final_pipeline_news.ipynb

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        "outputId": "e50f1dd5-89fb-4a4c-bd79-09dff78284f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGPeYw6DO84d",
        "outputId": "4e9e250b-a379-4dd7-aaaf-1ad3dd7826c2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/IR_Project/News\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/IR_Project/News/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DSDDeFvS5kdx"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "import pandas as pd\n",
        "from pprint import pprint\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56aZj9MH4HYv"
      },
      "source": [
        "## **Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CFyMvdDm4Gpr"
      },
      "outputs": [],
      "source": [
        "with open('train_questions.json') as f:\n",
        "    d = json.load(f)\n",
        "data = pd.DataFrame.from_dict(d)\n",
        "\n",
        "with open('train_outputs.json') as f:\n",
        "    t = json.load(f)\n",
        "\n",
        "outputs=pd.DataFrame.from_dict(t['golds'])\n",
        "final_data=pd.merge(data, outputs, on='id')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 81
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        "outputId": "52a5af95-8658-4e76-95d9-9c5e0d8bdba8"
      },
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>input</th>\n",
              "      <th>profile</th>\n",
              "      <th>output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>300</td>\n",
              "      <td>Generate a headline for the following article:...</td>\n",
              "      <td>[{'text': 'Homeowners sell their homes and buy...</td>\n",
              "      <td>The Pre-Crisis HELOC Chickens Are Coming Home ...</td>\n",
              "    </tr>\n",
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              "          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",
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            ],
            "text/plain": [
              "    id                                              input  \\\n",
              "0  300  Generate a headline for the following article:...   \n",
              "\n",
              "                                             profile  \\\n",
              "0  [{'text': 'Homeowners sell their homes and buy...   \n",
              "\n",
              "                                              output  \n",
              "0  The Pre-Crisis HELOC Chickens Are Coming Home ...  "
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "final_data.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8HCMdrHl4GlA",
        "outputId": "afd3bff9-eccb-4e58-8a98-09e4d314cb4b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "id                                                       300\n",
            "input      Generate a headline for the following article:...\n",
            "profile    [{'text': 'Homeowners sell their homes and buy...\n",
            "output     The Pre-Crisis HELOC Chickens Are Coming Home ...\n",
            "Name: 0, dtype: object\n"
          ]
        }
      ],
      "source": [
        "print(final_data.iloc[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eTVJ4d6f4km6"
      },
      "outputs": [],
      "source": [
        "#making my testing sample\n",
        "test_data=final_data.sample(100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Eg5LQIUSk2dE"
      },
      "outputs": [],
      "source": [
        "test_data=pd.read_pickle('preprompt_100.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MP2dnge9mF4c",
        "outputId": "9d91fd18-0c2e-4428-fa56-e7217f47dfa0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['id', 'input', 'profile', 'output', 'bm25_results', 'keyword_results',\n",
              "       'summary_results', 'style_results', 'input_keywords', 'input_summary',\n",
              "       'prompts'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lv_7rhFlmXCm"
      },
      "outputs": [],
      "source": [
        "test_data=test_data[['id', 'input', 'profile', 'output', 'input_keywords', 'input_summary',]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x1EO80My0oYf",
        "outputId": "9b07afb4-e938-4a6c-d551-2b8dcb24704d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1229"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(test_data.iloc[3]['profile'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HDzbw5_74JAu"
      },
      "source": [
        "# STEP1: Retriever"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B9DW50cZmsF8"
      },
      "source": [
        "### bm25"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gcTGAk5x2eHv"
      },
      "outputs": [],
      "source": [
        "!pip install langchain\n",
        "!pip install rank_bm25"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9phXc2VH2ovV"
      },
      "outputs": [],
      "source": [
        "from langchain.schema import Document\n",
        "from langchain.retrievers import BM25Retriever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K3RGa3iU2thg"
      },
      "outputs": [],
      "source": [
        "def get_relevant_docs(query : str, docs: list):\n",
        "  documents =  []\n",
        "\n",
        "  for item in tqdm(docs):\n",
        "      page = Document(page_content=item['text'],metadata = {\"id\" : item['id'], \"title\" : item['title']})\n",
        "      documents.append(page)\n",
        "\n",
        "  retriever = BM25Retriever.from_documents(documents)\n",
        "  top_docs=retriever.get_relevant_documents(query)[:3]\n",
        "  top3=[]\n",
        "  for i in top_docs[:3]:\n",
        "    res_dict={'text':i.page_content,'title':i.metadata['title'],'id':i.metadata['id']}\n",
        "    top3.append(res_dict)\n",
        "  return top3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kiINWHip422z",
        "outputId": "077ea508-81ea-4067-89e7-f4784ad1e161"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [00:03<00:00, 27.20it/s]\n"
          ]
        }
      ],
      "source": [
        "retriever_results=[]\n",
        "for row in tqdm(range(test_data.shape[0])):\n",
        "  input_text = test_data.iloc[row]['input']\n",
        "  top_ret=get_relevant_docs(input_text, test_data.iloc[row]['profile'])\n",
        "  retriever_results.append(top_ret)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CiwST00e5ZEy"
      },
      "outputs": [],
      "source": [
        "test_data['bm25_results']=retriever_results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I-Jfkq52muY-"
      },
      "source": [
        "### contriever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ItdKgtFIn093"
      },
      "outputs": [],
      "source": [
        "!pip install --no-cache-dir transformers sentencepiece\n",
        "!pip install faiss-gpu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JvwHV8RZ66Ar"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3qlHcrmOstUQ"
      },
      "outputs": [],
      "source": [
        "#got contriever results from python file on local machine"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-ElXP2Ko3yxF"
      },
      "outputs": [],
      "source": [
        "test_data=pd.read_pickle('cont_res_100.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "461rr8Nh4HzY",
        "outputId": "84656dd7-924a-4168-f3b4-22aa8c36d01a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'text': \"Parents: We are not there to make life easy on our kids. It's our job to help them grow up with the skills to become successful adults.\",\n",
              "  'title': \"Growing a Generation Who Doesn't know How to Fail\",\n",
              "  'id': '3019091003'},\n",
              " {'text': 'Teaching our kids to say \"thank you\" is important, but truly instilling a sense of gratitude in them is another matter entirely. Gratitude goes beyond good manners -- it\\'s a mindset and a lifestyle.',\n",
              "  'title': '11 Tips for Instilling True Gratitude in Your Kids',\n",
              "  'id': '301909793'},\n",
              " {'text': \"A friendly reminder from science that the flu vaccine isn't linked to autism.\",\n",
              "  'title': 'PSA: Pregnant Women Should Still Get Flu Shots',\n",
              "  'id': '301909365'}]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.iloc[0]['contriever']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2J5BX9kn552x"
      },
      "source": [
        "# STEP 2 KEYWORD EXTRACTION"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aZn4ZWoV58ka",
        "outputId": "6ef96d52-3a71-444b-b00e-1578c01ad77c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting keybert\n",
            "  Downloading keybert-0.8.3.tar.gz (29 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting sentence-transformers>=0.3.8 (from keybert)\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: scikit-learn>=0.22.2 in /usr/local/lib/python3.10/dist-packages (from keybert) (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from keybert) (1.23.5)\n",
            "Requirement already satisfied: rich>=10.4.0 in /usr/local/lib/python3.10/dist-packages (from keybert) (13.7.0)\n",
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            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22.2->keybert) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22.2->keybert) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22.2->keybert) (3.2.0)\n",
            "Requirement already satisfied: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=0.3.8->keybert) (4.35.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=0.3.8->keybert) (4.66.1)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=0.3.8->keybert) (2.1.0+cu118)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=0.3.8->keybert) (0.16.0+cu118)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=0.3.8->keybert) (3.8.1)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=0.3.8->keybert) (0.1.99)\n",
            "Requirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=0.3.8->keybert) (0.19.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (3.13.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (2.31.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (23.2)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.4.0->keybert) (0.1.2)\n",
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            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers>=0.3.8->keybert) (2.1.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=0.3.8->keybert) (2023.6.3)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=0.3.8->keybert) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=0.3.8->keybert) (0.4.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->sentence-transformers>=0.3.8->keybert) (8.1.7)\n",
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            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.6.0->sentence-transformers>=0.3.8->keybert) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (2023.11.17)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6.0->sentence-transformers>=0.3.8->keybert) (1.3.0)\n",
            "Building wheels for collected packages: keybert, sentence-transformers\n",
            "  Building wheel for keybert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keybert: filename=keybert-0.8.3-py3-none-any.whl size=39124 sha256=b8617b7b1fe28938ec9b02c1a2e1ee86defe69ce8ac1b6d2c83f083563a388e8\n",
            "  Stored in directory: /root/.cache/pip/wheels/70/88/07/1a3bc11fd1dd5f89924a02dcbca89a3015e25e8faa31f904dc\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125923 sha256=9d5f948c9d47203b7cbc4f77938bc4b7dda5cf7b7c3cbb1680b11cb735a6c89e\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built keybert sentence-transformers\n",
            "Installing collected packages: sentence-transformers, keybert\n",
            "Successfully installed keybert-0.8.3 sentence-transformers-2.2.2\n"
          ]
        }
      ],
      "source": [
        "!pip install keybert"
      ]
    },
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            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
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          },
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        {
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              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
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        {
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        {
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          },
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        },
        {
          "data": {
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        },
        {
          "data": {
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              "model_id": "1643c90d9e994d6eb2c5180cfcd7d1ce",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0c736b7c1a1f47c0939f562e5044433f",
              "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": "319ad5f059954bdca02d5aa8b574fb59",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from keybert import KeyBERT\n",
        "kw_model = KeyBERT()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "PJPFihZzQNcS"
      },
      "outputs": [],
      "source": [
        "#kw_model.extract_keywords(doc, stop_words='english',use_mmr=True, diversity=0.3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eOfpIQHa6au_",
        "outputId": "1c39bb4b-ab60-4de1-b96a-c7a8b1042561"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [00:22<00:00,  4.51it/s]\n"
          ]
        }
      ],
      "source": [
        "keyword_results=[]\n",
        "for row in tqdm(range(test_data.shape[0])):\n",
        "  #cur_list = test_data.iloc[row]['bm25_results'] #ifbm25 run this\n",
        "  cur_list = test_data.iloc[row]['contriever']\n",
        "  updated_list = [{'text': entry['text'], 'title': entry['title'], 'id': entry['id'],'keywords': kw_model.extract_keywords(entry['text'],, stop_words='english',use_mmr=True, diversity=0.3)} for entry in cur_list]\n",
        "\n",
        "  #updated_list = [{'text': entry['text'], 'title': entry['title'], 'id': entry['id'],'keywords': kw_model.extract_keywords(entry['text'])} for entry in cur_list]\n",
        "  keyword_results.append(updated_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x2KK5lt0_eYd"
      },
      "outputs": [],
      "source": [
        "test_data['keyword_results']=keyword_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "KIcym1xHAOGB",
        "outputId": "a01960bd-3a18-426e-fd35-c2fcf0fccc65"
