twitter_data_initial_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_json('data/tweets_2024-03-07_22-02-24.json', lines=True)\n",
    "# Convert 'date' column to datetime with the correct format\n",
    "df['date'] = pd.to_datetime(df['date'], format='%d/%m/%Y')\n",
    "# Save CSV\n",
    "df.to_csv('data/dataset.csv', index=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2023-10-01 00:00:00'), Timestamp('2024-03-07 00:00:00'))"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date.min(), df.date.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>author_name</th>\n",
       "      <th>author_handle</th>\n",
       "      <th>date</th>\n",
       "      <th>lang</th>\n",
       "      <th>url</th>\n",
       "      <th>mentioned_urls</th>\n",
       "      <th>is_retweet</th>\n",
       "      <th>media_type</th>\n",
       "      <th>images_urls</th>\n",
       "      <th>num_reply</th>\n",
       "      <th>num_retweet</th>\n",
       "      <th>num_like</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>今天刷到Sun Yuzhen的视频，感慨里面提到的一个小小的过滤器—— 『在问我怎么做视频之前，你自己先发20个视频，哪怕一个只有5秒钟。』\\n然而这个极其简单的过滤器，绝大多数人都通不过。这可能也是『只筛选，不培养』这句话现在这么流行的原因。\\n\\nhttps://youtube.com/watch?v=kMqd6Tc4X_0…\\n\\n#typefully Day 3/30</td>\n",
       "      <td>Yucheng L - /acc</td>\n",
       "      <td>@lyc_zh</td>\n",
       "      <td>2024-03-04</td>\n",
       "      <td>zh</td>\n",
       "      <td>https://twitter.com/lyc_zh/status/1764483531013861709</td>\n",
       "      <td>[https://t.co/NyrQd7zftK]</td>\n",
       "      <td>False</td>\n",
       "      <td>No media</td>\n",
       "      <td>None</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td></td>\n",
       "      <td>celestialmaze</td>\n",
       "      <td>@cmzw_</td>\n",
       "      <td>2024-01-24</td>\n",
       "      <td></td>\n",
       "      <td>https://twitter.com/cmzw_/status/1750091321619124250</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>Video</td>\n",
       "      <td>None</td>\n",
       "      <td>19</td>\n",
       "      <td>487</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td></td>\n",
       "      <td>木澄 玲生</td>\n",
       "      <td>@kisumirei41</td>\n",
       "      <td>2023-11-25</td>\n",
       "      <td>qme</td>\n",
       "      <td>https://twitter.com/kisumirei41/status/1728415540514025825</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>Image</td>\n",
       "      <td>[https://pbs.twimg.com/media/F_yQu4wa4AArlo4?format=jpg&amp;name=medium]</td>\n",
       "      <td>2</td>\n",
       "      <td>339</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>#Frieren \\n#fern\\n#フリーレン\\n#フェルン</td>\n",
       "      <td>TKC</td>\n",
       "      <td>@colin_5683</td>\n",
       "      <td>2024-02-27</td>\n",
       "      <td>qme</td>\n",
       "      <td>https://twitter.com/colin_5683/status/1762320555708129589</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>Image</td>\n",
       "      <td>[https://pbs.twimg.com/media/GHUFMUqbYAENcDt?format=jpg&amp;name=medium]</td>\n",
       "      <td>20</td>\n",
       "      <td>2643</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>风口变了…..\\n12月14日凌晨，美联储议息会议结果出来了：\\n维持联邦基金目标利率在5.25%-5.50%区间，每月缩减600亿美元国债和350亿美元MBS。\\n与市场预期一致。\\n \\n可以说，关于美联储加息这事儿，自7月份议息会议结束以来，美联储基本上就是在一步步确认市场预期。…</td>\n",
       "      <td>财经数据库</td>\n",
       "      <td>@caijingshujuku</td>\n",
       "      <td>2023-12-20</td>\n",
       "      <td>zh</td>\n",
       "      <td>https://twitter.com/caijingshujuku/status/1737483779034284237</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>Image</td>\n",
       "      <td>[https://pbs.twimg.com/media/GBzISGUWwAAaeNG?format=jpg&amp;name=small, https://pbs.twimg.com/media/GBzISGMWUAA_0GY?format=jpg&amp;name=small, https://pbs.twimg.com/media/GBzISGHXoAAGlQz?format=jpg&amp;name=small, https://pbs.twimg.com/media/GBzISGKWkAAf6tK?format=jpg&amp;name=small]</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>试了猎豹前首席科学家闵可锐做的这个密塔AI搜索，相当可以啊，某些方面比Perplexity还要好。\\n\\n尤其是研究能力开启以后直接生成的内容比一些媒体写的报告好多了，非常全面逻辑性也很强。…</td>\n",
       "      <td>歸藏</td>\n",
       "      <td>@op7418</td>\n",
       "      <td>2024-02-18</td>\n",
       "      <td>zh</td>\n",
       "      <td>https://twitter.com/op7418/status/1759128155225473202</td>\n",
       "      <td>[]</td>\n",
       "      <td>False</td>\n",
       "      <td>Image</td>\n",
       "      <td>[https://pbs.twimg.com/media/GGmscMNagAAZPJh?format=jpg&amp;name=900x900, https://pbs.twimg.com/media/GGmssQnaoAAeDT9?format=jpg&amp;name=900x900]</td>\n",
       "      <td>28</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>Generative Powers of Ten\\n\\nabs: https://arxiv.org/abs/2312.02149\\nproject page: https://powers-of-10.github.io\\n\\n\"We present a method that uses a text-to-image model to generate consistent content across multiple image scales, enabling extreme semantic zooms into a scene\"</td>\n",
