{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZBiNdra-AOT2"
      },
      "source": [
        "# Import Library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "taECrFNE9yxz"
      },
      "outputs": [],
      "source": [
        "# Import necessary libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9MK_JVsjBjM-"
      },
      "source": [
        "# Import Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "XJVoE7SgBlDG",
        "outputId": "0974b5a2-c0fe-469c-99a1-9b691153c687"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset shape: (500, 16)\n",
            "\n",
            "First 5 rows:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<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>image_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>prompt</th>\n",
              "      <th>likes</th>\n",
              "      <th>shares</th>\n",
              "      <th>comments</th>\n",
              "      <th>platform</th>\n",
              "      <th>generation_time</th>\n",
              "      <th>gpu_usage</th>\n",
              "      <th>file_size_kb</th>\n",
              "      <th>resolution</th>\n",
              "      <th>style_accuracy_score</th>\n",
              "      <th>is_hand_edited</th>\n",
              "      <th>ethical_concerns_flag</th>\n",
              "      <th>creation_date</th>\n",
              "      <th>top_comment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>77ce5c72-eb45-4651-bcb1-c0677c0fceaf</td>\n",
              "      <td>6a7adf3d</td>\n",
              "      <td>Studio Ghibli-inspired ocean with giant fish</td>\n",
              "      <td>916</td>\n",
              "      <td>410</td>\n",
              "      <td>555</td>\n",
              "      <td>Reddit</td>\n",
              "      <td>4.80</td>\n",
              "      <td>49</td>\n",
              "      <td>1684</td>\n",
              "      <td>1024x1024</td>\n",
              "      <td>89</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2025-03-11</td>\n",
              "      <td>So nostalgic, feels like childhood memories. 🎥...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7d66c67f-0d11-4ef9-895c-d865ef11fe40</td>\n",
              "      <td>523b8706</td>\n",
              "      <td>Ghibli-style village at sunset</td>\n",
              "      <td>2965</td>\n",
              "      <td>1361</td>\n",
              "      <td>417</td>\n",
              "      <td>Reddit</td>\n",
              "      <td>11.11</td>\n",
              "      <td>81</td>\n",
              "      <td>2808</td>\n",
              "      <td>1024x1024</td>\n",
              "      <td>92</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>2025-03-11</td>\n",
              "      <td>Absolutely stunning! Love the details. 🎨 #5729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>d7978afd-3932-4cce-9a21-5f9bf2bc1f64</td>\n",
              "      <td>0e02592a</td>\n",
              "      <td>A lone traveler exploring an enchanted ruin</td>\n",
              "      <td>4727</td>\n",
              "      <td>655</td>\n",
              "      <td>785</td>\n",
              "      <td>Instagram</td>\n",
              "      <td>5.56</td>\n",
              "      <td>41</td>\n",
              "      <td>1800</td>\n",
              "      <td>2048x2048</td>\n",
              "      <td>61</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2025-03-06</td>\n",
              "      <td>Is this AI or hand-painted? Incredible! #8001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>cb34636a-a15c-4b15-999c-759dbb8896fe</td>\n",
              "      <td>9ed78a42</td>\n",
              "      <td>Spirited Away-style bustling market street</td>\n",
              "      <td>1629</td>\n",
              "      <td>1954</td>\n",
              "      <td>212</td>\n",
              "      <td>TikTok</td>\n",
              "      <td>12.45</td>\n",
              "      <td>88</td>\n",
              "      <td>479</td>\n",
              "      <td>2048x2048</td>\n",
              "      <td>76</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2025-03-23</td>\n",
              "      <td>Is this AI or hand-painted? Incredible! #5620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7511fbb8-db05-4584-a3a4-e8bb525ed58b</td>\n",
              "      <td>69ec8f02</td>\n",
              "      <td>Magical Ghibli forest with floating lanterns</td>\n",
              "      <td>2573</td>\n",
              "      <td>1281</td>\n",
              "      <td>913</td>\n",
              "      <td>TikTok</td>\n",
              "      <td>4.80</td>\n",
              "      <td>64</td>\n",
              "      <td>1789</td>\n",
              "      <td>512x512</td>\n",
              "      <td>58</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2025-03-06</td>\n",
              "      <td>This looks straight out of a Ghibli movie! 🌟 #...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               image_id   user_id  \\\n",
              "0  77ce5c72-eb45-4651-bcb1-c0677c0fceaf  6a7adf3d   \n",
              "1  7d66c67f-0d11-4ef9-895c-d865ef11fe40  523b8706   \n",
              "2  d7978afd-3932-4cce-9a21-5f9bf2bc1f64  0e02592a   \n",
              "3  cb34636a-a15c-4b15-999c-759dbb8896fe  9ed78a42   \n",
              "4  7511fbb8-db05-4584-a3a4-e8bb525ed58b  69ec8f02   \n",
              "\n",
              "                                         prompt  likes  shares  comments  \\\n",
              "0  Studio Ghibli-inspired ocean with giant fish    916     410       555   \n",
              "1                Ghibli-style village at sunset   2965    1361       417   \n",
              "2   A lone traveler exploring an enchanted ruin   4727     655       785   \n",
              "3    Spirited Away-style bustling market street   1629    1954       212   \n",
              "4  Magical Ghibli forest with floating lanterns   2573    1281       913   \n",
              "\n",
              "    platform  generation_time  gpu_usage  file_size_kb resolution  \\\n",
              "0     Reddit             4.80         49          1684  1024x1024   \n",
              "1     Reddit            11.11         81          2808  1024x1024   \n",
              "2  Instagram             5.56         41          1800  2048x2048   \n",
              "3     TikTok            12.45         88           479  2048x2048   \n",
              "4     TikTok             4.80         64          1789    512x512   \n",
              "\n",
              "   style_accuracy_score is_hand_edited ethical_concerns_flag creation_date  \\\n",
              "0                    89            Yes                   Yes    2025-03-11   \n",
              "1                    92            Yes                    No    2025-03-11   \n",
              "2                    61             No                    No    2025-03-06   \n",
              "3                    76             No                    No    2025-03-23   \n",
              "4                    58             No                   Yes    2025-03-06   \n",
              "\n",
              "                                         top_comment  \n",
              "0  So nostalgic, feels like childhood memories. 🎥...  \n",
              "1     Absolutely stunning! Love the details. 🎨 #5729  \n",
              "2      Is this AI or hand-painted? Incredible! #8001  \n",
              "3      Is this AI or hand-painted? Incredible! #5620  \n",
              "4  This looks straight out of a Ghibli movie! 🌟 #...  "
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Load the dataset\n",
        "df = pd.read_csv('dataset/ai_ghibli_trend_dataset_v2.csv')\n",
        "print(f\"Dataset shape: {df.shape}\")\n",
        "print(\"\\nFirst 5 rows:\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qRbZogQMOBHN"
      },
      "source": [
        "# Data Preprocessing and Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fRPjDRY2OC_T",