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>input</th>\n",
              "      <th>profile</th>\n",
              "      <th>output</th>\n",
              "      <th>input_keywords</th>\n",
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              "      <th>contriever</th>\n",
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              "  <tbody>\n",
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              "      <th>1909</th>\n",
              "      <td>301909</td>\n",
              "      <td>Generate a headline for the following article:...</td>\n",
              "      <td>[{'text': 'Bryant's widow testified that she \"...</td>\n",
              "      <td>33 Phrases To Stop Your Toddler's Tantrums</td>\n",
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              "      <td>Beyonce: Met Gala 2013 Honorary Chair!</td>\n",
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              "      <td>FEARLESS MEMOIR: 'World's Fair' (Chapter 18)</td>\n",
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              "      <td>Generate a headline for the following article:...</td>\n",
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              "      <td>Buzz Aldrin Looked As Baffled As We Felt Durin...</td>\n",
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              "      <td>The coffee chain says its employees called for...</td>\n",
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              "\n",
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              "\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "    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",
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              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
              "    60% {\n",
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              "      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-ec4633b3-6c70-4e76-ab5a-984ea0e865be button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            id                                              input  \\\n",
              "1909    301909  Generate a headline for the following article:...   \n",
              "7215    307215  Generate a headline for the following article:...   \n",
              "693      30693  Generate a headline for the following article:...   \n",
              "2837    302837  Generate a headline for the following article:...   \n",
              "10676  3010676  Generate a headline for the following article:...   \n",
              "\n",
              "                                                 profile  \\\n",
              "1909   [{'text': 'Bryant's widow testified that she \"...   \n",
              "7215   [{'text': '5. Coming with your significant oth...   \n",
              "693    [{'text': '\"Drivin down the wrong side of the ...   \n",
              "2837   [{'text': 'He was subdued by passengers and cr...   \n",
              "10676  [{'text': 'Certain points during your day can ...   \n",
              "\n",
              "                                                  output  \\\n",
              "1909          33 Phrases To Stop Your Toddler's Tantrums   \n",
              "7215              Beyonce: Met Gala 2013 Honorary Chair!   \n",
              "693         FEARLESS MEMOIR: 'World's Fair' (Chapter 18)   \n",
              "2837   Buzz Aldrin Looked As Baffled As We Felt Durin...   \n",
              "10676  Starbucks To Stop Throwing Out Perfectly Good ...   \n",
              "\n",
              "                                         input_keywords  \\\n",
              "1909   tantrums, toddler, parenting, cooperation, smile   \n",
              "7215           beyonce, gala, chair, costume, institute   \n",
              "693                    feet, claws, fear, terror, budge   \n",
              "2837                 infinity, right, don, know, really   \n",
              "10676        donations, coffee, employees, food, asking   \n",
              "\n",
              "                                           input_summary  \\\n",
              "1909   Here are some parenting phrases that can help ...   \n",
              "7215   Do you think Beyonce is a good choice to co-ch...   \n",
              "693    My feet refused to move; they were glued to th...   \n",
              "2837   \"This is infinity here. It could be infinity. ...   \n",
              "10676  The coffee chain says its employees called for...   \n",
              "\n",
              "                                              contriever  \\\n",
              "1909   [{'text': 'Parents: We are not there to make l...   \n",
              "7215   [{'text': 'Editor's Note: Previous references ...   \n",
              "693    [{'text': 'All I could think about was Daddy. ...   \n",
              "2837   [{'text': '\"The article is an absolutely disgu...   \n",
              "10676  [{'text': 'This is a brutal reminder that glob...   \n",
              "\n",
              "                                         keyword_results  \n",
              "1909   [{'text': 'Parents: We are not there to make l...  \n",
              "7215   [{'text': 'Editor's Note: Previous references ...  \n",
              "693    [{'text': 'All I could think about was Daddy. ...  \n",
              "2837   [{'text': '\"The article is an absolutely disgu...  \n",
              "10676  [{'text': 'This is a brutal reminder that glob...  "
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ar3qxn-mA8oJ"
      },
      "outputs": [],
      "source": [
        "test_data.to_pickle('contriever/test_data_cont_100.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S4KQARQzENIb"
      },
      "source": [
        "#  STEP 3 SUMMARIZER"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F6hN9jXxMCi9"
      },
      "outputs": [],
      "source": [
        "test_data=pd.read_pickle('contriever/test_data_cont_100.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q7rc5mndEPW6",
        "outputId": "22a9ce41-0be9-4124-d41a-3c4428d0d972"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.11.17)\n",
            "Installing collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n"
          ]
        }
      ],
      "source": [
        "!pip install --no-cache-dir transformers sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5P7Cww3KKcWb",
        "outputId": "d73a95a5-cf2e-48c8-f49d-86be1ee6bf9e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of PegasusForConditionalGeneration were not initialized from the model checkpoint at ../pegasus-cnn_dailymail and are newly initialized: ['model.decoder.embed_positions.weight', 'model.encoder.embed_positions.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "import torch\n",
        "\n",
        "# Ensure CUDA is available and set the device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Path to your downloaded model\n",
        "model_path = \"../pegasus-cnn_dailymail\"\n",
        "# Load the tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_path).to(device).half()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BcmItnYHKyEc",
        "outputId": "85bb5e71-f868-4e3e-d6de-dd929629b1ab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Homeowners sell their homes and buy other homes for a variety of reasons.<n>This article is about finding the best sequence of steps in the process.\n"
          ]
        }
      ],
      "source": [
        "# TEST\n",
        "raw_document = 'Homeowners sell their homes and buy other homes for a variety of reasons including a need to live closer to a place of employment, to be closer to family, to enjoy a better climate, or simply to upgrade. This article is about finding the best sequence of steps in the process.'\n",
        "\n",
        "# Encode the text, ensuring that it is truncated to the maximum length acceptable by the model\n",
        "input_ids = tokenizer.encode(raw_document, return_tensors=\"pt\", truncation=True, max_length=model.config.max_position_embeddings).to(device)\n",
        "\n",
        "# Perform inference\n",
        "with torch.no_grad():\n",
        "    output = model.generate(\n",
        "        input_ids,\n",
        "        num_beams=5,\n",
        "        min_length=5,\n",
        "        no_repeat_ngram_size=3,\n",
        "        max_length=50\n",
        "    )\n",
        "\n",
        "# Decode and print the output\n",
        "decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "print(decoded_output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5uODCxPrM38S"
      },
      "outputs": [],
      "source": [
        "def get_summ(raw_document):\n",
        "  # Encode the text, ensuring that it is truncated to the maximum length acceptable by the model\n",
        "  input_ids = tokenizer.encode(raw_document, return_tensors=\"pt\", truncation=True, max_length=model.config.max_position_embeddings).to(device)\n",
        "\n",
        "  # Perform inference\n",
        "  with torch.no_grad():\n",
        "      output = model.generate(\n",
        "          input_ids,\n",
        "          num_beams=5,\n",
        "          min_length=5,\n",
        "          no_repeat_ngram_size=3,\n",
        "          max_length=50\n",
        "      )\n",
        "\n",
        "  # Decode and print the output\n",
        "  decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "  return decoded_output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N6VaAGT6QoTF"
      },
      "outputs": [],
      "source": [
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ohJWt-9NRz-",
        "outputId": "5ad5b39e-95db-40be-ffb6-4699cb365d08"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [06:18<00:00,  3.79s/it]\n"
          ]
        }
      ],
      "source": [
        "summ_results=[]\n",
        "for row in tqdm(range(test_data.shape[0])):\n",
        "  cur_list = test_data.iloc[row]['keyword_results']\n",
        "  updated_list = [{'text': entry['text'], 'title': entry['title'], 'id': entry['id'],'keywords': entry['keywords'],'summary':get_summ(entry['text'])} for entry in cur_list]\n",
        "  summ_results.append(updated_list)\n",
        "  if(row%10==0):\n",
        "    # Save to a pickle file\n",
        "    with open(\"contriever/data_summ.pkl\", \"wb\") as file:\n",
        "      pickle.dump(summ_results, file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "edar6i5aTSNB",
        "outputId": "5498e9d2-6cb9-4226-9d6e-026811077921"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'text': \"Parents: We are not there to make life easy on our kids. It's our job to help them grow up with the skills to become successful adults.\",\n",
              "  'title': \"Growing a Generation Who Doesn't know How to Fail\",\n",
              "  'id': '3019091003',\n",
              "  'keywords': [('parents', 0.5985),\n",
              "   ('kids', 0.4438),\n",
              "   ('adults', 0.4359),\n",
              "   ('life', 0.345),\n",
              "   ('skills', 0.3163)],\n",
              "  'summary': \"Parents: We are not there to make life easy on our kids.<n>It's our job to help them grow up with the skills to become successful adults.\"},\n",
              " {'text': 'Teaching our kids to say \"thank you\" is important, but truly instilling a sense of gratitude in them is another matter entirely. Gratitude goes beyond good manners -- it\\'s a mindset and a lifestyle.',\n",
              "  'title': '11 Tips for Instilling True Gratitude in Your Kids',\n",
              "  'id': '301909793',\n",
              "  'keywords': [('gratitude', 0.59),\n",
              "   ('manners', 0.3924),\n",
              "   ('kids', 0.2494),\n",
              "   ('important', 0.1572),\n",
              "   ('thank', 0.1485)],\n",
              "  'summary': 'Teaching kids to say \"thank you\" is important, but truly instilling a sense of gratitude in them is another matter entirely.<n> Gratitude goes beyond good manners -- it\\'s a mindset and a lifestyle.'},\n",
              " {'text': \"A friendly reminder from science that the flu vaccine isn't linked to autism.\",\n",
              "  'title': 'PSA: Pregnant Women Should Still Get Flu Shots',\n",
              "  'id': '301909365',\n",
              "  'keywords': [('autism', 0.5727),\n",
              "   ('vaccine', 0.5024),\n",
              "   ('flu', 0.4473),\n",
              "   ('reminder', 0.1656),\n",
              "   ('science', 0.162)],\n",
              "  'summary': \"A friendly reminder from science that the flu vaccine isn't linked to autism.\"}]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "summ_results[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7AybFhgoOiQF"
      },
      "outputs": [],
      "source": [
        "test_data['summary_results']=summ_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oF_inMhfUnFV"
      },
      "outputs": [],
      "source": [
        "test_data.to_pickle('contriever/test_data_100_summ.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1oAwvP-LZan6"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4IIi-A2N0_gv"
      },
      "source": [
        "# STEP 4 STYLE ANALYSIS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ulMAIV9TGEkG"
      },
      "source": [
        "##### Style Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zgXLeeetGm1F",
        "outputId": "c23ce72b-1ecf-4a59-cba6-bd9dc8fd0b76"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package cmudict to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/cmudict.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import collections as coll\n",
        "import math\n",
        "import pickle\n",
        "import string\n",
        "import numpy as np\n",
        "from nltk.corpus import cmudict\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize, sent_tokenize\n",
        "import nltk\n",
        "\n",
        "nltk.download('cmudict')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L45fG0tSGHK0"
      },
      "outputs": [],
      "source": [
        "def syllable_count_Manual(word):\n",
        "    word = word.lower()\n",
        "    count = 0\n",
        "    vowels = \"aeiouy\"\n",
        "    if word[0] in vowels:\n",
        "        count += 1\n",
        "    for index in range(1, len(word)):\n",
        "        if word[index] in vowels and word[index - 1] not in vowels:\n",
        "            count += 1\n",
        "            if word.endswith(\"e\"):\n",
        "                count -= 1\n",
        "    if count == 0:\n",
