LLM4BUS_hw2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNv1tTbx4eoY02OjrMwI+hG",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/iqbalmohomed/LLM4BUS/blob/main/LLM4BUS_assn2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5LzomiMVLhdo",
        "outputId": "c797f56e-8c12-4289-c50a-e3f6cd207e30"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (1.12.0)\n",
            "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from openai) (3.7.1)\n",
            "Requirement already satisfied: distro<2,>=1.7.0 in /usr/lib/python3/dist-packages (from openai) (1.7.0)\n",
            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from openai) (0.26.0)\n",
            "Requirement already satisfied: pydantic<3,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from openai) (2.6.1)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from openai) (1.3.0)\n",
            "Requirement already satisfied: tqdm>4 in /usr/local/lib/python3.10/dist-packages (from openai) (4.66.1)\n",
            "Requirement already satisfied: typing-extensions<5,>=4.7 in /usr/local/lib/python3.10/dist-packages (from openai) (4.9.0)\n",
            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai) (3.6)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai) (1.2.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai) (2024.2.2)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai) (1.0.2)\n",
            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai) (0.14.0)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.16.2 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai) (2.16.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install openai"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from openai import OpenAI\n",
        "from google.colab import userdata\n",
        "\n",
        "open_ai_key = userdata.get('open_ai_key')\n",
        "client = OpenAI(api_key=open_ai_key)"
      ],
      "metadata": {
        "id": "p2F6rUxQLnnC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# https://www.kaggle.com/datasets/rmisra/news-headlines-dataset-for-sarcasm-detection?resource=download\n",
        "import json\n",
        "\n",
        "def parse_data(file):\n",
        "    for l in open(file,'r'):\n",
        "        yield json.loads(l)\n",
        "\n",
        "data = list(parse_data('Sarcasm_Headlines_Dataset.json'))"
      ],
      "metadata": {
        "id": "zaKG1nqtdJlD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GTj04Pl-ezRt",
        "outputId": "8040abee-1872-48a8-8374-676c38efc967"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'article_link': 'https://www.huffingtonpost.com/entry/versace-black-code_us_5861fbefe4b0de3a08f600d5',\n",
              " 'headline': \"former versace store clerk sues over secret 'black code' for minority shoppers\",\n",
              " 'is_sarcastic': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "filename = 'Sarcasm_Headlines_Dataset.json'\n",
        "df = pd.read_json(filename,lines=True)"
      ],
      "metadata": {
        "id": "i5jfae4Wfnmh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(column='is_sarcastic')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "ExBdCCP1gLAA",
        "outputId": "f13fb198-5ff0-4c14-f517-4eb066cdc22d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'is_sarcastic'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_serious = df[df.is_sarcastic == 0]\n",
        "df_sarcastic = df[df.is_sarcastic == 1]"
      ],
      "metadata": {
        "id": "h0gANdQSho_J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_sarcastic"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "XZWoUYHOh1vC",
        "outputId": "e5832254-e836-42fb-d45f-20731e6979cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            article_link  \\\n",
              "2      https://local.theonion.com/mom-starting-to-fea...   \n",
              "3      https://politics.theonion.com/boehner-just-wan...   \n",
              "8      https://politics.theonion.com/top-snake-handle...   \n",
              "15     https://entertainment.theonion.com/nuclear-bom...   \n",
              "16     https://www.theonion.com/cosby-lawyer-asks-why...   \n",
              "...                                                  ...   \n",
              "26693  https://www.theonion.com/new-bailiff-tired-of-...   \n",
              "26694  https://www.theonion.com/breaking-the-onion-in...   \n",
              "26695  https://www.theonion.com/seaworld-crowd-applau...   \n",
              "26702  https://politics.theonion.com/pentagon-to-with...   \n",
              "26703  https://www.theonion.com/pope-francis-wearing-...   \n",
              "\n",
              "                                                headline  is_sarcastic  \n",
              "2      mom starting to fear son's web series closest ...             1  \n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
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          "height": 424
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        "id": "_H_OKRJ-iEeO",
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              "    <tr>\n",
              "      <th>26704</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/american-...</td>\n",
              "      <td>american politics in moral free-fall</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26705</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/americas-...</td>\n",