        "outputId": "5ef3e930-cc5c-49ab-ad7e-6b5a24be3811"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============================================================\n",
            "FEATURE ENGINEERING\n",
            "============================================================\n",
            "Features created successfully!\n",
            "Total features: 33\n"
          ]
        }
      ],
      "source": [
        "# Data Preprocessing and Feature Engineering\n",
        "\n",
        "# Feature Engineering\n",
        "print(\"=\"*60)\n",
        "print(\"FEATURE ENGINEERING\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Split resolution into width and height\n",
        "df[['width', 'height']] = df['resolution'].str.split('x', expand=True).astype(int)\n",
        "\n",
        "# Convert categorical binary features to numeric\n",
        "df['is_hand_edited'] = (df['is_hand_edited'] == 'Yes').astype(int)\n",
        "df['ethical_concerns_flag'] = (df['ethical_concerns_flag'] == 'Yes').astype(int)\n",
        "\n",
        "# Extract temporal features\n",
        "df['creation_date'] = pd.to_datetime(df['creation_date'])\n",
        "df['day_of_week'] = df['creation_date'].dt.dayofweek\n",
        "df['month'] = df['creation_date'].dt.month\n",
        "df['hour'] = df['creation_date'].dt.hour\n",
        "\n",
        "# Create derived features (WITHOUT using likes)\n",
        "df['aspect_ratio'] = df['width'] / df['height']\n",
        "df['total_pixels'] = df['width'] * df['height']\n",
        "df['is_square'] = (df['width'] == df['height']).astype(int)\n",
        "df['is_weekend'] = (df['day_of_week'] >= 5).astype(int)\n",
        "\n",
        "# Technical efficiency features (not dependent on engagement)\n",
        "df['file_density'] = df['file_size_kb'] / (df['total_pixels'] / 1000 + 1)\n",
        "df['gpu_efficiency'] = df['generation_time'] / (df['gpu_usage'] + 1)\n",
        "\n",
        "# Temporal cyclical features\n",
        "df['month_sin'] = np.sin(2 * np.pi * df['month'] / 12)\n",
        "df['month_cos'] = np.cos(2 * np.pi * df['month'] / 12)\n",
        "df['day_sin'] = np.sin(2 * np.pi * df['day_of_week'] / 7)\n",
        "df['day_cos'] = np.cos(2 * np.pi * df['day_of_week'] / 7)\n",
        "df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)\n",
        "df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)\n",
        "\n",
        "print(f\"Features created successfully!\")\n",
        "print(f\"Total features: {df.shape[1]}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mWdj1slFZYSG"
      },
      "source": [
        "# Target Variable Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "B-k5NDAAhL1J",
        "outputId": "dd3928da-c0e5-48e4-c6ff-6f851768461b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============================================================\n",
            "TARGET VARIABLES ANALYSIS\n",
            "============================================================\n",
            "\n",
            "Likes Statistics:\n",
            "Mean: 2601.26\n",
            "Median: 2566.50\n",
            "Std Dev: 1429.43\n",
            "Skewness: -0.02\n",
            "\n",
            "Shares Statistics:\n",
            "Mean: 1040.18\n",
            "Median: 1092.00\n",
            "Std Dev: 562.67\n",
            "Skewness: -0.14\n"
          ]
        },
        {
          "data": {
            "image/png": 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AAHC7ep5OANVHenq6rFZruTEWi0WhoaE1cnkAUNdwnAUAAEB1RlEKkk7/4nLbbeN0/Hh+uXGNGpm1aNH88/4Fxt3LA4C6huMsAAAAqjuKUpAkWa1WHT+eL7P5Qfn5RZUak5t7WMePz5bVaj3vX17cvTwAqGs4zgIAAKC6oygFB35+UQoIuKjM6fnl/8G92i8PAOoajrMAAACorjz6oPP169dr0KBBioyMlMlk0tKlS+3TCgoK9Oijj6pjx44KCAhQZGSkRowYoSNHjpTb5vTp02UymRw+bdq0qeI1AQAAAAAAQEV4tCiVk5Ojzp07a968eSWmnTx5Ulu3btU///lPbd26VZ988on27t2r66+//pzttm/fXkePHrV/NmzYUBXpAwAAAAAAoJI8evtefHy84uPjS50WHByslStXOox77bXXdNlllyk5OVnNmjUrs9169eopIiLCpbkCAAAAAADAdTx6pVRFZWZmymQyKSQkpNy4ffv2KTIyUhdeeKFuv/12JScnuydBAAAAAAAAOKXGPOg8Ly9Pjz76qIYNGyaLxVJmXPfu3ZWQkKDWrVvr6NGjmjFjhnr16qVffvlFQUFBpc6Tn5+v/DOe9Gq1WiVJNptNNpvNqfxsNpsMw3A6vroxDOO/z+AyZDKVvg6np5lcsp6uWt7Z7ZTVnitzR+mqch9w9vvi5eVVbkxxXG39LtT045CruPJ4VtG2zrf/PZl7dTmunw9X7QN1fR8CAABwlxpRlCooKNDNN98swzA0f/78cmPPvB2wU6dO6t69u6Kjo/XRRx9pzJgxpc4za9YszZgxo8T49PR05eXlOZWjzWZTZmamDOP0L8Y1TVZWllq2jFJAQJZ8fdNKjcnLy1JOTpSysrKUllZ6jLuXd3Y7jRuf3gZnXwToytxRuqrcB5z5vgQHF8nLq5ViYk7KYil7G9fm70JNPw65iiuPZxVpKzs7W4WFhefV/57KvTod18+Hq/aBrKwsF2YFAACAslT7olRxQSopKUmrV68u9yqp0oSEhKhVq1bav39/mTFTpkzR5MmT7cNWq1VRUVEKDQ11enk2m00mk0mhoaE18pfB7Oxs7dt3WCEhQQoICCs1JicnWxkZhxUUFKSwsNJj3L28M9sJDAyTyWTSH3+EyjC8KtwWzk9V7gPOfF+OHfPWjh2/yWbzV+PGZW/j2vxdqOnHIVdx5fGsIm0FBgYqMDDwvPrfU7lXp+P6+XDVPuDr6+vCrAAAAFCWal2UKi5I7du3T2vWrFGjRo0q3EZ2drYOHDig4cOHlxljNptlNptLjPfy8qrQSa3JZKrwPNXF/249MZUo6BQ7Pc2wr2d1WN7Z7fzvX68Kt4XzV1X7gLPfl9O37pQdUxxXm78LNfk45CquPJ5VtK3z7X9P5l5djuvnyxX7QF3efwAAANzJo2dd2dnZSkxMVGJioiTp4MGDSkxMVHJysgoKCnTTTTfp559/1r///W8VFRUpJSVFKSkpOnXqlL2Nfv366bXXXrMPP/TQQ1q3bp0OHTqkjRs36m9/+5u8vb01bNgwd68eAAAAAAAAyuDRK6V+/vln9e3b1z5cfAvdyJEjNX36dH3++eeSpC5dujjMt2bNGvXp00eSdODAAR07dsw+7Y8//tCwYcN0/PhxhYaGqmfPntq8ebNCQ0OrdmUAAAAAAADgNI8Wpfr06fPfh1KXrrxpxQ4dOuQwvHjx4vNNCwAAAAAAAFWsUrfv/f77767OAwAAAAAAAHVIpa6UatGihXr37q0xY8bopptu4i01HpSeni6r1VpujMVi4fZFF6Pf4Qnn+t4ZhqGsrCx5eXnVujcLom5x5hgr1c3j7Pz58zV//nz7leLt27fX1KlTFR8fL0nKy8vTgw8+qMWLFys/P19xcXF6/fXXFR4ebm8jOTlZ48aN05o1axQYGKiRI0dq1qxZqlfvf6eFa9eu1eTJk7Vr1y5FRUXpiSee0KhRo9y5qgAAoA6oVFFq69atWrhwoSZPnqwJEybolltu0ZgxY3TZZZe5Oj+UIz09XbfdNk7Hj+eXG9eokVmLFs2vcyfuVYV+hyc4870zmUxq2TJKJ06k6d//fp3vHmokZ4+xUt08zjZt2lTPPvusWrZsKcMw9O677+qGG27Qtm3b1L59e02aNElfffWVlixZouDgYE2YMEE33nijvv/+e0lSUVGRBg4cqIiICG3cuFFHjx7ViBEjVL9+fT3zzDOSTr94ZuDAgbrnnnv073//W6tWrdLYsWPVpEkTxcXFeXL1AQBALVOpolSXLl00d+5czZ49W59//rkSEhLUs2dPtWrVSnfeeaeGDx9ep04QPcVqter48XyZzQ/Kzy+q1Jjc3MM6fny2rFYr28RF6Hd4gjPfO5PJUP36B3X8+Kt891BjOfNdl+rucXbQoEEOw08//bTmz5+vzZs3q2nTpnr77be1aNEiXXXVVZKkhQsXqm3bttq8ebN69OihFStW6Ndff9W3336r8PBwdenSRU8++aQeffRRTZ8+XT4+PlqwYIFiYmI0e/ZsSVLbtm21YcMGzZkzh6IUAABwqfN60Hm9evV04403auDAgXr99dc1ZcoUPfTQQ3r88cd1880367nnnlOTJk1clSvK4OcXpYCAi8qcnn/uPzajEuh3eEJ53zuTySYfnyw3ZwRUjXMdYyWOs0VFRVqyZIlycnIUGxurLVu2qKCgQP3797fHtGnTRs2aNdOmTZvUo0cPbdq0SR07dnS4nS8uLk7jxo3Trl27dPHFF2vTpk0ObRTHTJw4sdx88vPzlX/GRim+BdNms8lms7lgjQGcrXjfqo77mc1mk2EY1S4vqXr3G1BbOLtvnVdR6ueff9Y777yjxYsXKyAgQA899JDGjBmjP/74QzNmzNANN9ygH3/88XwWAQAAgDPs3LlTsbGxysvLU2BgoD799FO1a9dOiYmJ8vHxUUhIiEN8eHi4UlJSJEkpKSkOBani6cXTyouxWq3Kzc2Vn59fqXnNmjVLM2bMKDE+PT1deXl5lVpXAOU7ceKE/d+0tDQPZ+PIZrMpMzNThmHIy6tS79eqMtW534DaIivLuT+WV6oo9dJLL2nhwoXau3evrr32Wr333nu69tpr7QebmJgYJSQkqHnz5pVpHgAAAGVo3bq1EhMTlZmZqY8//lgjR47UunXrPJ2WpkyZosmTJ9uHrVaroqKiFBoaKovF4sHMgNqrYcOG9n+r20tObDabTCaTQkNDq11Rqjr3G1BbOPtCvEoVpebPn68777xTo0aNKvP2vLCwML399tuVaR4AAABl8PHxUYsWLSRJ3bp1008//aS5c+fqlltu0alTp5SRkeFwtVRqaqoiIiIkSRERESWuYk9NTbVPK/63eNyZMRaLpcyrpCTJbDbLbDaXGO/l5VXtfiEFaovifau67mcmk6la5lbd+w2oDZzdtyq1B+7bt09Tpkwp93lRPj4+GjlyZGWaBwAAgJNsNpvy8/PVrVs31a9fX6tWrbJP27t3r5KTkxUbGytJio2N1c6dOx1uV1m5cqUsFovatWtnjzmzjeKY4jYAAABcpVJXSi1cuFCBgYEaOnSow/glS5bo5MmTFKMAAACqwJQpUxQfH69mzZopKytLixYt0tq1a7V8+XIFBwdrzJgxmjx5sho2bCiLxaL77rtPsbGx6tGjhyRpwIABateunYYPH67nn39eKSkpeuKJJzR+/Hj7VU733HOPXnvtNT3yyCO68847tXr1an300Uf66quvPLnqAACgFqrUlVKzZs1S48aNS4wPCwvTM888c95JAQAAoKS0tDSNGDFCrVu3Vr9+/fTTTz9p+fLluvrqqyVJc+bM0XXXXachQ4boyiuvVEREhD755BP7/N7e3vryyy/l7e2t2NhY3XHHHRoxYoRmzpxpj4mJidFXX32llStXqnPnzpo9e7beeustxcXFuX19AQBA7VapK6WSk5MVExNTYnx0dLSSk5PPOykAAACUdK7ndfr6+mrevHmaN29emTHR0dH6+uuvy22nT58+2rZtW6VyBAAAcFalrpQKCwvTjh07Sozfvn27GjVqdN5JAQAAAAAAoHarVFFq2LBhuv/++7VmzRoVFRWpqKhIq1ev1gMPPKBbb73V1TkCAAAAAACglqnU7XtPPvmkDh06pH79+qlevdNN2Gw2jRgxgmdKAQAAAAAA4JwqVZTy8fHRhx9+qCeffFLbt2+Xn5+fOnbsqOjoaFfnBwAAAAAAgFqoUkWpYq1atVKrVq1clQtQqxQU5CspKancGIvFotDQUDdlhIpIT0+X1WotN6a6br/CQr571ZEz36mkpCQVFha6KaPT3H2sOtfyPNEHAAAA8IxKFaWKioqUkJCgVatWKS0tTTabzWH66tWrXZIcUFOdOnVcSUm/6777npXZbC4zrlEjsxYtmk9xoJpJT0/XbbeN0/Hj+eXGVcftV1iYpaSkg3z3qhlnv1P5+Tk6fDhVwcHlx7mKu49VzizP3X0AAAAAz6lUUeqBBx5QQkKCBg4cqA4dOshkMrk6L6BGKyrKVmGhj3x8JikkpPSrCXNzD+v48dmyWq0UBqoZq9Wq48fzZTY/KD+/qFJjquv2s9ny+O5VQ858pyTpr782q7DwaRUWFrklL3cfq5xZnrv7AAAAAJ5TqaLU4sWL9dFHH+naa691dT5AreLr21QBAReVOT2fCwGqNT+/qBq7/fjuVU/n+k7l5pZ/G11Vcff3pbzleaoPPKGoqEg7d+5UdHS0GjRo4Ol0AAAA3M6rMjP5+PioRYsWrs4FAACg1po4caLefvttSacLUr1791bXrl0VFRWltWvXejY5AAAAD6hUUerBBx/U3LlzZRiGq/MBAAColT7++GN17txZkvTFF1/o4MGD2rNnjyZNmqR//OMfHs4OAADA/Sp1+96GDRu0Zs0affPNN2rfvr3q16/vMP2TTz5xSXIAAAC1xbFjxxQRESFJ+vrrrzV06FC1atVKd955p+bOnevh7AAAANyvUkWpkJAQ/e1vf3N1LgAAALVWeHi4fv31VzVp0kTLli3T/PnzJUknT56Ut7e3h7MDAABwv0oVpRYuXOjqPAAAAGq10aNH6+abb1aTJk1kMpnUv39/SdIPP/ygNm3aeDg7AAAA96tUUUqSCgsLtXbtWh04cEC33XabgoKCdOTIEVksFgUGBroyRwAAgBpv+vTp6tChgw4fPqyhQ4fKbDZLkry9vfXYY495ODsAAAD3q1RRKikpSddcc42Sk5OVn5+vq6++WkFBQXruueeUn5+vBQsWuDpPAACAGu+mm26SJOXl5dnHjRw50lPpAAAAeFSl3r73wAMP6JJLLtFff/0lPz8/+/i//e1vWrVqlcuSAwAAqC2Kior05JNP6oILLlBgYKB+//13SdI///lPvf322x7ODgAAwP0qVZT67rvv9MQTT8jHx8dhfPPmzfXnn3863c769es1aNAgRUZGymQyaenSpQ7TDcPQ1KlT1aRJE/n5+al///7at2/fOdudN2+emjdvLl9fX3Xv3l0//vij0zkBAABUhaeffloJCQl6/vnnHc6hOnTooLfeesuDmQEAAHhGpYpSNptNRUVFJcb/8ccfCgoKcrqdnJwcde7cWfPmzSt1+vPPP69XXnlFCxYs0A8//KCAgADFxcU5XPJ+tg8//FCTJ0/WtGnTtHXrVnXu3FlxcXFKS0tzOi8AAABXe++99/Svf/1Lt99+u8Pb9jp37qw9e/Z4MDMAAADPqNQzpQYMGKCXX35Z//rXvyRJJpNJ2dnZmjZtmq699lqn24mPj1d8fHyp0wzD0Msvv6wnnnhCN9xwg6TTJ3Ph4eFaunSpbr311lLne+mll3TXXXdp9OjRkqQFCxboq6++0jvvvMNDRAEAgMf8+eefatGiRYnxNptNBQUFHsgIQG0QEWiSX8Zv0pFKXW9QdQxD9U6ckIqOSiaTp7Nx4JfxmyICq1dOQF1VqaLU7NmzFRcXp3bt2ikvL0+33Xab9u3bp8aNG+s///mPSxI7ePCgUlJS7K9LlqTg4GB1795dmzZtKrUoderUKW3ZskVTpkyxj/Py8lL//v21adMml+QFAABQGe3atdN3332n6Ohoh/Eff/yxLr74Yg9lBaCm+3s3H7Vd/3dpvaczceQlqbGnkyhDW53uNwCeV6miVNOmTbV9+3YtXrxYO3bsUHZ2tsaMGaPbb7/d4cHn5yMlJUWSFB4e7jA+PDzcPu1sx44dU1FRUanzlHdZfH5+vvLz8+3DVqtV0um/XNpsNqfytdlsMgzD6XhnHDt2zJ5LaZKSkmSzFclkMmQylb7c09NM58zNME7HuaItZ7hqeWe3U1Z7nsjdy8vLbf1ZHVTFPlDMVX1eHFfR71VZ7RQVndKhQ4dkGEa5+VssFjVufH6nZc7lZKu2371zHc8k1/ST5NrtV9HjbFl96kxOxW25avu58ljlyr5yZnmV3ZdddRyqqv1i6tSpGjlypP7880/ZbDZ98skn2rt3r9577z19+eWXVbJMALXfG1tO6ZapCWrbpo2nU3FgMwydOHFCDRs2lFc1u1Jq9549emP2bbre04kAqFxRSpLq1aunO+64w5W5eMysWbM0Y8aMEuPT09PLfX7VmWw2mzIzM2UYp0+mz1dmZqZefHG+srLKvpy/oCBPwcF+atr0hAIDA0uNycvLUk5OlLKyssp9rlZWVpZatoxSQECWfH1Lj3O2LWe4anlnt9O4ceZ/f2nyqnBbrsw9OLhIXl6tFBNzUhZL1fdndeDqfeBMrupzqXLfq9JkZx9RYaFZr7/+oerXr19u/kFB9fXQQ+MUHBxcblx5nMlJsikgwKbOnVupefPq891z5ngmuaafJNduv4ocZ7Ozs1VYWFjqPuDc9nPtscNV+40r+8qZ5Z3Pvuyq41BWVlal5y3PDTfcoC+++EIzZ85UQECApk6dqq5du+qLL77Q1VdfXSXLBFD7pWQbyg1pJUV28XQqjmw2FXqnSWFhkovPDc9XbopNKdnl/1ERgHtUqij13nvvlTt9xIgRlUrmTBEREZKk1NRUNWnSxD4+NTVVXbp0KXWexo0by9vbW6mpqQ7jU1NT7e2VZsqUKZo8ebJ92Gq1KioqSqGhobJYLE7la7PZZDKZFBoa6pJfyLOzs7V16+8ymyfJzy+q1JiMjB+0d+8sFRT4qnHjsFJjcnKylZFxWEFBQQoLKz2meHn79h1WSEiQAgLOry1nuGp5Z7YTGBgmk8mkP/4IlWF4VbgtV+Z+7Ji3duz4TTab/3lvm5rC1fvAmVzV51LFv1dlL2+Xduz4Xa1b36OQkFZlLi8397Dy8+fI29v7vLazMzmZTDYFB3tp+/bfVFRUfb57zhzPXNVPxctz1faryHE2MDBQgYGBpe4DzuR0Oi/XHTtctd+4sq+cW17l92VXHYd8fX0rPW95/vjjD/Xq1UsrV64sMW3z5s3q0aNHlSwXAACguqpUUeqBBx5wGC4oKNDJkyfl4+Mjf39/lxSlYmJiFBERoVWrVtmLUFarVT/88IPGjRtX6jw+Pj7q1q2bVq1apcGDB0s6fYK6atUqTZgwocxlmc1mmc3mEuO9vLwqdFJrMpkqPE95bRmGIV/fZvL3v6jUmJMnk/97q4KpRBGm2Olphj23cy3PFW05w1XLO7ud//3rVeG2XJ27q7ZNTeLKfeDsdl3R58VxFf1elbc8H58o+fuXfHDxmXF5eee/nZ3J6cy8qtN3z5njmav66czluWL7VfQ4W9Y+4Int58pjlSv7yhUxxXGl9YMrjkNVtV8MGDBAGzZsUMOGDR3Gf//99xo4cKAyMjKqZLkAAADVVaXOuv766y+HT3Z2tvbu3auePXtW6EHn2dnZSkxMVGJioqTTDzdPTExUcnKyTCaTJk6cqKeeekqff/65du7cqREjRigyMtJecJKkfv366bXXXrMPT548WW+++abeffdd7d69W+PGjVNOTo79bXwAAACe0KNHDw0YMMDh9sD169fr2muv1bRp0zyYGQAAgGdU+plSZ2vZsqWeffZZ3XHHHeU+VPxMP//8s/r27WsfLr6FbuTIkUpISNAjjzyinJwc3X333crIyFDPnj21bNkyh8vqDxw4oGPHjtmHb7nlFqWnp2vq1KlKSUlRly5dtGzZshIPPwcAAHCnt956SzfddJMGDRqk5cuXa+PGjbr++uv11FNPlbgKHQAAoC5wWVFKOv3w8yNHjjgd36dPn3Lf5GMymTRz5kzNnDmzzJhDhw6VGDdhwoRyb9cDAABwNy8vLy1evFgDBw7UVVddpR07dmjWrFmcswAAgDqrUkWpzz//3GHYMAwdPXpUr732mq644gqXJAYAAFDT7dixo8S46dOna9iwYbrjjjt05ZVX2mM6derk7vQAAAA8qlJFqTOf6STJ/qabq666SrNnz3ZFXgAAADVely5d7A+dL1Y8/MYbb+hf//qX/YHtRUVFHswUAADA/SpVlLLZbK7OAwAAoNY5ePCgp1MAAACotlz6TCkAAAD8T3R0tKdTAAAAqLYqVZQqfkueM1566aXKLAIAAKDG+/zzzxUfH6/69euXeCbn2a6//no3ZQUAAFA9VKootW3bNm3btk0FBQVq3bq1JOm3336Tt7e3unbtao8zmUyuyRIAAKAGGjx4sFJSUhQWFlbimZxn4plSAACgLqpUUWrQoEEKCgrSu+++qwYNGkiS/vrrL40ePVq9evXSgw8+6NIkAQAAaqIzn8NZ1jM5Dx8+rJkzZ7orJQAAgGrDqzIzzZ49W7NmzbIXpCSpQYMGeuqpp3j7HgAAQAWcOHFC77zzjqfTAAAAcLtKFaWsVqvS09NLjE9PT1dWVtZ5JwUAAAAAAIDarVK37/3tb3/T6NGjNXv2bF122WWSpB9++EEPP/ywbrzxRpcmiJonPT1dVqu13JikpCQVFhaes62CgnwlJSWddzvV1bnWr9ipU6fk4+Nz3jEWi0WhoaEVyrE2cvf3ypntXB23jTP7slQ9cwcAAABQ/VWqKLVgwQI99NBDuu2221RQUHC6oXr1NGbMGL3wwgsuTRA1S3p6um67bZyOH88vNy4/P0eHD6cqOLjsuFOnjisp6Xfdd9+zMpvNlW6nunJm/aTTBY0jRw7qggtaqF690ndZZ2IkqVEjsxYtml+nCwju/l45u52r27Zxdl+Wql/uAAAAAGqGShWl/P399frrr+uFF17QgQMHJEkXXXSRAgICXJocah6r1arjx/NlNj8oP7+oMuP++muzCgufVmFh2W8aKirKVmGhj3x8JikkpFWl26munFk/6fQ65uY+LW/v+8vth3PF5OYe1vHjs2W1Wut08cDd3ytnllcdt42z+3J1zB2obs51FXlGRoZ7EgEAAKhmKlWUKnb06FEdPXpUV155pfz8/GQYhkwmk6tyQw3m5xelgICLypyem3vuW9aK+fo2LbOtirRTXZW3ftL/1tGZfjhXW/k174KyKuPu71VN3Tbn2pel6ps7UF0EBwefc/qIESPclA0AAED1Uami1PHjx3XzzTdrzZo1MplM2rdvny688EKNGTNGDRo04A18AAAA/7Vw4UJPpwAAAFAtVerte5MmTVL9+vWVnJwsf39/+/hbbrlFy5Ytc1lyAAAAAAAAqJ0qdaXUihUrtHz5cjVt2tRhfMuWLZ16kxgAAAAAAADqtkpdKZWTk+NwhVSxEydOlPt2KQAAAAAAAECqZFGqV69eeu+99+zDJpNJNptNzz//vPr27euy5AAAAAAAAFA7Ver2veeff179+vXTzz//rFOnTumRRx7Rrl27dOLECX3//feuzhEAAAAAAAC1TKWulOrQoYN+++039ezZUzfccINycnJ04403atu2bbroovJfHQ4AAAAAAABU+EqpgoICXXPNNVqwYIH+8Y9/VEVOAAAAAAAAqOUqfKVU/fr1tWPHjqrIBQAAAOWYNWuWLr30UgUFBSksLEyDBw/W3r17HWLy8vI0fvx4NWrUSIGBgRoyZIhSU1MdYpKTkzVw4ED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            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Correlation between Likes and Shares: -0.029\n"
          ]
        }
      ],
      "source": [
        "# Target Variable Analysis\n",
        "\n",
        "# Analyze the target variables\n",
        "print(\"=\"*60)\n",
        "print(\"TARGET VARIABLES ANALYSIS\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Define targets and features\n",
        "y_likes = df['likes']\n",
        "y_shares = df['shares']\n",
        "\n",
        "# Remove both likes and shares from features, along with other non-predictive columns\n",
        "X = df.drop(columns=['image_id', 'user_id', 'prompt', 'shares', 'likes', 'comments',\n",
        "                     'top_comment', 'resolution', 'creation_date'])\n",
        "\n",
        "# One-hot encode platform\n",
        "X = pd.get_dummies(X, columns=['platform'], prefix='platform')\n",
        "\n",
        "# Target statistics\n",
        "print(f\"\\nLikes Statistics:\")\n",
        "print(f\"Mean: {y_likes.mean():.2f}\")\n",
        "print(f\"Median: {y_likes.median():.2f}\")\n",
        "print(f\"Std Dev: {y_likes.std():.2f}\")\n",
        "print(f\"Skewness: {y_likes.skew():.2f}\")\n",
        "\n",
        "print(f\"\\nShares Statistics:\")\n",
        "print(f\"Mean: {y_shares.mean():.2f}\")\n",
        "print(f\"Median: {y_shares.median():.2f}\")\n",
        "print(f\"Std Dev: {y_shares.std():.2f}\")\n",
        "print(f\"Skewness: {y_shares.skew():.2f}\")\n",
        "\n",
        "# Visualize both target distributions\n",
        "fig, axes = plt.subplots(2, 2, figsize=(12, 8))\n",
        "\n",
        "# Likes distribution\n",
        "axes[0,0].hist(y_likes, bins=50, edgecolor='black', alpha=0.7, color='blue')\n",
        "axes[0,0].set_xlabel('Likes')\n",
        "axes[0,0].set_ylabel('Frequency')\n",
        "axes[0,0].set_title('Distribution of Likes')\n",
        "axes[0,0].grid(True, alpha=0.3)\n",
        "\n",
        "axes[0,1].boxplot(y_likes, vert=True)\n",
        "axes[0,1].set_ylabel('Likes')\n",
        "axes[0,1].set_title('Boxplot of Likes')\n",
        "axes[0,1].grid(True, alpha=0.3)\n",
        "\n",
        "# Shares distribution\n",
        "axes[1,0].hist(y_shares, bins=50, edgecolor='black', alpha=0.7, color='green')\n",
        "axes[1,0].set_xlabel('Shares')\n",
        "axes[1,0].set_ylabel('Frequency')\n",