        "        count += 1\n",
        "    return count\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------------------\n",
        "# COUNTS NUMBER OF SYLLABLES\n",
        "\n",
        "def syllable_count(word):\n",
        "    global cmuDictionary\n",
        "    d = cmuDictionary\n",
        "    try:\n",
        "        syl = [len(list(y for y in x if y[-1].isdigit())) for x in d[word.lower()]][0]\n",
        "    except:\n",
        "        syl = syllable_count_Manual(word)\n",
        "    return syl\n",
        "\n",
        "    # ----------------------------------------------------------------------------\n",
        "\n",
        "\n",
        "# removing stop words plus punctuation.\n",
        "def Avg_wordLength(str):\n",
        "    str.translate(string.punctuation)\n",
        "    tokens = word_tokenize(str, language='english')\n",
        "    st = [\",\", \".\", \"'\", \"!\", '\"', \"#\", \"$\", \"%\", \"&\", \"(\", \")\", \"*\", \"+\", \"-\", \".\", \"/\", \":\", \";\", \"<\", \"=\", '>', \"?\",\n",
        "          \"@\", \"[\", \"\\\\\", \"]\", \"^\", \"_\", '`', \"{\", \"|\", \"}\", '~', '\\t', '\\n']\n",
        "    stop = stopwords.words('english') + st\n",
        "    words = [word for word in tokens if word not in stop]\n",
        "    return np.average([len(word) for word in words])\n",
        "\n",
        "\n",
        "# ----------------------------------------------------------------------------\n",
        "\n",
        "\n",
        "# returns avg number of characters in a sentence\n",
        "def Avg_SentLenghtByCh(text):\n",
        "    tokens = sent_tokenize(text)\n",
        "    return np.average([len(token) for token in tokens])\n",
        "\n",
        "\n",
        "# ----------------------------------------------------------------------------\n",
        "\n",
        "# returns avg number of words in a sentence\n",
        "def Avg_SentLenghtByWord(text):\n",
        "    tokens = sent_tokenize(text)\n",
        "    return np.average([len(token.split()) for token in tokens])\n",
        "\n",
        "\n",
        "# ----------------------------------------------------------------------------\n",
        "\n",
        "\n",
        "# GIVES NUMBER OF SYLLABLES PER WORD\n",
        "def Avg_Syllable_per_Word(text):\n",
        "    tokens = word_tokenize(text, language='english')\n",
        "    st = [\",\", \".\", \"'\", \"!\", '\"', \"#\", \"$\", \"%\", \"&\", \"(\", \")\", \"*\", \"+\", \"-\", \".\", \"/\", \":\", \";\", \"<\", \"=\", '>', \"?\",\n",
        "          \"@\", \"[\", \"\\\\\", \"]\", \"^\", \"_\", '`', \"{\", \"|\", \"}\", '~', '\\t', '\\n']\n",
        "    stop = stopwords.words('english') + st\n",
        "    words = [word for word in tokens if word not in stop]\n",
        "    syllabls = [syllable_count(word) for word in words]\n",
        "    p = (\" \".join(words))\n",
        "    return sum(syllabls) / max(1, len(words))\n",
        "\n",
        "\n",
        "# -----------------------------------------------------------------------------\n",
        "\n",
        "# COUNTS SPECIAL CHARACTERS NORMALIZED OVER LENGTH OF CHUNK\n",
        "def CountSpecialCharacter(text):\n",
        "    st = [\"#\", \"$\", \"%\", \"&\", \"(\", \")\", \"*\", \"+\", \"-\", \"/\", \"<\", \"=\", '>',\n",
        "          \"@\", \"[\", \"\\\\\", \"]\", \"^\", \"_\", '`', \"{\", \"|\", \"}\", '~', '\\t', '\\n']\n",
        "    count = 0\n",
        "    for i in text:\n",
        "        if (i in st):\n",
        "            count = count + 1\n",
        "    return count / len(text)\n",
        "\n",
        "\n",
        "# ----------------------------------------------------------------------------\n",
        "\n",
        "def CountPuncuation(text):\n",
        "    st = [\",\", \".\", \"'\", \"!\", '\"', \";\", \"?\", \":\", \";\"]\n",
        "    count = 0\n",
        "    for i in text:\n",
        "        if (i in st):\n",
        "            count = count + 1\n",
        "    return float(count) / float(len(text))\n",
        "\n",
        "\n",
        "# ----------------------------------------------------------------------------\n",
        "# RETURNS NORMALIZED COUNT OF FUNCTIONAL WORDS FROM A Framework for\n",
        "# Authorship Identification of Online Messages: Writing-Style Features and Classification Techniques\n",
        "\n",
        "def CountFunctionalWords(text):\n",
        "    functional_words = \"\"\"a between in nor some upon\n",
        "    about both including nothing somebody us\n",
        "    above but inside of someone used\n",
        "    after by into off something via\n",
        "    all can is on such we\n",
        "    although cos it once than what\n",
        "    am do its one that whatever\n",
        "    among down latter onto the when\n",
        "    an each less opposite their where\n",
        "    and either like or them whether\n",
        "    another enough little our these which\n",
        "    any every lots outside they while\n",
        "    anybody everybody many over this who\n",
        "    anyone everyone me own those whoever\n",
        "    anything everything more past though whom\n",
        "    are few most per through whose\n",
        "    around following much plenty till will\n",
        "    as for must plus to with\n",
        "    at from my regarding toward within\n",
        "    be have near same towards without\n",
        "    because he need several under worth\n",
        "    before her neither she unless would\n",
        "    behind him no should unlike yes\n",
        "    below i nobody since until you\n",
        "    beside if none so up your\n",
        "    \"\"\"\n",
        "\n",
        "    functional_words = functional_words.split()\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    count = 0\n",
        "\n",
        "    for i in text:\n",
        "        if i in functional_words:\n",
        "            count += 1\n",
        "\n",
        "    return count / len(words)\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------------------------\n",
        "\n",
        "# also returns Honore Measure R\n",
        "def hapaxLegemena(text):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    V1 = 0\n",
        "    # dictionary comprehension . har word kay against value 0 kardi\n",
        "    freqs = {key: 0 for key in words}\n",
        "    for word in words:\n",
        "        freqs[word] += 1\n",
        "    for word in freqs:\n",
        "        if freqs[word] == 1:\n",
        "            V1 += 1\n",
        "    N = len(words)\n",
        "    V = float(len(set(words)))\n",
        "    R = 100 * math.log(N) / max(1, (1 - (V1 / V)))\n",
        "    h = V1 / N\n",
        "    return R, h\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------------------------\n",
        "\n",
        "def hapaxDisLegemena(text):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    count = 0\n",
        "    # Collections as coll Counter takes an iterable collapse duplicate and counts as\n",
        "    # a dictionary how many equivelant items has been entered\n",
        "    freqs = coll.Counter()\n",
        "    freqs.update(words)\n",
        "    for word in freqs:\n",
        "        if freqs[word] == 2:\n",
        "            count += 1\n",
        "\n",
        "    h = count / float(len(words))\n",
        "    S = count / float(len(set(words)))\n",
        "    return S, h\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------------------------\n",
        "\n",
        "# c(w)  = ceil (log2 (f(w*)/f(w))) f(w*) frequency of most commonly used words f(w) frequency of word w\n",
        "# measure of vocabulary richness and connected to zipfs law, f(w*) const rak kay zips law say rank nikal rahay hein\n",
        "def AvgWordFrequencyClass(text):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    # dictionary comprehension . har word kay against value 0 kardi\n",
        "    freqs = {key: 0 for key in words}\n",
        "    for word in words:\n",
        "        freqs[word] += 1\n",
        "    maximum = float(max(list(freqs.values())))\n",
        "    return np.average([math.floor(math.log((maximum + 1) / (freqs[word]) + 1, 2)) for word in words])\n",
        "\n",
        "\n",
        "# --------------------------------------------------------------------------\n",
        "# TYPE TOKEN RATIO NO OF DIFFERENT WORDS / NO OF WORDS\n",
        "def typeTokenRatio(text):\n",
        "    words = word_tokenize(text)\n",
        "    return len(set(words)) / len(words)\n",
        "\n",
        "\n",
        "# --------------------------------------------------------------------------\n",
        "# logW = V-a/log(N)\n",
        "# N = total words , V = vocabulary richness (unique words) ,  a=0.17\n",
        "# we can convert into log because we are only comparing different texts\n",
        "def BrunetsMeasureW(text):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    a = 0.17\n",
        "    V = float(len(set(words)))\n",
        "    N = len(words)\n",
        "    B = (V - a) / (math.log(N))\n",
        "    return B\n",
        "\n",
        "\n",
        "# ------------------------------------------------------------------------\n",
        "def RemoveSpecialCHs(text):\n",
        "    text = word_tokenize(text)\n",
        "    st = [\",\", \".\", \"'\", \"!\", '\"', \"#\", \"$\", \"%\", \"&\", \"(\", \")\", \"*\", \"+\", \"-\", \".\", \"/\", \":\", \";\", \"<\", \"=\", '>', \"?\",\n",
        "          \"@\", \"[\", \"\\\\\", \"]\", \"^\", \"_\", '`', \"{\", \"|\", \"}\", '~', '\\t', '\\n']\n",
        "\n",
        "    words = [word for word in text if word not in st]\n",
        "    return words\n",
        "\n",
        "\n",
        "# -------------------------------------------------------------------------\n",
        "# K  10,000 * (M - N) / N**2\n",
        "# , where M  Sigma i**2 * Vi.\n",
        "def YulesCharacteristicK(text):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    N = len(words)\n",
        "    freqs = coll.Counter()\n",
        "    freqs.update(words)\n",
        "    vi = coll.Counter()\n",
        "    vi.update(freqs.values())\n",
        "    M = sum([(value * value) * vi[value] for key, value in freqs.items()])\n",
        "    K = 10000 * (M - N) / math.pow(N, 2)\n",
        "    return K\n",
        "\n",
        "\n",
        "# -------------------------------------------------------------------------\n",
        "\n",
        "\n",
        "# -1*sigma(pi*lnpi)\n",
        "# Shannon and sympsons index are basically diversity indices for any community\n",
        "def ShannonEntropy(text):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    lenght = len(words)\n",
        "    freqs = coll.Counter()\n",
        "    freqs.update(words)\n",
        "    arr = np.array(list(freqs.values()))\n",
        "    distribution = 1. * arr\n",
        "    distribution /= max(1, lenght)\n",
        "    import scipy as sc\n",
        "    H = sc.stats.entropy(distribution, base=2)\n",
        "    # H = sum([(i/lenght)*math.log(i/lenght,math.e) for i in freqs.values()])\n",
        "    return H\n",
        "\n",
        "\n",
        "# ------------------------------------------------------------------\n",
        "# 1 - (sigma(n(n - 1))/N(N-1)\n",
        "# N is total number of words\n",
        "# n is the number of each type of word\n",
        "def SimpsonsIndex(text):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    freqs = coll.Counter()\n",
        "    freqs.update(words)\n",
        "    N = len(words)\n",
        "    n = sum([1.0 * i * (i - 1) for i in freqs.values()])\n",
        "    D = 1 - (n / (N * (N - 1)))\n",
        "    return D\n",
        "\n",
        "\n",
        "# ------------------------------------------------------------------\n",
        "\n",
        "def FleschReadingEase(text, NoOfsentences):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    l = float(len(words))\n",
        "    scount = 0\n",
        "    for word in words:\n",
        "        scount += syllable_count(word)\n",
        "\n",
        "    I = 206.835 - 1.015 * (l / float(NoOfsentences)) - 84.6 * (scount / float(l))\n",
        "    return I\n",
        "\n",
        "\n",
        "# -------------------------------------------------------------------\n",
        "def FleschCincadeGradeLevel(text, NoOfSentences):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    scount = 0\n",
        "    for word in words:\n",
        "        scount += syllable_count(word)\n",
        "\n",
        "    l = len(words)\n",
        "    F = 0.39 * (l / NoOfSentences) + 11.8 * (scount / float(l)) - 15.59\n",
        "    return F\n",
        "\n",
        "\n",
        "# -----------------------------------------------------------------\n",
        "def dale_chall_readability_formula(text, NoOfSectences):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    difficult = 0\n",
        "    adjusted = 0\n",
        "    NoOfWords = len(words)\n",
        "    with open('dale-chall.pkl', 'rb') as f:\n",
        "        fimiliarWords = pickle.load(f)\n",
        "    for word in words:\n",
        "        if word not in fimiliarWords:\n",
        "            difficult += 1\n",
        "    percent = (difficult / NoOfWords) * 100\n",
        "    if (percent > 5):\n",
        "        adjusted = 3.6365\n",
        "    D = 0.1579 * (percent) + 0.0496 * (NoOfWords / NoOfSectences) + adjusted\n",
        "    return D\n",
        "\n",
        "\n",
        "# ------------------------------------------------------------------\n",
        "def GunningFoxIndex(text, NoOfSentences):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    NoOFWords = float(len(words))\n",
        "    complexWords = 0\n",
        "    for word in words:\n",
        "        if (syllable_count(word) > 2):\n",
        "            complexWords += 1\n",
        "\n",
        "    G = 0.4 * ((NoOFWords / NoOfSentences) + 100 * (complexWords / NoOFWords))\n",
        "    return G\n",
        "\n",
        "\n",
        "# ------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3ENYswU7GHmC"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sHdbTgRaGI4q"
      },
      "source": [
        "#### Calls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oqe93ZeXGKch"
      },
      "outputs": [],
      "source": [
        "cmuDictionary = None\n",
        "def FeatureExtraction(chunk, winSize):\n",
        "    global cmuDictionary\n",
        "    cmuDictionary = cmudict.dict()\n",