              "      <td>america's best 20 hikes</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26706</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/reparatio...</td>\n",
              "      <td>reparations and obama</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26707</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/israeli-b...</td>\n",
              "      <td>israeli ban targeting boycott supporters raise...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26708</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/gourmet-g...</td>\n",
              "      <td>gourmet gifts for the foodie 2014</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14985 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "        document.querySelector('#df-ea6dcd21-ab72-4848-9cba-9af719f1c752 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ea6dcd21-ab72-4848-9cba-9af719f1c752');\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",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    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",
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              "      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-573c8499-c12c-4b51-aeb0-bf1a77b3e13f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_80a8e2ff-8876-466c-80b7-c1ad90afff78\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_serious')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_80a8e2ff-8876-466c-80b7-c1ad90afff78 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_serious');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_serious.headline[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "hlDsRFX_is6V",
        "outputId": "ba04bc12-e86c-4769-e842-11781bc44e01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"former versace store clerk sues over secret 'black code' for minority shoppers\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def chat(message):\n",
        "    \"\"\"\n",
        "    Send a message to the OpenAI GPT-4 model and return its response.\n",
        "\n",
        "    This function interacts with the OpenAI API, specifically using the GPT-3.5-turbo model. It takes a user's message as input, sends it to the model, and returns the model's text-only response. The function ensures the AI's output is concise by providing a system-level instruction.\n",
        "\n",
        "    Parameters:\n",
        "    message (str): A string containing the user's message to the AI.\n",
        "\n",
        "    Returns:\n",
        "    json: The json  response generated by the GPT model.\n",
        "    \"\"\"\n",
        "    #print(message)\n",
        "    e1 = df_serious.iloc[0].headline\n",
        "    e2 = df_serious.iloc[1].headline\n",
        "    e3 = df_sarcastic.iloc[0].headline\n",
        "    e4 = df_sarcastic.iloc[1].headline\n",
        "\n",
        "    response = client.chat.completions.create(\n",
        "        #model=\"gpt-4-turbo-preview\",\n",
        "\n",
        "        #model=\"gpt-4\",\n",
        "        model=\"gpt-3.5-turbo\",\n",
        "        #response_format={ \"type\": \"json_object\" },\n",
        "        logprobs=True,\n",
        "        messages=[\n",
        "            {\"role\": \"system\", \"content\": f\"\"\"\n",
        "            You are a helpful classification assistant. You only return valid JSON with the fields classification that has the values Serious or Funny, and the field confidence that has the values Low, Medium or High.\n",
        "\n",
        "            Below are examples of news headlines and their classifications. After studying these examples, please classify whether the headline at the end is Serious or Funny.\n",
        "\n",
        "              Example 1:\n",
        "\n",
        "              Text: \"{e1}\"\n",
        "              Classification: Serious\n",
        "\n",
        "              Example 2:\n",
        "\n",
        "              Text: \"{e2}\"\n",
        "              Classification: Serious\n",
        "\n",
        "              Example 3:\n",
        "\n",
        "              Text: \"{e3}\"\n",
        "              Classification: Funny\n",
        "\n",
        "              Example 4:\n",
        "\n",
        "              Text: \"{e4}\"\n",
        "              Classification: Funny\n",
        "\n",
        "              \"\"\"},\n",
        "            {\"role\": \"user\", \"content\": f\"Classify the following headline as Serious or Funny and return with probability as JSON: {message}\"}\n",
        "        ]\n",
        "    )\n",
        "    text_only = response.choices[0].message.content\n",
        "    return json.loads(text_only)"
      ],
      "metadata": {
        "id": "M-0_SOQ-Lw6C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results_serious = []\n",
        "for i in range(2,22):\n",
        "  res = chat(df_serious.iloc[i].headline)\n",
        "  results_serious.append(res)\n",
        "  print(res)\n",
        "  time.sleep(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n-sPm54PtTW5",
        "outputId": "b86b6077-2373-40ac-d9c7-2443fd7c5db1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'classification': 'Funny', 'confidence': 'Medium'}\n",
            "{'classification': 'Serious', 'confidence': 'High'}\n",