       "      <td>Tanishq Mathew Abraham, Ph.D.</td>\n",
       "      <td>@iScienceLuvr</td>\n",
       "      <td>2023-12-05</td>\n",
       "      <td>en</td>\n",
       "      <td>https://twitter.com/iScienceLuvr/status/1731875267889578425</td>\n",
       "      <td>[https://t.co/GLSH7IbK3a, https://t.co/DhLYj3lWLX]</td>\n",
       "      <td>False</td>\n",
       "      <td>Video</td>\n",
       "      <td>None</td>\n",
       "      <td>18</td>\n",
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       "      <td>0</td>\n",
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       "                                                                                                                                                                                                                                                                                   text  \\\n",
       "34                                                                                        今天刷到Sun Yuzhen的视频，感慨里面提到的一个小小的过滤器—— 『在问我怎么做视频之前，你自己先发20个视频，哪怕一个只有5秒钟。』\\n然而这个极其简单的过滤器，绝大多数人都通不过。这可能也是『只筛选，不培养』这句话现在这么流行的原因。\\n\\nhttps://youtube.com/watch?v=kMqd6Tc4X_0…\\n\\n#typefully Day 3/30   \n",
       "324                                                                                                                                                                                                                                                                                       \n",
       "982                                                                                                                                                                                                                                                                                       \n",
       "84                                                                                                                                                                                                                                                      #Frieren \\n#fern\\n#フリーレン\\n#フェルン   \n",
       "727                                                                                                                                    风口变了…..\\n12月14日凌晨，美联储议息会议结果出来了：\\n维持联邦基金目标利率在5.25%-5.50%区间，每月缩减600亿美元国债和350亿美元MBS。\\n与市场预期一致。\\n \\n可以说，关于美联储加息这事儿，自7月份议息会议结束以来，美联储基本上就是在一步步确认市场预期。…   \n",
       "155                                                                                                                                                                                    试了猎豹前首席科学家闵可锐做的这个密塔AI搜索，相当可以啊，某些方面比Perplexity还要好。\\n\\n尤其是研究能力开启以后直接生成的内容比一些媒体写的报告好多了，非常全面逻辑性也很强。…   \n",
       "869  Generative Powers of Ten\\n\\nabs: https://arxiv.org/abs/2312.02149\\nproject page: https://powers-of-10.github.io\\n\\n\"We present a method that uses a text-to-image model to generate consistent content across multiple image scales, enabling extreme semantic zooms into a scene\"   \n",
       "\n",
       "                       author_name    author_handle       date lang  \\\n",
       "34                Yucheng L - /acc          @lyc_zh 2024-03-04   zh   \n",
       "324                  celestialmaze           @cmzw_ 2024-01-24        \n",
       "982                          木澄 玲生     @kisumirei41 2023-11-25  qme   \n",
       "84                             TKC      @colin_5683 2024-02-27  qme   \n",
       "727                          财经数据库  @caijingshujuku 2023-12-20   zh   \n",
       "155                             歸藏          @op7418 2024-02-18   zh   \n",
       "869  Tanishq Mathew Abraham, Ph.D.    @iScienceLuvr 2023-12-05   en   \n",
       "\n",
       "                                                               url  \\\n",
       "34           https://twitter.com/lyc_zh/status/1764483531013861709   \n",
       "324           https://twitter.com/cmzw_/status/1750091321619124250   \n",
       "982     https://twitter.com/kisumirei41/status/1728415540514025825   \n",
       "84       https://twitter.com/colin_5683/status/1762320555708129589   \n",
       "727  https://twitter.com/caijingshujuku/status/1737483779034284237   \n",
       "155          https://twitter.com/op7418/status/1759128155225473202   \n",
       "869    https://twitter.com/iScienceLuvr/status/1731875267889578425   \n",
       "\n",
       "                                         mentioned_urls  is_retweet  \\\n",
       "34                            [https://t.co/NyrQd7zftK]       False   \n",
       "324                                                  []       False   \n",
       "982                                                  []       False   \n",
       "84                                                   []       False   \n",