        "\n",
        "    features = []\n",
        "\n",
        "    # LEXICAL FEATURES\n",
        "    mean_word_length = Avg_wordLength(chunk)\n",
        "    features.append(f\"Mean Word Length: {mean_word_length}\")\n",
        "\n",
        "    mean_sentence_length_chars = Avg_SentLenghtByCh(chunk)\n",
        "    features.append(f\"Mean Sentence Length (characters): {mean_sentence_length_chars}\")\n",
        "\n",
        "    mean_sentence_length_words = Avg_SentLenghtByWord(chunk)\n",
        "    features.append(f\"Mean Sentence Length (words): {mean_sentence_length_words}\")\n",
        "\n",
        "    mean_syllables_per_word = Avg_Syllable_per_Word(chunk)\n",
        "    features.append(f\"Mean Syllables per Word: {mean_syllables_per_word}\")\n",
        "\n",
        "    count_special_characters = CountSpecialCharacter(chunk)\n",
        "    features.append(f\"Count of Special Characters: {count_special_characters}\")\n",
        "\n",
        "    count_punctuation = CountPuncuation(chunk)\n",
        "    features.append(f\"Count of Punctuation: {count_punctuation}\")\n",
        "\n",
        "    count_functional_words = CountFunctionalWords(chunk)\n",
        "    features.append(f\"Count of Functional Words: {count_functional_words}\")\n",
        "\n",
        "    # VOCABULARY RICHNESS FEATURES\n",
        "    type_token_ratio = typeTokenRatio(chunk)\n",
        "    features.append(f\"Type-Token Ratio: {type_token_ratio}\")\n",
        "\n",
        "    honore_measure_r, hapax_legemena = hapaxLegemena(chunk)\n",
        "    features.append(f\"Hapax Legemena: {hapax_legemena}, Honore Measure R: {honore_measure_r}\")\n",
        "\n",
        "    sicheles_measure_s, hapax_dis_legemena = hapaxDisLegemena(chunk)\n",
        "    features.append(f\"Hapax Dis Legemena: {hapax_dis_legemena}, Sicheles Measure S: {sicheles_measure_s}\")\n",
        "\n",
        "    yules_characteristic_k = YulesCharacteristicK(chunk)\n",
        "    features.append(f\"Yules Characteristic K: {yules_characteristic_k}\")\n",
        "\n",
        "    simpsons_index = SimpsonsIndex(chunk)\n",
        "    features.append(f\"Simpsons Index: {simpsons_index}\")\n",
        "\n",
        "    brunets_measure_w = BrunetsMeasureW(chunk)\n",
        "    features.append(f\"Brunets Measure W: {brunets_measure_w}\")\n",
        "\n",
        "    shannon_entropy = ShannonEntropy(chunk)\n",
        "    features.append(f\"Shannon Entropy: {shannon_entropy}\")\n",
        "\n",
        "    # READABILITY FEATURES\n",
        "    flesch_reading_ease = FleschReadingEase(chunk, winSize)\n",
        "    features.append(f\"Flesch Reading Ease: {flesch_reading_ease}\")\n",
        "\n",
        "    flesch_cincade_grade_level = FleschCincadeGradeLevel(chunk, winSize)\n",
        "    features.append(f\"Flesch Cincade Grade Level: {flesch_cincade_grade_level}\")\n",
        "\n",
        "    #dale_chall_readability = dale_chall_readability_formula(chunk, winSize)\n",
        "    #features.append(f\"Dale Chall Readability Formula: {dale_chall_readability}\")\n",
        "\n",
        "    gunning_fox_index = GunningFoxIndex(chunk, winSize)\n",
        "    features.append(f\"Gunning Fox Index: {gunning_fox_index}\")\n",
        "\n",
        "    return '\\n'.join(features)\n",
        "\n",
        "\n",
        "\n",
        "def FeatureExtraction_titles(chunk_list):\n",
        "    global cmuDictionary\n",
        "    cmuDictionary = cmudict.dict()\n",
        "\n",
        "    features = []\n",
        "\n",
        "    # Initialize dictionaries to store sum and count for averaging\n",
        "    sum_dict = {}\n",
        "    count_dict = {}\n",
        "\n",
        "    # Function to update sum and count dictionaries\n",
        "    def update_dicts(feature_name, value):\n",
        "        if feature_name not in sum_dict:\n",
        "            sum_dict[feature_name] = value\n",
        "            count_dict[feature_name] = 1\n",
        "        else:\n",
        "            sum_dict[feature_name] += value\n",
        "            count_dict[feature_name] += 1\n",
        "\n",
        "    # Iterate over each chunk in the list\n",
        "    for chunk in chunk_list:\n",
        "        # LEXICAL FEATURES\n",
        "        update_dicts(\"Mean Word Length\", Avg_wordLength(chunk))\n",
        "        update_dicts(\"Mean Sentence Length (characters)\", Avg_SentLenghtByCh(chunk))\n",
        "        update_dicts(\"Mean Sentence Length (words)\", Avg_SentLenghtByWord(chunk))\n",
        "        update_dicts(\"Mean Syllables per Word\", Avg_Syllable_per_Word(chunk))\n",
        "        update_dicts(\"Count of Special Characters\", CountSpecialCharacter(chunk))\n",
        "        update_dicts(\"Count of Punctuation\", CountPuncuation(chunk))\n",
        "        update_dicts(\"Count of Functional Words\", CountFunctionalWords(chunk))\n",
        "\n",
        "        # VOCABULARY RICHNESS FEATURES\n",
        "        update_dicts(\"Type-Token Ratio\", typeTokenRatio(chunk))\n",
        "        honore_measure_r, hapax_legemena = hapaxLegemena(chunk)\n",
        "        update_dicts(\"Hapax Legemena\", hapax_legemena)\n",
        "        update_dicts(\"Honore Measure R\", honore_measure_r)\n",
        "        sicheles_measure_s, hapax_dis_legemena = hapaxDisLegemena(chunk)\n",
        "        update_dicts(\"Hapax Dis Legemena\", hapax_dis_legemena)\n",
        "        update_dicts(\"Sicheles Measure S\", sicheles_measure_s)\n",
        "        update_dicts(\"Yules Characteristic K\", YulesCharacteristicK(chunk))\n",
        "        update_dicts(\"Simpsons Index\", SimpsonsIndex(chunk))\n",
        "        update_dicts(\"Brunets Measure W\", BrunetsMeasureW(chunk))\n",
        "        update_dicts(\"Shannon Entropy\", ShannonEntropy(chunk))\n",
        "\n",
        "        # READABILITY FEATURES\n",
        "        update_dicts(\"Flesch Reading Ease\", FleschReadingEase(chunk, len(chunk)))\n",
        "        update_dicts(\"Flesch Cincade Grade Level\", FleschCincadeGradeLevel(chunk,len(chunk)))\n",
        "        #update_dicts(\"Dale Chall Readability Formula\", dale_chall_readability_formula(chunk,len(chunk)))\n",
        "        update_dicts(\"Gunning Fox Index\", GunningFoxIndex(chunk, len(chunk)))\n",
        "\n",
        "    # Calculate the average for each feature\n",
        "    for feature_name, feature_sum in sum_dict.items():\n",
        "        avg_value = feature_sum / count_dict[feature_name]\n",
        "        features.append(f\"{feature_name}: {avg_value}\")\n",
        "\n",
        "    return '\\n'.join(features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E-UWEQurGs3S"
      },
      "source": [
        "#### Test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bb4jJm4rGRjM"
      },
      "outputs": [],
      "source": [
        "exp='''\n",
        "Lexical Features:\n",
        "1. Mean Word Length: Average number of characters in words.\n",
        "2. Mean Sentence Length (characters): Average number of characters in sentences.\n",
        "3. Mean Sentence Length (words): Average number of words in sentences.\n",
        "4. Mean Syllables per Word: Average number of syllables in words.\n",
        "5. Count of Special Characters: Normalized count of special characters over the length of the chunk.\n",
        "6. Count of Punctuation: Normalized count of punctuation marks over the length of the chunk.\n",
        "7. Count of Functional Words: Normalized count of functional words from a specific set.\n",
        "\n",
        "Vocabulary Richness Features:\n",
        "8. Type-Token Ratio: Ratio of different words to total words.\n",
        "9. Hapax Legemena: Ratio of words that occur only once to total words.\n",
        "10. Sicheles Measure S: Ratio of words that occur twice to total words.\n",
        "11. Yules Characteristic K: Measure of vocabulary richness and connection to Zipf's Law.\n",
        "12. Simpsons Index: Diversity index based on word frequency.\n",
        "13. Brunets Measure W: Vocabulary richness index.\n",
        "14. Shannon Entropy: Information entropy based on word frequency.\n",
        "\n",
        "Readability Features:\n",
        "15. Flesch Reading Ease: Readability score based on average sentence length and syllables per word.\n",
        "16. Flesch Cincade Grade Level: Readability score based on average sentence length and syllables per word.\n",
        "17. Dale Chall Readability Formula: Readability score considering difficult words.\n",
        "18. Gunning Fox Index: Readability index based on sentence length and complex words.\n",
        "\n",
        "'''"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WX_Ed1mlKNHy"
      },
      "source": [
        "#### LLM BASED"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OWWaPXS-G6WP"
      },
      "outputs": [],
      "source": [
        "## PROMPT for LLM BASE WRITING STYLE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yKaBMTsQINBy",
        "outputId": "f29e92a3-97fc-47c7-dc27-89a07b7e5f6c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: google-generativeai in /usr/local/lib/python3.10/dist-packages (0.2.2)\n",
            "Requirement already satisfied: google-ai-generativelanguage==0.3.3 in /usr/local/lib/python3.10/dist-packages (from google-generativeai) (0.3.3)\n",
            "Requirement already satisfied: google-auth in /usr/local/lib/python3.10/dist-packages (from google-generativeai) (2.17.3)\n",
            "Requirement already satisfied: google-api-core in /usr/local/lib/python3.10/dist-packages (from google-generativeai) (2.11.1)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from google-generativeai) (3.20.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from google-generativeai) (4.66.1)\n",
            "Requirement already satisfied: proto-plus<2.0.0dev,>=1.22.0 in /usr/local/lib/python3.10/dist-packages (from google-ai-generativelanguage==0.3.3->google-generativeai) (1.22.3)\n",
            "Requirement already satisfied: googleapis-common-protos<2.0.dev0,>=1.56.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core->google-generativeai) (1.61.0)\n",
            "Requirement already satisfied: requests<3.0.0.dev0,>=2.18.0 in /usr/local/lib/python3.10/dist-packages (from google-api-core->google-generativeai) (2.31.0)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth->google-generativeai) (5.3.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth->google-generativeai) (0.3.0)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from google-auth->google-generativeai) (1.16.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth->google-generativeai) (4.9)\n",
            "Requirement already satisfied: grpcio<2.0dev,>=1.33.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core->google-generativeai) (1.59.3)\n",
            "Requirement already satisfied: grpcio-status<2.0.dev0,>=1.33.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core->google-generativeai) (1.48.2)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth->google-generativeai) (0.5.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core->google-generativeai) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core->google-generativeai) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core->google-generativeai) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core->google-generativeai) (2023.11.17)\n"
          ]
        }
      ],
      "source": [
        "!pip install google-generativeai"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UQGXbwTJIutY"
      },
      "outputs": [],
      "source": [
        "import google.generativeai as palm\n",
        "import os\n",
        "os.environ['API_KEY']=#API KEY HERE\n",
        "palm.configure(api_key=os.environ['API_KEY'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QVuV4o_zKQRR"
      },
      "source": [
        "## RUN STYLE ANALYSIS ON TEST DATA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wkPFZ-gPec5x"
      },
      "outputs": [],
      "source": [
        "import time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M_NYGYUebxR5"
      },
      "outputs": [],
      "source": [
        "def get_palm_response(input_text, prompt_instructions):\n",
        "  p=\"input text: \"+ input_text+\"\\n\"+prompt_instructions\n",
        "  response = palm.generate_text(prompt=p)\n",
        "  return response.result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "laVZ26zwKnB7"
      },
      "outputs": [],
      "source": [
        "def get_writing_style(text,prompt_instructions,all_titles):\n",
        "  statistical_analysis= FeatureExtraction_titles(all_titles)\n",
        "  llm_bases_analysis=get_palm_response(text, prompt_instructions)\n",
        "\n",
        "  return {'statistical_analysis':statistical_analysis,'llm_bases_analysis':llm_bases_analysis }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N96zVjPyVkVi",
        "outputId": "3b13cce1-4deb-460f-f984-348e5a83792e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [09:12<00:00,  5.53s/it]\n"
          ]
        }
      ],
      "source": [
        "prompt_instructions='''You are a writing style detector. Your job is to specify a writing style of input text above. When you do so, you describe outloud:\n",
        "general writing style (like creative, non-fiction),\n",
        "genres,\n",
        "style descriptors (like full of dialogue, short sentences),\n",
        "moods (like serious, joking),\n",
        "authors that share a similar style (like Kurt Vonnegut),\n",
        "common phrases or speech patterns. You are not to provide any other additional information.\n",
        "'''\n",
        "style_results=[]\n",
        "for row in tqdm(range(test_data.shape[0])):\n",
        "  cur_list = test_data.iloc[row]['summary_results']\n",
        "\n",
        "  all_titles = [entry['title'] for entry in cur_list]\n",
        "  all_profile_article = [entry['title']+'\\n'+entry['text'] for entry in cur_list]\n",
        "\n",
        "  style=get_writing_style(\"\\n\".join(all_profile_article),prompt_instructions,all_titles)\n",
        "  style_results.append(style)\n",
        "\n",
        "  if(row%10==0):\n",
        "    # Save to a pickle file\n",
        "    with open(\"style_checkpoints/cont_style_summ_\"+str(row)+\".pkl\", \"wb\") as file:\n",
        "      pickle.dump(style_results, file)\n",
        "      time.sleep(30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x8O_JyDN8kRk"
      },
      "outputs": [],
      "source": [
        "test_data['style_results']=style_results\n",