            "{'classification': 'Serious', 'confidence': 'Medium'}\n",
            "{'classification': 'Serious', 'confidence': 'Medium'}\n",
            "{'classification': 'Serious', 'confidence': 'Medium'}\n",
            "{'classification': 'Serious', 'confidence': 'High'}\n",
            "{'classification': 'Serious', 'confidence': 'Medium'}\n",
            "{'classification': 'Serious', 'confidence': 'Medium'}\n",
            "{'classification': 'Serious', 'confidence': 'Medium'}\n",
            "{'classification': 'Serious', 'confidence': 'High'}\n",
            "{'classification': 'Serious', 'confidence': 'High'}\n",
            "{'classification': 'Serious', 'confidence': 'High'}\n",
            "{'classification': 'Serious', 'confidence': 'High'}\n",
            "{'classification': 'Funny', 'confidence': 'Medium'}\n",
            "{'classification': 'Serious', 'confidence': 'Medium'}\n",
            "{'classification': 'Serious', 'confidence': 'Medium'}\n",
            "{'classification': 'Serious', 'confidence': 'High'}\n",
            "{'classification': 'Serious', 'confidence': 'High'}\n",
            "{'classification': 'Serious', 'confidence': 'Medium'}\n",
            "{'classification': 'Funny', 'confidence': 'Medium'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time"
      ],
      "metadata": {
        "id": "EwormqHGusut"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results_serious_df = pd.DataFrame.from_records(results_serious)\n",
        "results_serious_df['classification'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "FvOofnUeurJw",
        "outputId": "70124dae-fdea-4d50-fa07-eb6ace9acead"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results_funny = []\n",
        "for i in range(2,22):\n",
        "  res = chat(df_sarcastic.iloc[i].headline)\n",
        "  results_funny.append(res)\n",
        "  print(res)\n",
        "  time.sleep(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DEuu5AHgv7DI",
        "outputId": "7631132c-0808-43c1-c736-68679d774ca8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'classification': 'Funny', 'confidence': 'Medium'}\n",
            "{'classification': 'Serious', 'confidence': 'Low'}\n",
            "{'classification': 'Funny', 'confidence': 'High'}\n",
            "{'classification': 'Funny', 'confidence': 'Medium'}\n",
            "{'classification': 'Funny', 'confidence': 'Medium'}\n",
            "{'classification': 'Funny', 'confidence': 'Medium'}\n",
            "{'classification': 'Serious', 'confidence': 'Medium'}\n",
            "{'classification': 'Serious', 'confidence': 'High'}\n",
            "{'classification': 'Funny', 'confidence': 'Medium'}\n",
            "{'classification': 'Funny', 'confidence': 'Medium'}\n",
            "{'classification': 'Funny', 'confidence': 'High'}\n",
            "{'classification': 'Serious', 'confidence': 'High'}\n",
            "{'classification': 'Serious', 'confidence': 'Medium'}\n",
            "{'classification': 'Serious', 'confidence': 'High'}\n",
            "{'classification': 'Funny', 'confidence': 'High'}\n",
            "{'classification': 'Serious', 'confidence': 'Medium'}\n",
            "{'classification': 'Funny', 'confidence': 'Medium'}\n",
            "{'classification': 'Funny', 'confidence': 'Medium'}\n",
            "{'classification': 'Funny', 'confidence': 'Medium'}\n",
            "{'classification': 'Funny', 'confidence': 'Medium'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results_funny_df = pd.DataFrame.from_records(results_funny)\n",
        "results_funny_df['classification'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "X85gFkQBwMNR",
        "outputId": "fbebad16-f887-483c-92f3-ee11e94ac3bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "errors_funny = [3,8,9,13,14,15,17]"
      ],
      "metadata": {
        "id": "dwTFp81vwRvZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for k in errors_funny:\n",
        "  print(df_sarcastic.iloc[k].headline)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TFS9nain76Lr",
        "outputId": "32cdb371-d7c8-4706-d9ba-73beee0c9f4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nuclear bomb detonates during rehearsal for 'spider-man' musical\n",
            "ex-con back behind bar\n",
            "after careful consideration, bush recommends oil drilling\n",
            "christian bale visits sikh temple victims\n",
            "'right to live life in complete, stunned horror,' added to constitution\n",
            "nasa now almost positive mars is rocky\n",
            "longtime teacher retires without changing a single student's life\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "errors_serious = [2, 15, 21]\n",
        "for k in errors_serious:\n",
        "  print(df_serious.iloc[k].headline)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "msKV1_m98cm7",
        "outputId": "df7d5558-e3e4-429c-dd28-2e39e44366df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "j.k. rowling wishes snape happy birthday in the most magical way\n",
            "this is why you shouldn't go to the circus\n",
            "gillian jacobs on what it's like to kiss adam brody\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Error rate for Funny headlines: 7/20 = 35%\n",
        "\n",
        "Error rate for Serious headlines: 3/20 = 15%\n"
      ],
      "metadata": {
        "id": "MvQ4DvGTz7BX"
      }
    }
  ]
}