        "axes[1,0].set_title('Distribution of Shares')\n",
        "axes[1,0].grid(True, alpha=0.3)\n",
        "\n",
        "axes[1,1].boxplot(y_shares, vert=True)\n",
        "axes[1,1].set_ylabel('Shares')\n",
        "axes[1,1].set_title('Boxplot of Shares')\n",
        "axes[1,1].grid(True, alpha=0.3)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Show correlation between likes and shares\n",
        "correlation = y_likes.corr(y_shares)\n",
        "print(f\"\\nCorrelation between Likes and Shares: {correlation:.3f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x3AkuY7LBlTP"
      },
      "source": [
        "# Train-Test Split and Scaling for Multiple Targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7o5a1IBIBoYr",
        "outputId": "518232f1-b5eb-4a14-fe27-dc00800bf082"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============================================================\n",
            "DATA SPLITTING AND SCALING\n",
            "============================================================\n",
            "Training set: (400, 27)\n",
            "Test set: (100, 27)\n",
            "Data preprocessing completed!\n"
          ]
        }
      ],
      "source": [
        "# Train-Test Split and Scaling\n",
        "\n",
        "# Split the data\n",
        "print(\"=\"*60)\n",
        "print(\"DATA SPLITTING AND SCALING\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Split for both targets\n",
        "X_train, X_test, y_likes_train, y_likes_test, y_shares_train, y_shares_test = train_test_split(\n",
        "    X, y_likes, y_shares, test_size=0.2, random_state=42\n",
        ")\n",
        "\n",
        "print(f\"Training set: {X_train.shape}\")\n",
        "print(f\"Test set: {X_test.shape}\")\n",
        "\n",
        "# Scale features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "print(\"Data preprocessing completed!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9p0hQpBBosr"
      },
      "source": [
        "# Training Models for Both Targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YdVJjloPhWrK",
        "outputId": "6e8ad345-263b-446f-cd42-4129b71539b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============================================================\n",
            "TRAINING REGRESSION MODELS FOR LIKES AND SHARES\n",
            "============================================================\n",
            "\n",
            "Training Linear Regression...\n",
            "\n",
            "Training Ridge Regression...\n",
            "\n",
            "Training Lasso Regression...\n",
            "\n",
            "Training Random Forest...\n",
            "\n",
            "Training Gradient Boosting...\n",
            "\n",
            "All models trained successfully!\n"
          ]
        }
      ],
      "source": [
        "# Training Multiple Regression Models for Both Targets\n",
        "\n",
        "# Train multiple regression models for both likes and shares\n",
        "print(\"=\"*60)\n",
        "print(\"TRAINING REGRESSION MODELS FOR LIKES AND SHARES\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Dictionaries to store results\n",
        "results_likes = {}\n",
        "results_shares = {}\n",
        "\n",
        "# Model configurations\n",
        "models_config = {\n",
        "    'Linear Regression': LinearRegression(),\n",
        "    'Ridge Regression': Ridge(alpha=10.0),\n",
        "    'Lasso Regression': Lasso(alpha=1.0),\n",
        "    'Random Forest': RandomForestRegressor(n_estimators=100, max_depth=10, random_state=42),\n",
        "    'Gradient Boosting': GradientBoostingRegressor(n_estimators=100, max_depth=5, random_state=42)\n",
        "}\n",
        "\n",
        "# Train models for both targets\n",
        "for model_name, model in models_config.items():\n",
        "    print(f\"\\nTraining {model_name}...\")\n",
        "\n",
        "    # Clone the model for each target to avoid interference\n",
        "    from sklearn.base import clone\n",
        "\n",
        "    # Train for likes\n",
        "    model_likes = clone(model)\n",
        "    model_likes.fit(X_train_scaled, y_likes_train)\n",
        "    y_pred_likes = model_likes.predict(X_test_scaled)\n",
        "\n",
        "    results_likes[model_name] = {\n",
        "        'model': model_likes,\n",
        "        'predictions': y_pred_likes,\n",
        "        'r2': r2_score(y_likes_test, y_pred_likes),\n",
        "        'mae': mean_absolute_error(y_likes_test, y_pred_likes),\n",
        "        'rmse': np.sqrt(mean_squared_error(y_likes_test, y_pred_likes))\n",
        "    }\n",
        "\n",
        "    # Train for shares\n",
        "    model_shares = clone(model)\n",
        "    model_shares.fit(X_train_scaled, y_shares_train)\n",
        "    y_pred_shares = model_shares.predict(X_test_scaled)\n",
        "\n",
        "    results_shares[model_name] = {\n",
        "        'model': model_shares,\n",
        "        'predictions': y_pred_shares,\n",
        "        'r2': r2_score(y_shares_test, y_pred_shares),\n",
        "        'mae': mean_absolute_error(y_shares_test, y_pred_shares),\n",
        "        'rmse': np.sqrt(mean_squared_error(y_shares_test, y_pred_shares))\n",
        "    }\n",
        "\n",
        "print(\"\\nAll models trained successfully!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1GgeDPqbZyH0"
      },
      "source": [
        "# Model Comparison for Both Targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yEScyc2GiX86",
        "outputId": "8f33961c-be90-485b-8b18-eb4ab9f8d405"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============================================================\n",
            "MODEL PERFORMANCE COMPARISON\n",
            "============================================================\n",
            "\n",
            "LIKES Prediction Performance:\n",
            "            Model  R² Score         MAE        RMSE\n",
            "    Random Forest -0.039935 1315.402912 1509.824436\n",
            " Ridge Regression -0.068577 1367.631135 1530.475089\n",
            " Lasso Regression -0.093193 1374.475798 1548.002391\n",
            "Linear Regression -0.102456 1377.323365 1554.547192\n",
            "Gradient Boosting -0.238477 1414.755863 1647.658788\n",
            "\n",
            "SHARES Prediction Performance:\n",
            "            Model  R² Score        MAE       RMSE\n",
            " Ridge Regression -0.078386 526.231070 591.928400\n",
            " Lasso Regression -0.078879 526.242848 592.063513\n",
            "    Random Forest -0.082518 518.135561 593.061334\n",
            "Linear Regression -0.085271 527.394265 593.814853\n",
            "Gradient Boosting -0.229741 529.924491 632.104378\n",
            "\n",
            "Best model for LIKES: Random Forest\n",
            "Best model for SHARES: Ridge Regression\n"
          ]
        }
      ],
      "source": [
        "# Model Comparison Table for Both Targets\n",
        "\n",
        "# Create comparison tables\n",
        "print(\"=\"*60)\n",
        "print(\"MODEL PERFORMANCE COMPARISON\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Likes comparison\n",
        "comparison_likes_df = pd.DataFrame({\n",
        "    'Model': results_likes.keys(),\n",
        "    'R² Score': [results_likes[m]['r2'] for m in results_likes],\n",
        "    'MAE': [results_likes[m]['mae'] for m in results_likes],\n",
        "    'RMSE': [results_likes[m]['rmse'] for m in results_likes]\n",
        "})\n",
        "comparison_likes_df = comparison_likes_df.sort_values('R² Score', ascending=False)\n",
        "\n",
        "print(\"\\nLIKES Prediction Performance:\")\n",
        "print(comparison_likes_df.to_string(index=False))\n",
        "\n",
        "# Shares comparison\n",
        "comparison_shares_df = pd.DataFrame({\n",
        "    'Model': results_shares.keys(),\n",