        "test_data.to_pickle('contriever/test_data_100_style.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uz5r_DqclJRS",
        "outputId": "ae7876c9-1d84-448f-a3e5-784b07652309"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'statistical_analysis': 'Mean Word Length: 4.928571428571428\\nMean Sentence Length (characters): 48.333333333333336\\nMean Sentence Length (words): 8.666666666666666\\nMean Syllables per Word: 1.4404761904761905\\nCount of Special Characters: 0.0\\nCount of Punctuation: 0.014049097900029575\\nCount of Functional Words: 0.5425925925925926\\nType-Token Ratio: 1.0\\nHapax Legemena: 1.0\\nHonore Measure R: 219.30837373367004\\nHapax Dis Legemena: 0.0\\nSicheles Measure S: 0.0\\nYules Characteristic K: 8879.62962962963\\nSimpsons Index: 1.0\\nBrunets Measure W: 4.017751666462215\\nShannon Entropy: 3.163951032109891\\nFlesch Reading Ease: 91.1828784679089\\nFlesch Cincade Grade Level: 0.5873541227118864\\nGunning Fox Index: 4.370695586447138',\n",
              " 'llm_bases_analysis': 'general writing style: non-fiction\\ngenres: opinion piece, parenting advice, health advice\\nstyle descriptors: informal, conversational, personal, authoritative\\nmoods: serious, informative\\nauthors that share a similar style: Amy Poehler, Chelsea Handler, Tina Fey\\ncommon phrases or speech patterns: \"We are not there to make life easy on our kids\", \"truly instilling a sense of gratitude in them is another matter entirely\", \"a friendly reminder from science\"'}"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.iloc[0]['style_results']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4SZ0nzU4nkb5"
      },
      "source": [
        "## Adding keywords and summary for input too."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AGQTZA7L_-fX"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Upv_ffi0npMu"
      },
      "outputs": [],
      "source": [
        "keyword_list=[]\n",
        "summary_list=[]\n",
        "for row in tqdm(range(test_data.shape[0])):\n",
        "  input = test_data.iloc[row]['input'].split(\"Generate a headline for the following article:\")[-1].strip()\n",
        "  keywords_scores=kw_model.extract_keywords(input)\n",
        "  keyword_list.append(', '.join([kw[0] for kw in keywords_scores]))\n",
        "  summary_list.append(get_summ(input))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qy7kl0_zpkQj"
      },
      "outputs": [],
      "source": [
        "test_data['input_keywords']=keyword_list\n",
        "test_data[\"input_summary\"]=summary_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qO7-lwvkpkMu"
      },
      "outputs": [],
      "source": [
        "test_data.to_pickle('contriever/preprompt_100.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J9tIarZUiGqI"
      },
      "source": [
        "# COMPLETE PIPELINE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bH8ueyMWp3t-"
      },
      "outputs": [],
      "source": [
        "preprompt_data=pd.read_pickle(\"contriever/preprompt_100.pkl\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        },
        "id": "8bNtkTRwiIy0",
        "outputId": "77591ca3-484b-48dc-e649-1eff50f3aac6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7eab2a8c-1e0a-4dc9-a16e-4737c13047c7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>input</th>\n",
              "      <th>profile</th>\n",
              "      <th>output</th>\n",
              "      <th>input_keywords</th>\n",
              "      <th>input_summary</th>\n",
              "      <th>contriever</th>\n",
              "      <th>keyword_results</th>\n",
              "      <th>summary_results</th>\n",
              "      <th>style_results</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1909</th>\n",
              "      <td>301909</td>\n",
              "      <td>Generate a headline for the following article:...</td>\n",
              "      <td>[{'text': 'Bryant's widow testified that she \"...</td>\n",
              "      <td>33 Phrases To Stop Your Toddler's Tantrums</td>\n",
              "      <td>tantrums, toddler, parenting, cooperation, smile</td>\n",
              "      <td>Here are some parenting phrases that can help ...</td>\n",
              "      <td>[{'text': 'Parents: We are not there to make l...</td>\n",
              "      <td>[{'text': 'Parents: We are not there to make l...</td>\n",
              "      <td>[{'text': 'Parents: We are not there to make l...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 4....</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7eab2a8c-1e0a-4dc9-a16e-4737c13047c7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-convert: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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\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-7eab2a8c-1e0a-4dc9-a16e-4737c13047c7 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-7eab2a8c-1e0a-4dc9-a16e-4737c13047c7');\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",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          id                                              input  \\\n",
              "1909  301909  Generate a headline for the following article:...   \n",
              "\n",
              "                                                profile  \\\n",
              "1909  [{'text': 'Bryant's widow testified that she \"...   \n",
              "\n",
              "                                          output  \\\n",
              "1909  33 Phrases To Stop Your Toddler's Tantrums   \n",
              "\n",
              "                                        input_keywords  \\\n",
              "1909  tantrums, toddler, parenting, cooperation, smile   \n",
              "\n",
              "                                          input_summary  \\\n",
              "1909  Here are some parenting phrases that can help ...   \n",
              "\n",
              "                                             contriever  \\\n",
              "1909  [{'text': 'Parents: We are not there to make l...   \n",
              "\n",
              "                                        keyword_results  \\\n",
              "1909  [{'text': 'Parents: We are not there to make l...   \n",
              "\n",
              "                                        summary_results  \\\n",
              "1909  [{'text': 'Parents: We are not there to make l...   \n",
              "\n",
              "                                          style_results  \n",
              "1909  {'statistical_analysis': 'Mean Word Length: 4....  "
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "preprompt_data.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pjdGbt3sibju"
      },
      "outputs": [],
      "source": [
        "def generate_title_prompt(input_text, style_results, summary_results,ip_keys,ip_summ):\n",
        "    # Extracting style analysis and general writing style of the user\n",
        "    style_analysis = style_results['statistical_analysis']\n",
        "    general_style = style_results['llm_bases_analysis']\n",
        "\n",
        "    # Extracting information from previous articles\n",
        "    previous_article_prompts = []\n",
        "    for article in summary_results:\n",
        "        article_text = article['text']\n",
        "        title = article['title']\n",
        "        keywords = ', '.join([kw[0] for kw in article['keywords']])\n",
        "        summary = article['summary']\n",
        "        previous_article_prompt = (\n",
        "            f\"Article Text: {article_text}\\n\"\n",
        "            f\"Title: {title}\\n\"\n",
        "            f\"Keywords: {keywords}\\n\"\n",
        "            f\"Summary: {summary}\\n\\n\"\n",
        "        )\n",
        "        previous_article_prompts.append(previous_article_prompt)\n",
        "\n",
        "    # Combining all information into the final prompt\n",
        "    final_prompt_details = (\n",
        "        f\"Previous Articles Analysis:\\n{''.join(previous_article_prompts)}\"\n",
        "        f\"Statistical Writing Style Analysis of Titles:\\n{style_analysis}\\n\\n\"\n",
        "        f\"General Writing Style of User:\\n{general_style}\\n\\n\"\n",
        "        f\"New Input Article:\\nArticle Text: {input_text}\\n\"\n",
        "        f\"Keywords: {ip_keys}\\n\"\n",
        "        f\"Summary: {ip_summ}\\n\\n\"\n",
        "    )\n",
        "\n",
        "    final_prompt=\"Your task is to analyze the provided data to understand the user's writing style and approach to titling. This includes examining previous articles with their titles, keywords, and summaries, a statistical analysis of the user's writing style in their titles, a general characterization of their writing style, and the text of a new input article. Based on this analysis, generate a title for the new article that aligns with the user's unique style.\"\\\n",
        "    +\"\\n\"\\\n",
        "    +final_prompt_details\\\n",
        "    +\"\\n\"+\"After reviewing all these elements, create a title for the new article that reflects the user's unique writing style and titling approach, as well as being pertinent to the new article's content and theme\"\n",
        "\n",
        "    return final_prompt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F8zzJDxEjyQI"
      },
      "outputs": [],
      "source": [
        "# Example usage\n",
        "input_article = preprompt_data.iloc[0]['input']\n",
        "ip_keys=preprompt_data.iloc[0]['input_keywords']\n",
        "ip_summ=preprompt_data.iloc[0]['input_summary']\n",
        "style_results = preprompt_data.iloc[0]['style_results']\n",
        "summary_results = preprompt_data.iloc[0]['summary_results']\n",
        "\n",
        "prompt_to_llm = generate_title_prompt(input_article, style_results, summary_results,ip_keys,ip_summ)\n",
        "pprint(prompt_to_llm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rAuV6V9IzBQO",
        "outputId": "e11ae4a3-e3ef-4ca6-a12e-3539a493ac5e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [00:00<00:00, 3433.54it/s]\n"
          ]
        }
      ],
      "source": [
        "prompt_list=[]\n",
        "for row in tqdm(range(preprompt_data.shape[0])):\n",
        "  input_article = preprompt_data.iloc[row]['input']\n",
        "  ip_keys=preprompt_data.iloc[row]['input_keywords']\n",
        "  ip_summ=preprompt_data.iloc[row]['input_summary']\n",
        "  style_results = preprompt_data.iloc[row]['style_results']\n",
        "  summary_results = preprompt_data.iloc[row]['summary_results']\n",
        "  prompt_list.append(generate_title_prompt(input_article, style_results, summary_results,ip_keys,ip_summ))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EOKltLIgsXig"
      },
      "outputs": [],
      "source": [
        "preprompt_data['prompts']=prompt_list\n",
        "preprompt_data.to_pickle('contriever/preprompt_100.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85BXritbsrln"
      },
      "source": [
        "# RUN PROMPTS THROUGH PALM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vykrYlu4tbcL"
      },
      "outputs": [],
      "source": [
        "preprompt_data=pd.read_pickle('contriever/preprompt_100.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "zBrG1lsvtkCp",
        "outputId": "df899306-1cb8-413e-b900-c8ada7432e64"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0ba7e130-d7e8-44eb-bbe0-23fdf524c18b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>input</th>\n",
              "      <th>profile</th>\n",
              "      <th>output</th>\n",
              "      <th>input_keywords</th>\n",
              "      <th>input_summary</th>\n",
              "      <th>contriever</th>\n",
              "      <th>keyword_results</th>\n",
              "      <th>summary_results</th>\n",
              "      <th>style_results</th>\n",
              "      <th>prompts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1909</th>\n",
              "      <td>301909</td>\n",
              "      <td>Generate a headline for the following article:...</td>\n",
              "      <td>[{'text': 'Bryant's widow testified that she \"...</td>\n",
              "      <td>33 Phrases To Stop Your Toddler's Tantrums</td>\n",
              "      <td>tantrums, toddler, parenting, cooperation, smile</td>\n",
              "      <td>Here are some parenting phrases that can help ...</td>\n",
              "      <td>[{'text': 'Parents: We are not there to make l...</td>\n",
              "      <td>[{'text': 'Parents: We are not there to make l...</td>\n",
              "      <td>[{'text': 'Parents: We are not there to make l...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 4....</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ba7e130-d7e8-44eb-bbe0-23fdf524c18b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-convert: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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\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-0ba7e130-d7e8-44eb-bbe0-23fdf524c18b 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-0ba7e130-d7e8-44eb-bbe0-23fdf524c18b');\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",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          id                                              input  \\\n",
              "1909  301909  Generate a headline for the following article:...   \n",
              "\n",
              "                                                profile  \\\n",
              "1909  [{'text': 'Bryant's widow testified that she \"...   \n",
              "\n",
              "                                          output  \\\n",
              "1909  33 Phrases To Stop Your Toddler's Tantrums   \n",
              "\n",
              "                                        input_keywords  \\\n",
              "1909  tantrums, toddler, parenting, cooperation, smile   \n",
              "\n",
              "                                          input_summary  \\\n",
              "1909  Here are some parenting phrases that can help ...   \n",
              "\n",
              "                                             contriever  \\\n",
              "1909  [{'text': 'Parents: We are not there to make l...   \n",
              "\n",
              "                                        keyword_results  \\\n",
              "1909  [{'text': 'Parents: We are not there to make l...   \n",
              "\n",
              "                                        summary_results  \\\n",