       "727                                                  []       False   \n",
       "155                                                  []       False   \n",
       "869  [https://t.co/GLSH7IbK3a, https://t.co/DhLYj3lWLX]       False   \n",
       "\n",
       "    media_type  \\\n",
       "34    No media   \n",
       "324      Video   \n",
       "982      Image   \n",
       "84       Image   \n",
       "727      Image   \n",
       "155      Image   \n",
       "869      Video   \n",
       "\n",
       "                                                                                                                                                                                                                                                                      images_urls  \\\n",
       "34                                                                                                                                                                                                                                                                           None   \n",
       "324                                                                                                                                                                                                                                                                          None   \n",
       "982                                                                                                                                                                                                          [https://pbs.twimg.com/media/F_yQu4wa4AArlo4?format=jpg&name=medium]   \n",
       "84                                                                                                                                                                                                           [https://pbs.twimg.com/media/GHUFMUqbYAENcDt?format=jpg&name=medium]   \n",
       "727  [https://pbs.twimg.com/media/GBzISGUWwAAaeNG?format=jpg&name=small, https://pbs.twimg.com/media/GBzISGMWUAA_0GY?format=jpg&name=small, https://pbs.twimg.com/media/GBzISGHXoAAGlQz?format=jpg&name=small, https://pbs.twimg.com/media/GBzISGKWkAAf6tK?format=jpg&name=small]   \n",
       "155                                                                                                                                    [https://pbs.twimg.com/media/GGmscMNagAAZPJh?format=jpg&name=900x900, https://pbs.twimg.com/media/GGmssQnaoAAeDT9?format=jpg&name=900x900]   \n",
       "869                                                                                                                                                                                                                                                                          None   \n",
       "\n",
       "     num_reply  num_retweet  num_like  \n",
       "34           6            2         0  \n",
       "324         19          487         0  \n",
       "982          2          339         0  \n",
       "84          20         2643         0  \n",
       "727          1           15         0  \n",
       "155         28          110         0  \n",
       "869         18           85         0  "
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "歸藏        42\n",
       "宝玉        32\n",
       "财经数据库     24\n",
       "toyxyz    18\n",
       "-Zho-     15\n",
       "Name: author_name, dtype: int64"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.author_name.value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots\n",
    "Few basic plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
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          "showarrow": false,
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",
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   "source": [
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "def plot_likes_with_cumulative(df, start_date, end_date):\n",
    "    df['date'] = pd.to_datetime(df['date'])\n",
    "    df = df[(df['date'] >= start_date) & (df['date'] <= end_date)]\n",
    "    df['media_type'] = df['media_type'].replace({'No media': 'Text'})\n",
    "    df['year_month'] = df['date'].dt.strftime('%Y-%m')\n",
    "\n",
    "    # Prepare the monthly count DataFrame\n",
    "    monthly_likes_df = df.groupby(['year_month', 'media_type']).size().reset_index(name='counts')\n",
    "\n",
    "    # Prepare the cumulative DataFrame\n",
    "    cumulative_likes_df = monthly_likes_df.copy()\n",
    "    cumulative_likes_df['cumulative_counts'] = cumulative_likes_df.groupby('media_type')['counts'].cumsum()\n",
    "\n",
    "    # Select a color palette\n",
    "    color_palette = px.colors.qualitative.Plotly\n",
    "\n",
    "    # Match colors to media type\n",
