch of both the sentences are\n",
        "        # encoded together and separated by [SEP] token.\n",
        "        encoded = self.tokenizer.batch_encode_plus(\n",
        "            sentence_pairs.tolist(),\n",
        "            add_special_tokens=True,\n",
        "            max_length=256,\n",
        "            return_attention_mask=True,\n",
        "            return_token_type_ids=True,\n",
        "            pad_to_max_length=True,\n",
        "            truncation=True,\n",
        "            return_tensors=\"tf\",\n",
        "        )\n",
        "\n",
        "        # Convert batch of encoded features to numpy array.\n",
        "        input_ids = np.array(encoded[\"input_ids\"], dtype=\"int32\")\n",
        "        attention_masks = np.array(encoded[\"attention_mask\"], dtype=\"int32\")\n",
        "        token_type_ids = np.array(encoded[\"token_type_ids\"], dtype=\"int32\")\n",
        "\n",
        "        # Set to true if data generator is used for training/validation.\n",
        "        if self.include_targets:\n",
        "            labels = np.array(self.labels[indexes], dtype=\"int32\")\n",
        "            return [input_ids, attention_masks, token_type_ids], labels\n",
        "        else:\n",
        "            return [input_ids, attention_masks, token_type_ids]\n",
        "\n",
        "    def on_epoch_end(self):\n",
        "        # Shuffle indexes after each epoch if shuffle is set to True.\n",
        "        if self.shuffle:\n",
        "            np.random.RandomState(42).shuffle(self.indexes)"
      ],
      "metadata": {
        "id": "-_lFxemvgla6"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def check(sentence1, sentence2, model, label_list):\n",
        "    sentence_pairs = np.array([[str(sentence1), str(sentence2)]])\n",
        "    test_data = BertSemanticDataGenerator(\n",
        "        sentence_pairs, labels=None, batch_size=1, shuffle=False, include_targets=False,\n",
        "    )\n",
        "\n",
        "    proba = model.predict(test_data[0])[0]\n",
        "    idx = np.argmax(proba)\n",
        "    print(idx)\n",
        "    proba = f\"{proba[idx]: .2f}%\"\n",
        "    pred = label_list[idx]\n",
        "    return pred, proba"
      ],
      "metadata": {
        "id": "mRHrjpevgcrg"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "opinions.iloc[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_r_lktBvjUFP",
        "outputId": "03ecec43-5434-4087-8a46-485c2dec777f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                                    c22adee811b6\n",
              "topic_id                                              007ACE74B050\n",
              "text             I think that the face is a natural landform be...\n",
              "type                                                         Claim\n",
              "effectiveness                                             Adequate\n",
              "Name: 0, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "-nOZgDDtsGu_",
        "outputId": "75cd118d-3ad0-44d7-c40d-934aaf54735d"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           topic_id                                             topics  \\\n",
              "0      007ACE74B050  On my perspective, I think that the face is a ...   \n",
              "1      007ACE74B050  On my perspective, I think that the face is a ...   \n",
              "2      007ACE74B050  On my perspective, I think that the face is a ...   \n",
              "3      007ACE74B050  On my perspective, I think that the face is a ...   \n",
              "4      007ACE74B050  On my perspective, I think that the face is a ...   \n",
              "...             ...                                                ...   \n",
              "23349  FF9E0379CD98  I think is not benefit for student, that take ...   \n",
              "23350  FF9E0379CD98  I think is not benefit for student, that take ...   \n",
              "23351  FFA381E58FC6  Advice is something that can impact a persons ...   \n",
              "23352  FFA381E58FC6  Advice is something that can impact a persons ...   \n",
              "23353  FFA381E58FC6  Advice is something that can impact a persons ...   \n",
              "\n",
              "                                             conclusions  \\\n",
              "0      Though people were not satified about how the ...   \n",
              "1      Though people were not satified about how the ...   \n",
              "2      Though people were not satified about how the ...   \n",