              "1909  [{'text': 'Parents: We are not there to make l...   \n",
              "\n",
              "                                          style_results  \\\n",
              "1909  {'statistical_analysis': 'Mean Word Length: 4....   \n",
              "\n",
              "                                                prompts  \n",
              "1909  Your task is to analyze the provided data to u...  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "preprompt_data.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MnALmS_qsu2L",
        "outputId": "8eedcc80-20c4-4db0-fa6b-f55f38536ff2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [06:53<00:00,  4.14s/it]\n"
          ]
        }
      ],
      "source": [
        "import pickle\n",
        "import time\n",
        "response_list=[]\n",
        "for row in tqdm(range(preprompt_data.shape[0])):\n",
        "  cur_prompt=preprompt_data.iloc[row]['prompts']\n",
        "  response = palm.generate_text(prompt=cur_prompt)\n",
        "  response_list.append(response.result)\n",
        "\n",
        "  if(row%10==0):\n",
        "    # Save to a pickle file\n",
        "    with open(\"prompting_checkpoints/cont_prompt_out\"+str(row)+\".pkl\", \"wb\") as file:\n",
        "      pickle.dump(response_list, file)\n",
        "      time.sleep(30)\n",
        "\n",
        "\n",
        "preprompt_data['model_output']=response_list\n",
        "preprompt_data.to_pickle('contriever/postprompt_100.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1tl0bfm1xgn_"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gwb2gYE4xglO"
      },
      "outputs": [],
      "source": [
        "post_prompt_data=pd.read_pickle('contriever/postprompt_100.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2ogSBeLGkSP"
      },
      "source": [
        "## GETTING UN-PERSONALIZED RESULTS AS WELL"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uebAcHuyiV6",
        "outputId": "9c7b46c3-b53b-4e73-c73b-b09e8a30de5d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "id                                                            301909\n",
              "input              Generate a headline for the following article:...\n",
              "profile            [{'text': 'Bryant's widow testified that she \"...\n",
              "output                    33 Phrases To Stop Your Toddler's Tantrums\n",
              "bm25_results       [{'text': 'Here are five important tips to hel...\n",
              "keyword_results    [{'text': 'Here are five important tips to hel...\n",
              "summary_results    [{'text': 'Here are five important tips to hel...\n",
              "style_results      {'statistical_analysis': 'Mean Word Length: 4....\n",
              "input_keywords      tantrums, toddler, parenting, cooperation, smile\n",
              "input_summary      Here are some parenting phrases that can help ...\n",
              "prompts            Your task is to analyze the provided data to u...\n",
              "model_output       5 Parenting Phrases That Will Stop Tantrums In...\n",
              "Name: 1909, dtype: object"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "post_prompt_data.iloc[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1uRmwOfGHMod",
        "outputId": "0346e5e0-035a-4267-b136-d8e6d4e809a2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [04:04<00:00,  2.44s/it]\n"
          ]
        }
      ],
      "source": [
        "import pickle\n",
        "import time\n",
        "response_list=[]\n",
        "for row in tqdm(range(post_prompt_data.shape[0])):\n",
        "  response = palm.generate_text(prompt=post_prompt_data.iloc[row]['input'])\n",
        "  response_list.append(response.result)\n",
        "\n",
        "  if(row%10==0):\n",
        "    time.sleep(20)\n",
        "\n",
        "post_prompt_data['no_per_model_output']=response_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LFJzBgbJHq6i"
      },
      "outputs": [],
      "source": [
        "post_prompt_data['no_per_model_output']=response_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "4t3hpPYDKEdj",
        "outputId": "36de97dc-dd96-4092-bf9e-5b9ab98cb10e"
      },
      "outputs": [
        {
          "data": {
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>input</th>\n",
              "      <th>profile</th>\n",
              "      <th>output</th>\n",
              "      <th>bm25_results</th>\n",
              "      <th>keyword_results</th>\n",
              "      <th>summary_results</th>\n",
              "      <th>style_results</th>\n",
              "      <th>input_keywords</th>\n",
              "      <th>input_summary</th>\n",
              "      <th>prompts</th>\n",
              "      <th>model_output</th>\n",
              "      <th>no_per_model_output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1909</th>\n",
              "      <td>301909</td>\n",
              "      <td>Generate a headline for the following article:...</td>\n",
              "      <td>[{'text': 'Bryant's widow testified that she \"...</td>\n",
              "      <td>33 Phrases To Stop Your Toddler's Tantrums</td>\n",
              "      <td>[{'text': 'Here are five important tips to hel...</td>\n",
              "      <td>[{'text': 'Here are five important tips to hel...</td>\n",
              "      <td>[{'text': 'Here are five important tips to hel...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 4....</td>\n",
              "      <td>tantrums, toddler, parenting, cooperation, smile</td>\n",
              "      <td>Here are some parenting phrases that can help ...</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>5 Parenting Phrases That Will Stop Tantrums In...</td>\n",
              "      <td>5 Toddler-Taming Phrases That Actually Work</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7215</th>\n",
              "      <td>307215</td>\n",
              "      <td>Generate a headline for the following article:...</td>\n",
              "      <td>[{'text': '5. Coming with your significant oth...</td>\n",
              "      <td>Beyonce: Met Gala 2013 Honorary Chair!</td>\n",
              "      <td>[{'text': 'Check out the two covers below. Do ...</td>\n",
              "      <td>[{'text': 'Check out the two covers below. Do ...</td>\n",
              "      <td>[{'text': 'Check out the two covers below. Do ...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 5....</td>\n",
              "      <td>beyonce, gala, chair, costume, institute</td>\n",
              "      <td>Do you think Beyonce is a good choice to co-ch...</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>Beyonce Co-Chairs Met Gala With Anna Wintour, ...</td>\n",
              "      <td>**Beyoncé Co-Chairs Met Gala: Should She Be th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>693</th>\n",
              "      <td>30693</td>\n",
              "      <td>Generate a headline for the following article:...</td>\n",
              "      <td>[{'text': '\"Drivin down the wrong side of the ...</td>\n",
              "      <td>FEARLESS MEMOIR: 'World's Fair' (Chapter 18)</td>\n",
              "      <td>[{'text': 'I let my pants drop to the floor an...</td>\n",
              "      <td>[{'text': 'I let my pants drop to the floor an...</td>\n",
              "      <td>[{'text': 'I let my pants drop to the floor an...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 4....</td>\n",
              "      <td>feet, claws, fear, terror, budge</td>\n",
              "      <td>My feet refused to move; they were glued to th...</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2837</th>\n",
              "      <td>302837</td>\n",
              "      <td>Generate a headline for the following article:...</td>\n",
              "      <td>[{'text': 'He was subdued by passengers and cr...</td>\n",
              "      <td>Buzz Aldrin Looked As Baffled As We Felt Durin...</td>\n",
              "      <td>[{'text': 'It could also be cheeky passcode tr...</td>\n",
              "      <td>[{'text': 'It could also be cheeky passcode tr...</td>\n",
              "      <td>[{'text': 'It could also be cheeky passcode tr...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 4....</td>\n",
              "      <td>infinity, right, don, know, really</td>\n",
              "      <td>\"This is infinity here. It could be infinity. ...</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>Could This Be The Beginning Of Infinity?</td>\n",
              "      <td>**Could this be infinity?**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10676</th>\n",
              "      <td>3010676</td>\n",
              "      <td>Generate a headline for the following article:...</td>\n",
              "      <td>[{'text': 'Certain points during your day can ...</td>\n",
              "      <td>Starbucks To Stop Throwing Out Perfectly Good ...</td>\n",
              "      <td>[{'text': 'Finding a treatment for sociopaths ...</td>\n",
              "      <td>[{'text': 'Finding a treatment for sociopaths ...</td>\n",
              "      <td>[{'text': 'Finding a treatment for sociopaths ...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 4....</td>\n",
              "      <td>donations, coffee, employees, food, asking</td>\n",
              "      <td>The coffee chain says its employees called for...</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>Starbucks Employees Want More Food Donations -...</td>\n",
              "      <td>Starbucks Workers Want More Food Donations: 'T...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07d5a5fc-c5bf-477d-8a1d-206e7313c547')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-convert: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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\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-07d5a5fc-c5bf-477d-8a1d-206e7313c547 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-07d5a5fc-c5bf-477d-8a1d-206e7313c547');\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-b9007bef-e5ef-4bc4-b47d-4f7530019b93\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b9007bef-e5ef-4bc4-b47d-4f7530019b93')\"\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-b9007bef-e5ef-4bc4-b47d-4f7530019b93 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            id                                              input  \\\n",
              "1909    301909  Generate a headline for the following article:...   \n",
              "7215    307215  Generate a headline for the following article:...   \n",
              "693      30693  Generate a headline for the following article:...   \n",
              "2837    302837  Generate a headline for the following article:...   \n",
              "10676  3010676  Generate a headline for the following article:...   \n",
              "\n",
              "                                                 profile  \\\n",
              "1909   [{'text': 'Bryant's widow testified that she \"...   \n",
              "7215   [{'text': '5. Coming with your significant oth...   \n",
              "693    [{'text': '\"Drivin down the wrong side of the ...   \n",
              "2837   [{'text': 'He was subdued by passengers and cr...   \n",
              "10676  [{'text': 'Certain points during your day can ...   \n",
              "\n",
              "                                                  output  \\\n",
              "1909          33 Phrases To Stop Your Toddler's Tantrums   \n",
              "7215              Beyonce: Met Gala 2013 Honorary Chair!   \n",
              "693         FEARLESS MEMOIR: 'World's Fair' (Chapter 18)   \n",
              "2837   Buzz Aldrin Looked As Baffled As We Felt Durin...   \n",
              "10676  Starbucks To Stop Throwing Out Perfectly Good ...   \n",
              "\n",
              "                                            bm25_results  \\\n",
              "1909   [{'text': 'Here are five important tips to hel...   \n",
              "7215   [{'text': 'Check out the two covers below. Do ...   \n",
              "693    [{'text': 'I let my pants drop to the floor an...   \n",
              "2837   [{'text': 'It could also be cheeky passcode tr...   \n",
              "10676  [{'text': 'Finding a treatment for sociopaths ...   \n",
              "\n",
              "                                         keyword_results  \\\n",
              "1909   [{'text': 'Here are five important tips to hel...   \n",
              "7215   [{'text': 'Check out the two covers below. Do ...   \n",
              "693    [{'text': 'I let my pants drop to the floor an...   \n",
              "2837   [{'text': 'It could also be cheeky passcode tr...   \n",
              "10676  [{'text': 'Finding a treatment for sociopaths ...   \n",
              "\n",
              "                                         summary_results  \\\n",
              "1909   [{'text': 'Here are five important tips to hel...   \n",
              "7215   [{'text': 'Check out the two covers below. Do ...   \n",
              "693    [{'text': 'I let my pants drop to the floor an...   \n",
              "2837   [{'text': 'It could also be cheeky passcode tr...   \n",
              "10676  [{'text': 'Finding a treatment for sociopaths ...   \n",
              "\n",
              "                                           style_results  \\\n",
              "1909   {'statistical_analysis': 'Mean Word Length: 4....   \n",
              "7215   {'statistical_analysis': 'Mean Word Length: 5....   \n",
              "693    {'statistical_analysis': 'Mean Word Length: 4....   \n",
              "2837   {'statistical_analysis': 'Mean Word Length: 4....   \n",
              "10676  {'statistical_analysis': 'Mean Word Length: 4....   \n",
              "\n",
              "                                         input_keywords  \\\n",
              "1909   tantrums, toddler, parenting, cooperation, smile   \n",
              "7215           beyonce, gala, chair, costume, institute   \n",
              "693                    feet, claws, fear, terror, budge   \n",
              "2837                 infinity, right, don, know, really   \n",
              "10676        donations, coffee, employees, food, asking   \n",
              "\n",
              "                                           input_summary  \\\n",
              "1909   Here are some parenting phrases that can help ...   \n",
              "7215   Do you think Beyonce is a good choice to co-ch...   \n",
              "693    My feet refused to move; they were glued to th...   \n",