    "    color_map = {media_type: color_palette[i] for i, media_type in enumerate([ 'Video', 'Image',  'Text'])}\n",
    "\n",
    "    # Creating subplots\n",
    "    fig = make_subplots(rows=2, cols=1, shared_xaxes=True,\n",
    "                        subplot_titles=(\"Monthly Likes by Media Type\", \"Cumulative Likes by Media Type\"),\n",
    "                        vertical_spacing=0.15)\n",
    "\n",
    "    # Adding the monthly bar chart\n",
    "    for media_type in monthly_likes_df['media_type'].unique():\n",
    "        filtered_df = monthly_likes_df[monthly_likes_df['media_type'] == media_type]\n",
    "        fig.add_trace(go.Bar(x=filtered_df['year_month'], y=filtered_df['counts'], opacity=0.8,\n",
    "                             name=media_type, marker_color=color_map[media_type]),\n",
    "                      row=1, col=1)\n",
    "\n",
    "    # Adding the cumulative area chart\n",
    "    for media_type in cumulative_likes_df['media_type'].unique():\n",
    "        filtered_cumulative_df = cumulative_likes_df[cumulative_likes_df['media_type'] == media_type]\n",
    "        fig.add_trace(go.Scatter(x=filtered_cumulative_df['year_month'], y=filtered_cumulative_df['cumulative_counts'],\n",
    "                                 mode='lines', name=media_type, fill='tonexty',\n",
    "                                 line=dict(color=color_map[media_type])),\n",
    "                      row=2, col=1)\n",
    "\n",
    "    # Update layout for clarity and visual appeal\n",
    "    fig.update_layout(height=560, title_text=\"Likes Analysis by Media Type\", title_x=0.5,  title_font = dict(size=24, family=\"MonaspaceNeon-Regular\", color=\"black\"))\n",
    "    fig.update_xaxes(title_text=\"Month-Year\", row=2, col=1)\n",
    "    fig.update_yaxes(title_text=\"N_Monthly\", row=1, col=1)\n",
    "    fig.update_yaxes(title_text=\"N_Cumulative\", row=2, col=1)\n",
    "\n",
    "    fig.show()\n",
    "\n",
    "# Call the function with your DataFrame and a date range\n",
    "plot_likes_with_cumulative(df, '2023-09-01', '2024-03-07')"
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    "def plot_combined_donut_chart_ordered_correctly(data_df, date_col = 'StrTime'):\n",
    "    # Convert 'StrTime' to datetime and extract the weekday name and number\n",
    "    data_df['WeekdayName'] = data_df[date_col].dt.day_name()\n",
    "    # Map weekday names to numbers for sorting (Monday=0, ..., Sunday=6)\n",
    "    weekdays_map = {'Monday': 0, 'Tuesday': 1, 'Wednesday': 2, 'Thursday': 3, 'Friday': 4, 'Saturday': 5, 'Sunday': 6}\n",
    "    data_df['WeekdayNumber'] = data_df['WeekdayName'].map(weekdays_map)\n",
    "    \n",
    "    # Prepare the data: count messages by WeekdayName, ignoring sender\n",
    "    weekday_distribution = data_df.groupby(['WeekdayName', 'WeekdayNumber']).size().reset_index(name='Count')\n",
    "    \n",
    "    # Sort by WeekdayNumber to ensure correct order\n",
    "    weekday_distribution.sort_values('WeekdayNumber', inplace=True)\n",
    "    \n",
    "    # Now that sorting is done, WeekdayNumber is no longer needed\n",
    "    weekday_distribution = weekday_distribution.drop(columns=['WeekdayNumber'])\n",
    "    \n",
    "    # Create Donut chart with a different color scheme\n",
    "    fig = px.pie(weekday_distribution, names='WeekdayName', values='Count',\n",
    "                 title=\"Tweets Liked by Weekday\",\n",
    "                 color_discrete_sequence=px.colors.qualitative.Plotly,\n",
    "                 hole=0.4)\n",
    "    \n",
    "    # Add slight margin between each day for better visualization\n",
    "    fig.update_traces(textinfo='percent+label', pull=0.02)\n",
    "    fig.update_layout(height=560, title_x=0.5, title_font = dict(size=24, family=\"MonaspaceNeon-Regular\", color=\"black\"), legend=dict(\n",
    "    orientation=\"h\", xanchor=\"center\", x=0.5))\n",
    "    fig.update_traces(sort=False, direction='clockwise')\n",
    "\n",
    "    fig.show()\n",
    "\n",
    "# Ensure data_df['StrTime'] is a datetime column before calling the function\n",
    "plot_combined_donut_chart_ordered_correctly(df, date_col='date')"
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   "cell_type": "code",
   "execution_count": 253,
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      "/Users/jerlin/anaconda3/lib/python3.11/site-packages/plotly_calplot/calplot.py:159: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
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       "                            \n",
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly_calplot import calplot\n",
    "\n",
    "def convert_to_day_df(df):\n",
    "    # Convert the 'date' column to datetime if it's not already\n",