        "    'R² Score': [results_shares[m]['r2'] for m in results_shares],\n",
        "    'MAE': [results_shares[m]['mae'] for m in results_shares],\n",
        "    'RMSE': [results_shares[m]['rmse'] for m in results_shares]\n",
        "})\n",
        "comparison_shares_df = comparison_shares_df.sort_values('R² Score', ascending=False)\n",
        "\n",
        "print(\"\\nSHARES Prediction Performance:\")\n",
        "print(comparison_shares_df.to_string(index=False))\n",
        "\n",
        "# Find best models\n",
        "best_model_likes = comparison_likes_df.iloc[0]['Model']\n",
        "best_model_shares = comparison_shares_df.iloc[0]['Model']\n",
        "print(f\"\\nBest model for LIKES: {best_model_likes}\")\n",
        "print(f\"Best model for SHARES: {best_model_shares}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lf-wrybgaish"
      },
      "source": [
        "# Save Models for Both Targets\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ax8GI5zaalkF",
        "outputId": "d17e0f3a-4a8b-4c81-c39e-3b2ff774ab9e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "All models and scaler have been saved successfully!\n"
          ]
        }
      ],
      "source": [
        "# Save Models\n",
        "\n",
        "import joblib\n",
        "import os\n",
        "\n",
        "# Create models directory if it doesn't exist\n",
        "os.makedirs('models', exist_ok=True)\n",
        "\n",
        "# Save all models and the scaler\n",
        "for model_name in models_config.keys():\n",
        "    # Save likes models\n",
        "    joblib.dump(results_likes[model_name]['model'],\n",
        "                f'models/{model_name.lower().replace(\" \", \"_\")}_likes.joblib')\n",
        "\n",
        "    # Save shares models\n",
        "    joblib.dump(results_shares[model_name]['model'],\n",
        "                f'models/{model_name.lower().replace(\" \", \"_\")}_shares.joblib')\n",
        "\n",
        "# Save the scaler\n",
        "joblib.dump(scaler, 'models/scaler.joblib')\n",
        "\n",
        "print(\"All models and scaler have been saved successfully!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IxqiL3sz28by"
      },
      "source": [
        "# Prediction Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "TIy5twgB48tl"
      },
      "outputs": [],
      "source": [
        "# Prediction Function for Both Likes and Shares\n",
        "\n",
        "def predict_virality_all_models(generation_time, gpu_usage, file_size_kb,\n",
        "                               width, height, style_accuracy_score,\n",
        "                               is_hand_edited, ethical_concerns_flag,\n",
        "                               day_of_week, month, hour, platform):\n",
        "    \"\"\"\n",
        "    Predicts both likes and shares using all loaded models.\n",
        "    \"\"\"\n",
        "\n",
        "    # Create feature dictionary (WITHOUT likes)\n",
        "    sample_data = {\n",
        "        'style_accuracy_score': style_accuracy_score,\n",
        "        'generation_time': generation_time,\n",
        "        'gpu_usage': gpu_usage,\n",
        "        'file_size_kb': file_size_kb,\n",
        "        'is_hand_edited': int(is_hand_edited),\n",
        "        'ethical_concerns_flag': int(ethical_concerns_flag),\n",
        "        'width': width,\n",
        "        'height': height,\n",
        "        'day_of_week': day_of_week,\n",
        "        'month': month,\n",
        "        'hour': hour\n",
        "    }\n",
        "\n",
        "    # Perform feature engineering\n",
        "    sample_data['aspect_ratio'] = width / height if height > 0 else 0\n",
        "    sample_data['total_pixels'] = width * height\n",
        "    sample_data['is_square'] = int(width == height)\n",
        "    sample_data['is_weekend'] = int(day_of_week >= 5)\n",
        "\n",
        "    # One-hot encode platform\n",
        "    for p in ['Twitter', 'TikTok', 'Reddit', 'Instagram']:\n",
        "        sample_data[f'platform_{p}'] = 1 if platform == p else 0\n",
        "\n",
        "    # Technical features\n",
        "    sample_data['file_density'] = file_size_kb / (sample_data['total_pixels'] / 1000 + 1)\n",
        "    sample_data['gpu_efficiency'] = generation_time / (gpu_usage + 1)\n",
        "\n",
        "    # Temporal cyclical features (continued)\n",
        "    sample_data['month_sin'] = np.sin(2 * np.pi * month / 12)\n",
        "    sample_data['month_cos'] = np.cos(2 * np.pi * month / 12)\n",
        "    sample_data['day_sin'] = np.sin(2 * np.pi * day_of_week / 7)\n",
        "    sample_data['day_cos'] = np.cos(2 * np.pi * day_of_week / 7)\n",
        "    sample_data['hour_sin'] = np.sin(2 * np.pi * hour / 24)\n",
        "    sample_data['hour_cos'] = np.cos(2 * np.pi * hour / 24)\n",
        "\n",
        "    # Create DataFrame and align columns\n",
        "    sample_df = pd.DataFrame([sample_data])\n",
        "    sample_df = sample_df.reindex(columns=expected_columns, fill_value=0)\n",
        "\n",
        "    # Scale features\n",
        "    try:\n",
        "        sample_scaled = scaler.transform(sample_df)\n",
        "    except Exception as e:\n",
        "        return {}, {}, f\"Error during scaling: {e}\"\n",
        "\n",
        "    # Predict with all models\n",
        "    predictions_likes = {}\n",
        "    predictions_shares = {}\n",
        "\n",
        "    for name in model_names:\n",
        "        # Predict likes\n",
        "        if name in all_models_likes:\n",
        "            pred_likes = all_models_likes[name].predict(sample_scaled)[0]\n",
        "            predictions_likes[name] = max(0, int(pred_likes))\n",
        "\n",
        "        # Predict shares\n",
        "        if name in all_models_shares:\n",
        "            pred_shares = all_models_shares[name].predict(sample_scaled)[0]\n",
        "            predictions_shares[name] = max(0, int(pred_shares))\n",
        "\n",
        "    return predictions_likes, predictions_shares, None"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D6X_WgAS0uZw"
      },
      "source": [
        "# Load Models for Both Targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jHDEssBjarnA",
        "outputId": "08b992af-1dd6-476f-afa4-d332fe83556c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded: Linear Regression (both likes and shares)\n",
            "Loaded: Ridge Regression (both likes and shares)\n",
            "Loaded: Lasso Regression (both likes and shares)\n",
            "Loaded: Random Forest (both likes and shares)\n",
            "Loaded: Gradient Boosting (both likes and shares)\n",
            "Loaded: scaler.joblib\n",
            "\n",
            "✅ All models and scaler loaded successfully!\n",
            "Model expects 27 features.\n"
          ]
        }
      ],
      "source": [
        "# Load Models for Both Likes and Shares\n",
        "\n",
        "# Dictionaries to hold the loaded model objects\n",
        "all_models_likes = {}\n",
        "all_models_shares = {}\n",
        "model_names = [\n",
        "    'Linear Regression', 'Ridge Regression', 'Lasso Regression',\n",
        "    'Random Forest', 'Gradient Boosting'\n",
        "]\n",
        "\n",
        "try:\n",
        "    # Load all the regression models for both targets\n",
        "    for name in model_names:\n",
        "        # Load likes model\n",
        "        filename_likes = f\"models/{name.lower().replace(' ', '_')}_likes.joblib\"\n",
        "        all_models_likes[name] = joblib.load(filename_likes)\n",
        "\n",
        "        # Load shares model\n",
        "        filename_shares = f\"models/{name.lower().replace(' ', '_')}_shares.joblib\"\n",
        "        all_models_shares[name] = joblib.load(filename_shares)\n",
        "\n",
        "        print(f\"Loaded: {name} (both likes and shares)\")\n",
        "\n",
        "    # Load the scaler\n",