              "2837   \"This is infinity here. It could be infinity. ...   \n",
              "10676  The coffee chain says its employees called for...   \n",
              "\n",
              "                                                 prompts  \\\n",
              "1909   Your task is to analyze the provided data to u...   \n",
              "7215   Your task is to analyze the provided data to u...   \n",
              "693    Your task is to analyze the provided data to u...   \n",
              "2837   Your task is to analyze the provided data to u...   \n",
              "10676  Your task is to analyze the provided data to u...   \n",
              "\n",
              "                                            model_output  \\\n",
              "1909   5 Parenting Phrases That Will Stop Tantrums In...   \n",
              "7215   Beyonce Co-Chairs Met Gala With Anna Wintour, ...   \n",
              "693                                                 None   \n",
              "2837            Could This Be The Beginning Of Infinity?   \n",
              "10676  Starbucks Employees Want More Food Donations -...   \n",
              "\n",
              "                                     no_per_model_output  \n",
              "1909         5 Toddler-Taming Phrases That Actually Work  \n",
              "7215   **Beyoncé Co-Chairs Met Gala: Should She Be th...  \n",
              "693                                                 None  \n",
              "2837                         **Could this be infinity?**  \n",
              "10676  Starbucks Workers Want More Food Donations: 'T...  "
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "post_prompt_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fI8i31SaHMl3"
      },
      "outputs": [],
      "source": [
        "post_prompt_data.to_pickle('postprompt_100.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZMPLN369rTl"
      },
      "source": [
        "# Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "or9Cl86lHL3e"
      },
      "outputs": [],
      "source": [
        "#*****check on 'contriever/postprompt_100.pkl'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jp2RF5xU2olf",
        "outputId": "c23a5a34-17c6-41ba-d635-1b1a9ef6bde6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting rouge\n",
            "  Downloading rouge-1.0.1-py3-none-any.whl (13 kB)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from rouge) (1.16.0)\n",
            "Installing collected packages: rouge\n",
            "Successfully installed rouge-1.0.1\n"
          ]
        }
      ],
      "source": [
        "!pip install rouge"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TgUQpQQs9x4D"
      },
      "outputs": [],
      "source": [
        "from transformers import BertTokenizer, BertModel\n",
        "import torch\n",
        "import numpy as np\n",
        "from rouge import Rouge"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XcMg4Y_V4lpl"
      },
      "outputs": [],
      "source": [
        "def getRougeScores(groundTruth, generatedPrompt):\n",
        "  rouge = Rouge()\n",
        "  scores = rouge.get_scores(text1, text2)\n",
        "  r1, r2, rl = scores[0]['rouge-1']['r'] , scores[0]['rouge-2']['r'] , scores[0]['rouge-l']['r']\n",
        "\n",
        "  return r1,r2,rl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YDjFV-Z05kHC"
      },
      "outputs": [],
      "source": [
        "def getBertScore(groundTruth, generatedPrompt):\n",
        "  tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "  model = BertModel.from_pretrained(\"bert-base-uncased\")\n",
        "\n",
        "  # Step 1: Prepare the texts for BERT\n",
        "  inputs1 = tokenizer(groundTruth, return_tensors=\"pt\", padding=True, truncation=True)\n",
        "  inputs2 = tokenizer(generatedPrompt, return_tensors=\"pt\", padding=True, truncation=True)\n",
        "\n",
        "  # Step 2: Feed the texts to the BERT model\n",
        "  outputs1 = model(**inputs1)\n",
        "  outputs2 = model(**inputs2)\n",
        "\n",
        "  # Step 3: Obtain the representation vectors\n",
        "  embeddings1 = outputs1.last_hidden_state.mean(dim=1).detach().numpy()\n",
        "  embeddings2 = outputs2.last_hidden_state.mean(dim=1).detach().numpy()\n",
        "\n",
        "  # Step 4: Calculate cosine similarity\n",
        "  similarity = np.dot(embeddings1, embeddings2.T) / (np.linalg.norm(embeddings1) * np.linalg.norm(embeddings2))\n",
        "\n",
        "  return similarity[0][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cmfZfqmq9NpC"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5omzmFX69OS9"
      },
      "source": [
        "#Evaluating News HeadLine Generation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1mX5sLfl-y89"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import pickle as pk\n",
        "\n",
        "from rouge import Rouge\n",
        "from pandas import DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "a1Q6jTx8-cHS",
        "outputId": "6abd01e7-c4d9-4656-e8d6-125e3f4453d8"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>input</th>\n",
              "      <th>profile</th>\n",
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              "      <th>input_keywords</th>\n",
              "      <th>input_summary</th>\n",
              "      <th>contriever</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1909</th>\n",
              "      <td>301909</td>\n",
              "      <td>Generate a headline for the following article:...</td>\n",
              "      <td>[{'text': 'Bryant's widow testified that she \"...</td>\n",
              "      <td>33 Phrases To Stop Your Toddler's Tantrums</td>\n",
              "      <td>tantrums, toddler, parenting, cooperation, smile</td>\n",
              "      <td>Here are some parenting phrases that can help ...</td>\n",
              "      <td>[{'text': 'Parents: We are not there to make l...</td>\n",
              "      <td>[{'text': 'Parents: We are not there to make l...</td>\n",
              "      <td>[{'text': 'Parents: We are not there to make l...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 4....</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>5 Parenting Phrases That Will Make Your Toddle...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7215</th>\n",
              "      <td>307215</td>\n",
              "      <td>Generate a headline for the following article:...</td>\n",
              "      <td>[{'text': '5. Coming with your significant oth...</td>\n",
              "      <td>Beyonce: Met Gala 2013 Honorary Chair!</td>\n",
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              "      <td>Do you think Beyonce is a good choice to co-ch...</td>\n",
              "      <td>[{'text': 'Editor's Note: Previous references ...</td>\n",
              "      <td>[{'text': 'Editor's Note: Previous references ...</td>\n",
              "      <td>[{'text': 'Editor's Note: Previous references ...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 5....</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>Beyonce To Co-Chair 2015 Costume Institute Gal...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>693</th>\n",
              "      <td>30693</td>\n",
              "      <td>Generate a headline for the following article:...</td>\n",
              "      <td>[{'text': '\"Drivin down the wrong side of the ...</td>\n",
              "      <td>FEARLESS MEMOIR: 'World's Fair' (Chapter 18)</td>\n",
              "      <td>feet, claws, fear, terror, budge</td>\n",
              "      <td>My feet refused to move; they were glued to th...</td>\n",
              "      <td>[{'text': 'All I could think about was Daddy. ...</td>\n",
              "      <td>[{'text': 'All I could think about was Daddy. ...</td>\n",
              "      <td>[{'text': 'All I could think about was Daddy. ...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 5....</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2837</th>\n",
              "      <td>302837</td>\n",
              "      <td>Generate a headline for the following article:...</td>\n",
              "      <td>[{'text': 'He was subdued by passengers and cr...</td>\n",
              "      <td>Buzz Aldrin Looked As Baffled As We Felt Durin...</td>\n",
              "      <td>infinity, right, don, know, really</td>\n",
              "      <td>\"This is infinity here. It could be infinity. ...</td>\n",
              "      <td>[{'text': '\"The article is an absolutely disgu...</td>\n",
              "      <td>[{'text': '\"The article is an absolutely disgu...</td>\n",
              "      <td>[{'text': '\"The article is an absolutely disgu...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 5....</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>None</td>\n",
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              "    <tr>\n",
              "      <th>10676</th>\n",
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              "      <td>Generate a headline for the following article:...</td>\n",
              "      <td>[{'text': 'Certain points during your day can ...</td>\n",
              "      <td>Starbucks To Stop Throwing Out Perfectly Good ...</td>\n",
              "      <td>donations, coffee, employees, food, asking</td>\n",
              "      <td>The coffee chain says its employees called for...</td>\n",
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              "    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",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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            ],
            "text/plain": [
              "            id                                              input  \\\n",
              "1909    301909  Generate a headline for the following article:...   \n",
              "7215    307215  Generate a headline for the following article:...   \n",
              "693      30693  Generate a headline for the following article:...   \n",
              "2837    302837  Generate a headline for the following article:...   \n",
              "10676  3010676  Generate a headline for the following article:...   \n",
              "\n",
              "                                                 profile  \\\n",
              "1909   [{'text': 'Bryant's widow testified that she \"...   \n",
              "7215   [{'text': '5. Coming with your significant oth...   \n",
              "693    [{'text': '\"Drivin down the wrong side of the ...   \n",
              "2837   [{'text': 'He was subdued by passengers and cr...   \n",
              "10676  [{'text': 'Certain points during your day can ...   \n",
              "\n",
              "                                                  output  \\\n",
              "1909          33 Phrases To Stop Your Toddler's Tantrums   \n",
              "7215              Beyonce: Met Gala 2013 Honorary Chair!   \n",
              "693         FEARLESS MEMOIR: 'World's Fair' (Chapter 18)   \n",
              "2837   Buzz Aldrin Looked As Baffled As We Felt Durin...   \n",
              "10676  Starbucks To Stop Throwing Out Perfectly Good ...   \n",
              "\n",
              "                                         input_keywords  \\\n",
              "1909   tantrums, toddler, parenting, cooperation, smile   \n",
              "7215           beyonce, gala, chair, costume, institute   \n",
              "693                    feet, claws, fear, terror, budge   \n",
              "2837                 infinity, right, don, know, really   \n",
              "10676        donations, coffee, employees, food, asking   \n",
              "\n",
              "                                           input_summary  \\\n",
              "1909   Here are some parenting phrases that can help ...   \n",
              "7215   Do you think Beyonce is a good choice to co-ch...   \n",
              "693    My feet refused to move; they were glued to th...   \n",
              "2837   \"This is infinity here. It could be infinity. ...   \n",
              "10676  The coffee chain says its employees called for...   \n",
              "\n",
              "                                              contriever  \\\n",
              "1909   [{'text': 'Parents: We are not there to make l...   \n",
              "7215   [{'text': 'Editor's Note: Previous references ...   \n",
              "693    [{'text': 'All I could think about was Daddy. ...   \n",
              "2837   [{'text': '\"The article is an absolutely disgu...   \n",
              "10676  [{'text': 'This is a brutal reminder that glob...   \n",
              "\n",
              "                                         keyword_results  \\\n",
              "1909   [{'text': 'Parents: We are not there to make l...   \n",
              "7215   [{'text': 'Editor's Note: Previous references ...   \n",
              "693    [{'text': 'All I could think about was Daddy. ...   \n",
              "2837   [{'text': '\"The article is an absolutely disgu...   \n",
              "10676  [{'text': 'This is a brutal reminder that glob...   \n",
              "\n",
              "                                         summary_results  \\\n",
              "1909   [{'text': 'Parents: We are not there to make l...   \n",
              "7215   [{'text': 'Editor's Note: Previous references ...   \n",
              "693    [{'text': 'All I could think about was Daddy. ...   \n",
              "2837   [{'text': '\"The article is an absolutely disgu...   \n",
              "10676  [{'text': 'This is a brutal reminder that glob...   \n",
              "\n",
              "                                           style_results  \\\n",
              "1909   {'statistical_analysis': 'Mean Word Length: 4....   \n",
              "7215   {'statistical_analysis': 'Mean Word Length: 5....   \n",
              "693    {'statistical_analysis': 'Mean Word Length: 5....   \n",
              "2837   {'statistical_analysis': 'Mean Word Length: 5....   \n",
              "10676  {'statistical_analysis': 'Mean Word Length: 5....   \n",
              "\n",
              "                                                 prompts  \\\n",
              "1909   Your task is to analyze the provided data to u...   \n",
              "7215   Your task is to analyze the provided data to u...   \n",
              "693    Your task is to analyze the provided data to u...   \n",
              "2837   Your task is to analyze the provided data to u...   \n",