              "3      Though people were not satified about how the ...   \n",
              "4      Though people were not satified about how the ...   \n",
              "...                                                  ...   \n",
              "23349  Conclusion: you dont are prepared for take the...   \n",
              "23350  Conclusion: you dont are prepared for take the...   \n",
              "23351  In conclusion asking for an opinion can be ben...   \n",
              "23352  In conclusion asking for an opinion can be ben...   \n",
              "23353  In conclusion asking for an opinion can be ben...   \n",
              "\n",
              "                                                opinions          type  \n",
              "0      I think that the face is a natural landform be...         Claim  \n",
              "1      If life was on Mars, we would know by now. The...      Evidence  \n",
              "2      People thought that the face was formed by ali...  Counterclaim  \n",
              "3      though some say that life on Mars does exist, ...      Rebuttal  \n",
              "4      It says in paragraph 7, on April 5, 1998, Mars...      Evidence  \n",
              "...                                                  ...           ...  \n",
              "23349  you cant work or cant study after school with ...      Evidence  \n",
              "23350  in the end you cant take the class because is ...  Counterclaim  \n",
              "23351  For many people they don't like only asking on...         Claim  \n",
              "23352  also people have different views and opinions ...         Claim  \n",
              "23353  someone can use everything that many people sa...      Evidence  \n",
              "\n",
              "[23354 rows x 5 columns]"
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-74f55943-5fc4-4f42-b638-f4cb6ce03643 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-74f55943-5fc4-4f42-b638-f4cb6ce03643');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dbcfcd03-fd39-417c-a5a3-05d64d819cfe\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dbcfcd03-fd39-417c-a5a3-05d64d819cfe')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dbcfcd03-fd39-417c-a5a3-05d64d819cfe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7f8bfc56-1347-4564-8b7f-0e3d880acd96\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7f8bfc56-1347-4564-8b7f-0e3d880acd96 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx = 200\n",
        "s1 = df['topics'][idx]\n",
        "s2 = df['opinions'][idx]\n",
        "ef_test = df['effectiveness'][idx]\n",
        "ty_test = df['type'][idx]\n",
        "\n",
        "ef_pred, ef_prob = check(s1, s2, model_effectiveness, labels_similarity)\n",
        "ty_pred, ty_prob = check(s1, s2, model_type, labels_type)\n",
        "print(f\"Topic :{s1}\\nOpinion : {s2}\\nEffectiveness: {ef_test}\\tty_pred : {ty_test}\\nPredicted Effectiveness: {ef_pred}\\tPredicted Type: {ty_pred}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2-mPhu2qUWt",
        "outputId": "b76a0ee1-5beb-4cd9-85c3-9358a4870039"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n",
            "0\n",
            "1/1 [==============================] - 2s 2s/step\n",
            "0\n",
            "Topic :Emplimenting a computer system in schools that is capable of reading a students emotions is inappropriate, lazy, and dangerous. \n",
            "Opinion : In school, students are faced with one major task: to learn. Teachers are faced with the task of teaching. A teacher's job is to engage students and the students job is to stay engaged with the teacher. \n",
            "Effectiveness: Effective\tty_pred : Evidence\n",
            "Predicted Effectiveness: Effective\tPredicted Type: Claim\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sentence1 = \"Car-free cities seem to be more safe.\"\n",
        "sentence2 = \"make the streets safer \"\n",
        "check(sentence1, sentence2, model_effectiveness, labels_similarity),check(sentence1, sentence2, model_type, labels_type)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZkwoxjJthwDu",
        "outputId": "6534a82d-0d22-4ed7-d8ae-5cde790158d5"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 3s 3s/step\n",
            "1\n",
            "1/1 [==============================] - 4s 4s/step\n",
            "0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(('Adequate', ' 0.82%'), ('Claim', ' 0.99%'))"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hWammjdAuj6e"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}