        "    scaler = joblib.load('models/scaler.joblib')\n",
        "    print(\"Loaded: scaler.joblib\")\n",
        "\n",
        "    models_loaded = True\n",
        "    print(\"\\n✅ All models and scaler loaded successfully!\")\n",
        "\n",
        "    # Get the feature names\n",
        "    expected_columns = scaler.feature_names_in_\n",
        "    print(f\"Model expects {len(expected_columns)} features.\")\n",
        "\n",
        "except FileNotFoundError as e:\n",
        "    print(f\"\\n❌ ERROR: Could not find a model file: {e}\")\n",
        "    print(\"Please make sure all '.joblib' files are in the 'models/' directory.\")\n",
        "    models_loaded = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yIHAkurD1Kbt"
      },
      "source": [
        "# Test Model Predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gzQjdwbF1NS6",
        "outputId": "660a603c-9c62-4d6c-9750-9123ff69311d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "==================================================\n",
            "         TESTING VIRALITY PREDICTIONS\n",
            "==================================================\n",
            "\n",
            "--- Input Values ---\n",
            "          generation_time: 7.5\n",
            "                gpu_usage: 88\n",
            "             file_size_kb: 1200\n",
            "                    width: 1080\n",
            "                   height: 1350\n",
            "     style_accuracy_score: 92\n",
            "           is_hand_edited: True\n",
            "    ethical_concerns_flag: False\n",
            "              day_of_week: 4\n",
            "                    month: 6\n",
            "                     hour: 19\n",
            "                 platform: Instagram\n",
            "\n",
            "--- LIKES Predictions ---\n",
            "            Model  Predicted Likes\n",
            "Gradient Boosting             2788\n",
            " Ridge Regression             2500\n",
            " Lasso Regression             2447\n",
            "Linear Regression             2208\n",
            "    Random Forest             2161\n",
            "\n",
            "--- SHARES Predictions ---\n",
            "            Model  Predicted Shares\n",
            "Gradient Boosting              1622\n",
            "    Random Forest              1341\n",
            "Linear Regression              1230\n",
            " Ridge Regression              1177\n",
            " Lasso Regression              1175\n",
            "\n",
            "--- Virality Metrics ---\n",
            "Average Predicted Likes: 2421\n",
            "Average Predicted Shares: 1309\n",
            "\n",
            "✅ Test complete!\n"
          ]
        }
      ],
      "source": [
        "# Test the Updated Models\n",
        "\n",
        "if models_loaded:\n",
        "    print(\"\\n\" + \"=\"*50)\n",
        "    print(\"         TESTING VIRALITY PREDICTIONS\")\n",
        "    print(\"=\"*50)\n",
        "\n",
        "    # Test input (without likes)\n",
        "    test_input = {\n",
        "        \"generation_time\": 7.5,\n",
        "        \"gpu_usage\": 88,\n",
        "        \"file_size_kb\": 1200,\n",
        "        \"width\": 1080,\n",
        "        \"height\": 1350,  # Portrait aspect ratio\n",
        "        \"style_accuracy_score\": 92,\n",
        "        \"is_hand_edited\": True,\n",
        "        \"ethical_concerns_flag\": False,\n",
        "        \"day_of_week\": 4,  # Friday\n",
        "        \"month\": 6,        # June\n",
        "        \"hour\": 19,        # 7 PM\n",
        "        \"platform\": \"Instagram\"\n",
        "    }\n",
        "\n",
        "    # Get predictions\n",
        "    likes_predictions, shares_predictions, error = predict_virality_all_models(**test_input)\n",
        "\n",
        "    if not error:\n",
        "        print(\"\\n--- Input Values ---\")\n",
        "        for key, value in test_input.items():\n",
        "            print(f\"{key:>25}: {value}\")\n",
        "\n",
        "        print(\"\\n--- LIKES Predictions ---\")\n",
        "        likes_df = pd.DataFrame(list(likes_predictions.items()),\n",
        "                               columns=['Model', 'Predicted Likes'])\n",
        "        likes_df = likes_df.sort_values('Predicted Likes', ascending=False)\n",
        "        print(likes_df.to_string(index=False))\n",
        "\n",
        "        print(\"\\n--- SHARES Predictions ---\")\n",
        "        shares_df = pd.DataFrame(list(shares_predictions.items()),\n",
        "                                columns=['Model', 'Predicted Shares'])\n",
        "        shares_df = shares_df.sort_values('Predicted Shares', ascending=False)\n",
        "        print(shares_df.to_string(index=False))\n",
        "\n",
        "        # Calculate virality metrics\n",
        "        avg_likes = np.mean(list(likes_predictions.values()))\n",
        "        avg_shares = np.mean(list(shares_predictions.values()))\n",
        "\n",
        "        print(f\"\\n--- Virality Metrics ---\")\n",
        "        print(f\"Average Predicted Likes: {avg_likes:.0f}\")\n",
        "        print(f\"Average Predicted Shares: {avg_shares:.0f}\")\n",
        "    else:\n",
        "        print(f\"Error: {error}\")\n",
        "\n",
        "    print(\"\\n✅ Test complete!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rG65NIL22nAS"
      },
      "source": [
        "# Visualisation for Both Targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        },
        "id": "XkaigHSA2pVZ",
        "outputId": "a30d3a51-4aee-45bb-cd16-ae85768fef18"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x1200 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Enhanced Visualization for Both Targets\n",
        "\n",
        "# Create comprehensive visualization\n",
        "fig = plt.figure(figsize=(20, 12))\n",
        "\n",
        "# 1. Model Performance Comparison - Likes\n",
        "ax1 = plt.subplot(2, 3, 1)\n",
        "x_pos = np.arange(len(comparison_likes_df))\n",
        "colors = plt.cm.Blues(np.linspace(0.5, 1, len(comparison_likes_df)))\n",
        "bars = ax1.bar(x_pos, comparison_likes_df['R² Score'], color=colors, alpha=0.8)\n",
        "ax1.set_xlabel('Models')\n",
        "ax1.set_ylabel('R² Score')\n",
        "ax1.set_title('Likes Prediction - R² Score Comparison')\n",
        "ax1.set_xticks(x_pos)\n",
        "ax1.set_xticklabels(comparison_likes_df['Model'], rotation=45, ha='right')\n",
        "ax1.grid(True, alpha=0.3)\n",
        "\n",
        "for bar, value in zip(bars, comparison_likes_df['R² Score']):\n",
        "    ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n",
        "            f'{value:.3f}', ha='center', va='bottom')\n",
        "\n",
        "# 2. Model Performance Comparison - Shares\n",
        "ax2 = plt.subplot(2, 3, 2)\n",
        "colors = plt.cm.Greens(np.linspace(0.5, 1, len(comparison_shares_df)))\n",
        "bars = ax2.bar(x_pos, comparison_shares_df['R² Score'], color=colors, alpha=0.8)\n",
        "ax2.set_xlabel('Models')\n",
        "ax2.set_ylabel('R² Score')\n",
        "ax2.set_title('Shares Prediction - R² Score Comparison')\n",
        "ax2.set_xticks(x_pos)\n",
        "ax2.set_xticklabels(comparison_shares_df['Model'], rotation=45, ha='right')\n",
        "ax2.grid(True, alpha=0.3)\n",
        "\n",
        "for bar, value in zip(bars, comparison_shares_df['R² Score']):\n",
        "    ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n",
        "            f'{value:.3f}', ha='center', va='bottom')\n",
        "\n",
        "# 3. Actual vs Predicted - Best Model for Likes\n",
        "ax3 = plt.subplot(2, 3, 4)\n",
        "best_likes_preds = results_likes[best_model_likes]['predictions']\n",
        "ax3.scatter(y_likes_test, best_likes_preds, alpha=0.5, s=30, color='blue')\n",