              "10676  Your task is to analyze the provided data to u...   \n",
              "\n",
              "                                            model_output  \n",
              "1909   5 Parenting Phrases That Will Make Your Toddle...  \n",
              "7215   Beyonce To Co-Chair 2015 Costume Institute Gal...  \n",
              "693                                                 None  \n",
              "2837                                                None  \n",
              "10676  Starbucks Employees Want More Food Donations, ...  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "retriever_results = pd.read_pickle(r'contriever/postprompt_100.pkl')\n",
        "\n",
        "retriever_results.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "-f8DwNNsCFo4",
        "outputId": "1a12f7d5-54b6-4fb7-8319-f77b089dce5a"
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        {
          "data": {
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              "version_major": 2,
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        {
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        }
      ],
      "source": [
        "bertScore=[]\n",
        "for index, row in retriever_results.iterrows() :\n",
        "  output = row['output']\n",
        "  modelOp = row['model_output']\n",
        "  #modelOp = row['no_per_model_output']\n",
        "\n",
        "  if output != \"\" and output != None and modelOp != \"\" and modelOp != None :\n",
        "    bertScore.append(getBertScore(output, modelOp))\n",
        "  else :\n",
        "    bertScore.append(0)\n",
        "\n",
        "\n",
        "#bertScore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XoQ0OeT9D5yR"
      },
      "outputs": [],
      "source": [
        "r1lst = []\n",
        "r2lst = []\n",
        "rllst = []\n",
        "\n",
        "rouge = Rouge()\n",
        "\n",
        "for index, row in retriever_results.iterrows() :\n",
        "  output = row['output']\n",
        "  modelOp = row['model_output']\n",
        "  #modelOp = row['no_per_model_output']\n",
        "\n",
        "  if output != \"\" and output != None and modelOp != \"\" and modelOp != None :\n",
        "    scores = rouge.get_scores(output, modelOp)\n",
        "    r1, r2, rl = scores[0]['rouge-1']['r'] , scores[0]['rouge-2']['r'] , scores[0]['rouge-l']['r']\n",
        "  else:\n",
        "    r1, r2, rl = 0,0,0\n",
        "\n",
        "  r1lst.append(r1)\n",
        "  r2lst.append(r2)\n",
        "  rllst.append(r1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RdRpKoD8OCeu"
      },
      "outputs": [],
      "source": [
        "r1lst"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6JOh2lijL1eF"
      },
      "outputs": [],
      "source": [
        "retriever_results['BertScore'] = bertScore\n",
        "retriever_results['r1-rouge'] = r1lst\n",
        "retriever_results['r2-rouge'] = r2lst\n",
        "retriever_results['rl-rouge'] = rllst"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Ir4meY6OIsR"
      },
      "outputs": [],
      "source": [
        "retriever_results['BertScore'] = bertScore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "6JShTTBEWqOq",
        "outputId": "001cca4a-5e65-453c-dc61-2e6760fb90ad"
      },
      "outputs": [
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              "      <td>Beyonce: Met Gala 2013 Honorary Chair!</td>\n",
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              "  [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-f5acd375-74e2-4f16-b8d6-a2fbe6f26568 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            id                                              input  \\\n",
              "1909    301909  Generate a headline for the following article:...   \n",
              "7215    307215  Generate a headline for the following article:...   \n",
              "693      30693  Generate a headline for the following article:...   \n",
              "2837    302837  Generate a headline for the following article:...   \n",
              "10676  3010676  Generate a headline for the following article:...   \n",
              "\n",
              "                                                 profile  \\\n",
              "1909   [{'text': 'Bryant's widow testified that she \"...   \n",
              "7215   [{'text': '5. Coming with your significant oth...   \n",
              "693    [{'text': '\"Drivin down the wrong side of the ...   \n",
              "2837   [{'text': 'He was subdued by passengers and cr...   \n",
              "10676  [{'text': 'Certain points during your day can ...   \n",
              "\n",
              "                                                  output  \\\n",
              "1909          33 Phrases To Stop Your Toddler's Tantrums   \n",
              "7215              Beyonce: Met Gala 2013 Honorary Chair!   \n",
              "693         FEARLESS MEMOIR: 'World's Fair' (Chapter 18)   \n",
              "2837   Buzz Aldrin Looked As Baffled As We Felt Durin...   \n",
              "10676  Starbucks To Stop Throwing Out Perfectly Good ...   \n",
              "\n",
              "                                         input_keywords  \\\n",
              "1909   tantrums, toddler, parenting, cooperation, smile   \n",
              "7215           beyonce, gala, chair, costume, institute   \n",
              "693                    feet, claws, fear, terror, budge   \n",
              "2837                 infinity, right, don, know, really   \n",
              "10676        donations, coffee, employees, food, asking   \n",
              "\n",
              "                                           input_summary  \\\n",
              "1909   Here are some parenting phrases that can help ...   \n",
              "7215   Do you think Beyonce is a good choice to co-ch...   \n",
              "693    My feet refused to move; they were glued to th...   \n",
              "2837   \"This is infinity here. It could be infinity. ...   \n",
              "10676  The coffee chain says its employees called for...   \n",
              "\n",
              "                                              contriever  \\\n",
              "1909   [{'text': 'Parents: We are not there to make l...   \n",
              "7215   [{'text': 'Editor's Note: Previous references ...   \n",
              "693    [{'text': 'All I could think about was Daddy. ...   \n",
              "2837   [{'text': '\"The article is an absolutely disgu...   \n",
              "10676  [{'text': 'This is a brutal reminder that glob...   \n",
              "\n",
              "                                         keyword_results  \\\n",
              "1909   [{'text': 'Parents: We are not there to make l...   \n",
              "7215   [{'text': 'Editor's Note: Previous references ...   \n",
              "693    [{'text': 'All I could think about was Daddy. ...   \n",
              "2837   [{'text': '\"The article is an absolutely disgu...   \n",
              "10676  [{'text': 'This is a brutal reminder that glob...   \n",
              "\n",
              "                                         summary_results  \\\n",
              "1909   [{'text': 'Parents: We are not there to make l...   \n",
              "7215   [{'text': 'Editor's Note: Previous references ...   \n",
              "693    [{'text': 'All I could think about was Daddy. ...   \n",
              "2837   [{'text': '\"The article is an absolutely disgu...   \n",
              "10676  [{'text': 'This is a brutal reminder that glob...   \n",
              "\n",
              "                                           style_results  \\\n",
              "1909   {'statistical_analysis': 'Mean Word Length: 4....   \n",
              "7215   {'statistical_analysis': 'Mean Word Length: 5....   \n",
              "693    {'statistical_analysis': 'Mean Word Length: 5....   \n",
              "2837   {'statistical_analysis': 'Mean Word Length: 5....   \n",
              "10676  {'statistical_analysis': 'Mean Word Length: 5....   \n",
              "\n",
              "                                                 prompts  \\\n",
              "1909   Your task is to analyze the provided data to u...   \n",
              "7215   Your task is to analyze the provided data to u...   \n",
              "693    Your task is to analyze the provided data to u...   \n",
              "2837   Your task is to analyze the provided data to u...   \n",
              "10676  Your task is to analyze the provided data to u...   \n",
              "\n",
              "                                            model_output  BertScore  r1-rouge  \\\n",
              "1909   5 Parenting Phrases That Will Make Your Toddle...   0.763001  0.222222   \n",
              "7215   Beyonce To Co-Chair 2015 Costume Institute Gal...   0.769582  0.090909   \n",
              "693                                                 None   0.000000  0.000000   \n",
              "2837                                                None   0.000000  0.000000   \n",
              "10676  Starbucks Employees Want More Food Donations, ...   0.629246  0.200000   \n",
              "\n",
              "       r2-rouge  rl-rouge  \n",
              "1909        0.0  0.222222  \n",
              "7215        0.0  0.090909  \n",
              "693         0.0  0.000000  \n",
              "2837        0.0  0.000000  \n",
              "10676       0.0  0.200000  "
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "retriever_results.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WZgEGkorTn2Z"
      },
      "outputs": [],
      "source": [
        "retriever_results.to_pickle(\"contriever/metric_output.pkl\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VnsUrY2LT5wC",
        "outputId": "d181b825-6154-4432-d352-4319b80a4177"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.6073589402437211"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def average(list):\n",
        "\n",
        "  return sum(bertScore)/len(bertScore)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yWLmDMYWXKyT",
        "outputId": "02fe8947-1947-4a39-95e8-b0cdcab455ef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean r1-rouge 0.1362561849914791\n",
            "Mean r2-rouge 0.03139023476523477\n",
            "Mean rl-rouge 0.1362561849914791\n",
            "Mean bert 0.5802332758903503\n"
          ]
        }
      ],
      "source": [
        "print(\"Mean r1-rouge {}\".format(retriever_results['r1-rouge'].mean()))\n",
        "print(\"Mean r2-rouge {}\".format(retriever_results['r2-rouge'].mean()))\n",
        "print(\"Mean rl-rouge {}\".format(retriever_results['rl-rouge'].mean()))\n",
        "print(\"Mean bert {}\".format(retriever_results['BertScore'].mean()))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HzuKiXuPXSk0",
        "outputId": "f25a8e8e-9106-486b-e735-e22d6157a4c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean r1 0.16616607925790133\n",
            "Mean r2 0.03828077410394483\n",
            "Mean rl 0.16616607925790133\n",
            "Mean bert 0.7076015559638419\n",
            "None Count:  18\n"
          ]
        }
      ],
      "source": [
        "bertSum = 0\n",
        "r1Sum = 0\n",
        "r2Sum = 0\n",
        "NoneCount = 0\n",
        "r = 0\n",
        "\n",
        "for index, row in retriever_results.iterrows() :\n",
        "  if row['model_output'] == None:\n",
        "    NoneCount += 1\n",
        "  else:\n",
        "    bertSum += row['BertScore']\n",
        "    r1Sum += row['r1-rouge']\n",
        "    r2Sum += row['r2-rouge']\n",
        "    r += row['rl-rouge']\n",
        "\n",
        "noneNull = retriever_results.shape[0] - NoneCount\n",
        "\n",
        "print(\"Mean r1\", r1Sum/noneNull)\n",
        "print(\"Mean r2\", r2Sum/noneNull)\n",
        "print(\"Mean rl\", r/noneNull)\n",
        "print(\"Mean bert\", bertSum/noneNull)\n",
        "print(\"None Count: \", NoneCount)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j47afNRYZKNW"
      },
      "outputs": [],
      "source": [
        "def printScores(df, groundTruthColumn, outputColumn):\n",
        "  bertScore=[]\n",
        "  r1lst = []\n",
        "  r2lst = []\n",
        "  rllst = []\n",
        "\n",
        "  bertSum = 0\n",
        "  r1Sum = 0\n",
        "  r2Sum = 0\n",
        "  NoneCount = 0\n",
        "  r = 0\n",
        "\n",
        "  rouge = Rouge()\n",
        "\n",
        "\n",
        "  for index, row in df.iterrows() :\n",
        "    output = row[groundTruthColumn]\n",
        "    modelOp = row[outputColumn]\n",
        "\n",
        "    if output != \"\" and output != None and modelOp != \"\" and modelOp != None :\n",
        "      bs = getBertScore(output, modelOp)\n",
        "      bertScore.append(bs)\n",
        "      scores = rouge.get_scores(output, modelOp)\n",
        "      r1, r2, rl = scores[0]['rouge-1']['r'] , scores[0]['rouge-2']['r'] , scores[0]['rouge-l']['r']\n",
        "\n",
        "      bertSum += bs\n",
        "      r1Sum += r1\n",
        "      r2Sum += r2\n",
        "      r += rl\n",
        "\n",
        "    else :\n",
        "      bertScore.append(0)\n",
        "      r1, r2, rl = 0,0,0\n",
        "\n",
        "      NoneCount += 1\n",
        "  r1lst.append(r1)\n",
        "  r2lst.append(r2)\n",
        "  rllst.append(rl)\n",
        "\n",
        "  metrics = pd.DataFrame(data=[r1lst, r2lst, rllst,bertScore], columns=('r1-rouge', 'R2', 'RL', 'BertScore'))\n",
        "  print(\"Mean r1-rouge {}\".format(metrics['r1-rouge'].mean()))\n",
        "  print(\"Mean r2-rouge {}\".format(metrics['r2-rouge'].mean()))\n",
        "  print(\"Mean rl-rouge {}\".format(metrics['rl-rouge'].mean()))\n",
        "  # print(\"Mean bert {}\".format(metrics['BertScore'].mean()))\n",
        "\n",
        "\n",
        "  noneNull = retriever_results.shape[0] - NoneCount\n",
        "\n",
        "  print(\"Mean r1\", r1Sum/noneNull)\n",
        "  print(\"Mean r2\", r2Sum/noneNull)\n",
        "  print(\"Mean rl\", r/noneNull)\n",
        "  # print(\"Mean bert\", bertSum/noneNull)\n",
        "  print(\"None Count: \", NoneCount)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pOA1fe0WcOeF"
      },
      "outputs": [],
      "source": [
        "retriever_results = pd.read_pickle(r'postprompt_100.pkl')\n",
        "\n",
        "# retriever_results\n",
        "\n",
        "printScores(retriever_results, 'output', 'no_per_model_output')"
      ]
    }
  ],
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