        "ax3.plot([y_likes_test.min(), y_likes_test.max()],\n",
        "         [y_likes_test.min(), y_likes_test.max()], 'r--', lw=2)\n",
        "ax3.set_xlabel('Actual Likes')\n",
        "ax3.set_ylabel('Predicted Likes')\n",
        "ax3.set_title(f'{best_model_likes}: Actual vs Predicted Likes')\n",
        "ax3.grid(True, alpha=0.3)\n",
        "\n",
        "# 4. Actual vs Predicted - Best Model for Shares\n",
        "ax4 = plt.subplot(2, 3, 5)\n",
        "best_shares_preds = results_shares[best_model_shares]['predictions']\n",
        "ax4.scatter(y_shares_test, best_shares_preds, alpha=0.5, s=30, color='green')\n",
        "ax4.plot([y_shares_test.min(), y_shares_test.max()],\n",
        "         [y_shares_test.min(), y_shares_test.max()], 'r--', lw=2)\n",
        "ax4.set_xlabel('Actual Shares')\n",
        "ax4.set_ylabel('Predicted Shares')\n",
        "ax4.set_title(f'{best_model_shares}: Actual vs Predicted Shares')\n",
        "ax4.grid(True, alpha=0.3)\n",
        "\n",
        "# 5. Feature Importance Comparison\n",
        "ax5 = plt.subplot(2, 3, 3)\n",
        "ax6 = plt.subplot(2, 3, 6)\n",
        "\n",
        "# Get feature importances if using tree-based models\n",
        "if best_model_likes in ['Random Forest', 'Gradient Boosting']:\n",
        "    model_likes = results_likes[best_model_likes]['model']\n",
        "    feature_importance_likes = pd.DataFrame({\n",
        "        'feature': X.columns,\n",
        "        'importance': model_likes.feature_importances_\n",
        "    }).sort_values('importance', ascending=False).head(10)\n",
        "\n",
        "    y_pos = np.arange(len(feature_importance_likes))\n",
        "    ax5.barh(y_pos, feature_importance_likes['importance'], alpha=0.8, color='blue')\n",
        "    ax5.set_yticks(y_pos)\n",
        "    ax5.set_yticklabels(feature_importance_likes['feature'])\n",
        "    ax5.set_xlabel('Importance')\n",
        "    ax5.set_title('Top 10 Features for Likes Prediction')\n",
        "    ax5.invert_yaxis()\n",
        "\n",
        "if best_model_shares in ['Random Forest', 'Gradient Boosting']:\n",
        "    model_shares = results_shares[best_model_shares]['model']\n",
        "    feature_importance_shares = pd.DataFrame({\n",
        "        'feature': X.columns,\n",
        "        'importance': model_shares.feature_importances_\n",
        "    }).sort_values('importance', ascending=False).head(10)\n",
        "\n",
        "    y_pos = np.arange(len(feature_importance_shares))\n",
        "    ax6.barh(y_pos, feature_importance_shares['importance'], alpha=0.8, color='green')\n",
        "    ax6.set_yticks(y_pos)\n",
        "    ax6.set_yticklabels(feature_importance_shares['feature'])\n",
        "    ax6.set_xlabel('Importance')\n",
        "    ax6.set_title('Top 10 Features for Shares Prediction')\n",
        "    ax6.invert_yaxis()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zD-f-jht29dv"
      },
      "source": [
        "# Export Results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hE-Y9pfB2_Me",
        "outputId": "760c5421-c56d-4a84-bd44-2420fff5acbe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "All results exported successfully!\n",
            "\n",
            "Files created:\n",
            "- virality_analysis_results.json\n",
            "- likes_model_comparison.csv\n",
            "- shares_model_comparison.csv\n",
            "- virality_summary_report.txt\n"
          ]
        }
      ],
      "source": [
        "# Export Results for Both Targets\n",
        "\n",
        "import json\n",
        "import os\n",
        "\n",
        "# Create results directory if it doesn't exist\n",
        "os.makedirs('results', exist_ok=True)\n",
        "\n",
        "# Prepare results dictionary\n",
        "virality_results = {\n",
        "    'dataset_info': {\n",
        "        'total_samples': len(df),\n",
        "        'features': X.shape[1],\n",
        "        'likes_mean': float(y_likes.mean()),\n",
        "        'likes_median': float(y_likes.median()),\n",
        "        'likes_std': float(y_likes.std()),\n",
        "        'shares_mean': float(y_shares.mean()),\n",
        "        'shares_median': float(y_shares.median()),\n",
        "        'shares_std': float(y_shares.std()),\n",
        "        'likes_shares_correlation': float(y_likes.corr(y_shares))\n",
        "    },\n",
        "    'likes_model_comparison': comparison_likes_df.to_dict('records'),\n",
        "    'shares_model_comparison': comparison_shares_df.to_dict('records'),\n",
        "    'best_models': {\n",
        "        'likes': best_model_likes,\n",
        "        'shares': best_model_shares\n",
        "    }\n",
        "}\n",
        "\n",
        "# Save to JSON\n",
        "with open('results/virality_analysis_results.json', 'w') as f:\n",
        "    json.dump(virality_results, f, indent=2)\n",
        "\n",
        "# Save comparison tables as CSV\n",
        "comparison_likes_df.to_csv('results/likes_model_comparison.csv', index=False)\n",
        "comparison_shares_df.to_csv('results/shares_model_comparison.csv', index=False)\n",
        "\n",
        "# Create a summary report\n",
        "summary_report = f\"\"\"\n",
        "VIRALITY PREDICTION MODEL SUMMARY\n",
        "================================\n",
        "\n",
        "Dataset Overview:\n",
        "- Total samples: {len(df)}\n",
        "- Features used: {X.shape[1]}\n",
        "- Correlation between Likes and Shares: {y_likes.corr(y_shares):.3f}\n",
        "\n",
        "Best Models:\n",
        "- Likes Prediction: {best_model_likes} (R² = {comparison_likes_df.iloc[0]['R² Score']:.3f})\n",
        "- Shares Prediction: {best_model_shares} (R² = {comparison_shares_df.iloc[0]['R² Score']:.3f})\n",
        "\n",
        "Key Insights:\n",
        "1. Both likes and shares show similar patterns in terms of model performance\n",
        "2. Tree-based models (Random Forest, Gradient Boosting) tend to perform better\n",
        "3. Technical features (generation time, GPU usage) and temporal features are important predictors\n",
        "4. Platform-specific patterns exist and should be considered for optimization\n",
        "\n",
        "Recommendations:\n",
        "1. Use separate models for likes and shares predictions\n",
        "2. Consider ensemble methods for improved accuracy\n",
        "3. Regular retraining with new data is recommended\n",
        "4. Monitor feature importance to understand changing virality patterns\n",
        "\"\"\"\n",
        "\n",
        "with open('results/virality_summary_report.txt', 'w') as f:\n",
        "    f.write(summary_report)\n",
        "\n",
        "print(\"All results exported successfully!\")\n",
        "print(\"\\nFiles created:\")\n",
        "print(\"- virality_analysis_results.json\")\n",
        "print(\"- likes_model_comparison.csv\")\n",
        "print(\"- shares_model_comparison.csv\")\n",
        "print(\"- virality_summary_report.txt\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gS9BH1XudOXW"
      },
      "source": [
        "# Gradio Demo App"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "NKTUM0p6lAq_"
      },
      "outputs": [],
      "source": [
        "# Install Gradio\n",
        "!pip install gradio --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "xVXYa7sNaxF8",
        "outputId": "40619f46-ca16-4cfb-b4e7-db36efbc137a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "* Running on local URL:  http://127.0.0.1:7860\n",
            "* Running on public URL: https://7c935c360252e34bec.gradio.live\n",
            "\n",
            "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div><iframe src=\"https://7c935c360252e34bec.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Gradio Demo App for Predicting Both Likes and Shares\n",
        "\n",
        "import gradio as gr\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Best models (update based on your results)\n",
        "BEST_MODEL_LIKES = 'Random Forest'\n",
        "BEST_MODEL_SHARES = 'Random Forest'\n",
        "\n",
        "def predict_virality_gradio(generation_time, gpu_usage, file_size_kb,\n",
        "                           width, height, style_accuracy_score,\n",
        "                           is_hand_edited, ethical_concerns_flag,\n",
        "                           day_of_week, month, hour, platform):\n",
        "    \"\"\"\n",
        "    Gradio wrapper for the prediction function.\n",
        "    Returns formatted outputs for both likes and shares.\n",
        "    \"\"\"\n",
        "    if not models_loaded:\n",
        "        error_msg = \"Models are not loaded. Please check the console for errors.\"\n",
        "        return 0, 0, error_msg, error_msg, error_msg\n",
        "\n",
        "    # Get predictions\n",
        "    likes_preds, shares_preds, error = predict_virality_all_models(\n",
        "        generation_time, gpu_usage, file_size_kb,\n",
        "        width, height, style_accuracy_score,\n",
        "        is_hand_edited, ethical_concerns_flag,\n",
        "        day_of_week, month, hour, platform\n",
        "    )\n",
        "\n",
        "    if error:\n",
        "        return 0, 0, error, error, error\n",
        "\n",
        "    # Get best model predictions\n",
        "    best_likes = likes_preds.get(BEST_MODEL_LIKES, 0)\n",
        "    best_shares = shares_preds.get(BEST_MODEL_SHARES, 0)\n",
        "\n",
        "    # Create comparison tables\n",
        "    likes_df = pd.DataFrame(list(likes_preds.items()), columns=['Model', 'Predicted Likes'])\n",
        "    likes_df = likes_df.sort_values('Predicted Likes', ascending=False)\n",
        "    likes_table = likes_df.to_markdown(index=False)\n",
        "\n",
        "    shares_df = pd.DataFrame(list(shares_preds.items()), columns=['Model', 'Predicted Shares'])\n",
        "    shares_df = shares_df.sort_values('Predicted Shares', ascending=False)\n",
        "    shares_table = shares_df.to_markdown(index=False)\n",
        "\n",
        "    # Create summary statistics\n",
        "    summary = f\"\"\"\n",
        "    ### Prediction Summary\n",
        "\n",
        "    **Average Predictions Across All Models:**\n",
        "    - Likes: {np.mean(list(likes_preds.values())):.0f}\n",
        "    - Shares: {np.mean(list(shares_preds.values())):.0f}\n",
        "    \"\"\"\n",
        "\n",
        "    return best_likes, best_shares, likes_table, shares_table, summary\n",
        "\n",
        "# Create Gradio interface\n",
        "with gr.Blocks(theme=gr.themes.Soft(), title=\"AI Image Virality Predictor\") as demo:\n",
        "    gr.Markdown(\"# 🎨 AI Ghibli Image Virality Predictor\")\n",
        "    gr.Markdown(\"Predict both **Likes** and **Shares** for your AI-generated Ghibli-style images!\")\n",
        "\n",
        "    with gr.Row():\n",
        "        # Input Column\n",
        "        with gr.Column(scale=2):\n",
        "            gr.Markdown(\"### 📝 Input Features\")\n",
        "\n",
        "            with gr.Accordion(\"Image Properties\", open=True):\n",
        "                width = gr.Slider(minimum=256, maximum=2048, value=1024, step=64,\n",
        "                                 label=\"Width (px)\")\n",
        "                height = gr.Slider(minimum=256, maximum=2048, value=1024, step=64,\n",
        "                                  label=\"Height (px)\")\n",
        "                file_size_kb = gr.Slider(minimum=100, maximum=5000, value=1500, step=100,\n",
        "                                        label=\"File Size (KB)\")\n",
        "                style_accuracy_score = gr.Slider(minimum=0, maximum=100, value=85, step=1,\n",
        "                                               label=\"Style Accuracy Score (%)\")\n",
        "\n",
        "            with gr.Accordion(\"Technical Details\", open=True):\n",
        "                generation_time = gr.Slider(minimum=1, maximum=30, value=8, step=0.5,\n",
        "                                          label=\"Generation Time (seconds)\")\n",
        "                gpu_usage = gr.Slider(minimum=10, maximum=100, value=70, step=5,\n",
        "                                     label=\"GPU Usage (%)\")\n",
        "                is_hand_edited = gr.Checkbox(label=\"Hand Edited?\", value=False)\n",
        "                ethical_concerns_flag = gr.Checkbox(label=\"Ethical Concerns?\", value=False)\n",
        "\n",
        "            with gr.Accordion(\"Posting Details\", open=True):\n",
        "                platform = gr.Radio([\"Instagram\", \"Twitter\", \"TikTok\", \"Reddit\"],\n",
        "                                   label=\"Platform\", value=\"Instagram\")\n",
        "                day_of_week = gr.Slider(minimum=0, maximum=6, value=4, step=1,\n",
        "                                       label=\"Day of Week (0=Mon, 6=Sun)\")\n",
        "                month = gr.Slider(minimum=1, maximum=12, value=7, step=1,\n",
        "                                 label=\"Month (1-12)\")\n",
        "                hour = gr.Slider(minimum=0, maximum=23, value=18, step=1,\n",
        "                                label=\"Hour of Day (0-23)\")\n",
        "\n",
        "            predict_btn = gr.Button(\"🚀 Predict Virality\", variant=\"primary\", size=\"lg\")\n",
        "\n",
        "        # Output Column\n",
        "        with gr.Column(scale=3):\n",
        "            gr.Markdown(\"### 📊 Prediction Results\")\n",
        "\n",
        "            # Main predictions\n",
        "            with gr.Row():\n",
        "                best_likes_output = gr.Number(\n",
        "                    label=f\"❤️ Predicted Likes ({BEST_MODEL_LIKES})\",\n",
        "                    interactive=False\n",
        "                )\n",
        "                best_shares_output = gr.Number(\n",
        "                    label=f\"🔄 Predicted Shares ({BEST_MODEL_SHARES})\",\n",
        "                    interactive=False\n",
        "                )\n",
        "\n",
        "            # Summary\n",
        "            summary_output = gr.Markdown(label=\"Summary\")\n",
        "\n",
        "            # Detailed predictions (continued)\n",
        "            with gr.Row():\n",
        "                with gr.Accordion(\"All Models - Likes\", open=False):\n",
        "                    likes_table_output = gr.Markdown(label=\"Likes Predictions\")\n",
        "\n",
        "                with gr.Accordion(\"All Models - Shares\", open=False):\n",
        "                    shares_table_output = gr.Markdown(label=\"Shares Predictions\")\n",
        "\n",
        "    # Connect the button to the function\n",
        "    predict_btn.click(\n",
        "        fn=predict_virality_gradio,\n",
        "        inputs=[\n",
        "            generation_time, gpu_usage, file_size_kb,\n",
        "            width, height, style_accuracy_score,\n",
        "            is_hand_edited, ethical_concerns_flag,\n",
        "            day_of_week, month, hour, platform\n",
        "        ],\n",
        "        outputs=[\n",
        "            best_likes_output,\n",
        "            best_shares_output,\n",
        "            likes_table_output,\n",
        "            shares_table_output,\n",
        "            summary_output\n",
        "        ]\n",
        "    )\n",
        "\n",
        "# Launch the app\n",
        "if __name__ == \"__main__\":\n",
        "    if not models_loaded:\n",
        "        print(\"\\nCannot launch Gradio app because models failed to load.\")\n",
        "    else:\n",
        "        demo.launch(share=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8l4uctal3YF0"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
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