    "    df['date'] = pd.to_datetime(df['date'])\n",
    "    day_df = df.groupby(df['date'].dt.date).size().reset_index(name='num_tweets')\n",
    "    \n",
    "    return day_df\n",
    "\n",
    "day_df = convert_to_day_df(df)\n",
    "day_df.head(2)\n",
    "\n",
    "fig = calplot(day_df[(day_df['date'].astype(str)>'2023-09-01') & (day_df['date'].astype(str) < '2024-03-07')], x=\"date\", y=\"num_tweets\", \n",
    "              start_month = 1,\n",
    "    end_month = 12,\n",
    "    )\n",
    "\n",
    "# set height of the figure\n",
    "fig.update_layout(height=250)\n",
    "# add title \n",
    "fig.update_layout(title='Number of Liked Tweets per Day 2023')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image captions \n",
    "Proof of concept: get high level summary, detailed description and tags for first image from liked tweets. \n",
    "\n",
    "\n",
    "With GPT-4-V. \n",
    "\n",
    "Gemini 1.0 also works pretty great for this usecase.\n",
    "\n",
    "Mainly, convert image to structured json data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "import google.generativeai as genai\n",
    "import replicate\n",
    "import os \n",
    "import json\n",
    "from IPython.display import Image, display\n",
    "import PIL.Image\n",
    "from PIL import Image\n",
    "import urllib.request \n",
    "\n",
    "# 配置 genai 使用环境变量中的 API KEY\n",
    "api_key = os.getenv('GEMINI_API_KEY')\n",
    "if not api_key:\n",
    "    print(\"GEMINI_API_KEY environment variable not set.\")\n",
    "    sys.exit(1)\n",
    "\n",
    "genai.configure(api_key=api_key)\n",
    "\n",
    "# Set up the model\n",
    "generation_config = {\"temperature\": 0.9, \"top_p\": 0.9, \"top_k\": 40, \"max_output_tokens\": 2048}\n",
    "\n",
    "# 从环境变量中获取 API KEY\n",
    "API_KEY = os.getenv('REPLICATE_API_TOKEN')\n",
    "\n",
    "# 配置Replicate客户端使用API KEY\n",
    "replicate_client = replicate.Client(api_token=API_KEY)\n",
    "\n",
    "# 构建 prompt\n",
    "prompt_parts = [\n",
    "  \"You will be given a JSON string (that may not be completely valid), your task is to create a valid JSON results based on the input. Try keep the structure of the input JSON as much as possible.\",\n",
    "]\n",
    "\n",
    "model = genai.GenerativeModel(model_name=\"gemini-1.0-pro-001\", generation_config=generation_config)\n",
    "\n",
    "def get_valid_json_with_gemini(input):\n",
    "    response = model.generate_content(prompt_parts)\n",
    "    # 提取 response 中的 text\n",
    "    if hasattr(response, 'candidates'):\n",
    "        for candidate in response.candidates:\n",
    "            if hasattr(candidate, 'content') and hasattr(candidate.content, 'parts'):\n",
    "                for part in candidate.content.parts:\n",
    "                    return part.text\n",
    "                    print(part.text)\n",
    "\n",
    "def get_tweet_image_captioning(image_url, author_name, text):\n",
    "    # Opening the image for Image Understanding\n",
    "    urllib.request.urlretrieve(image_url, \"images/input.png\") \n",
    "    image = PIL.Image.open('images/input.png')\n",
    "\n",
    "        # 构建提示，结合了文本和图像\n",
    "    prompt_parts = [\n",
    "    f\"\"\" This is a tweet from {author_name}, \n",
    "    with the following text:\n",
    "    {text}.\n",
    "\n",
    "    Below is an image from the tweet.\n",
    "    Your task is to caption the image and provide a description. reference the text or author if necessary. \n",
    "\n",
    "    Output rules you MUST follow:\n",
    "    - \"Summary\": A brief high level, big picture description of the image. What is this and what is this for?\n",
    "    - \"Description\": A detailed description of the image. If there are text in the image, please include all of it in the description. (in its original language)\n",
    "    - \"Tags\": a brief list of high level tags that describe the image. (e.g. \"cat\", \"anime girl\", \"food\")\n",
    "\n",
    "    Return results in a JSON format (with \"Answer\" as key, contains list of  dictionary with exactly these key names):    \n",
    "\"\"\",\n",
    "      image\n",
    "    ]\n",
    "\n",
    "    model = genai.GenerativeModel(model_name=\"gemini-pro-vision\", generation_config=generation_config)\n",
    "    response = model.generate_content(prompt_parts)\n",
    "    # 提取 response 中的 text\n",
    "    if hasattr(response, 'candidates'):\n",
    "        for candidate in response.candidates:\n",
    "            if hasattr(candidate, 'content') and hasattr(candidate.content, 'parts'):\n",
    "                for part in candidate.content.parts:\n",
    "                    return part.text\n",
    "                    print(part.text)\n",
    "\n",
    "    # Post process the response with gemini json mode\n",
    "    json_captions = get_valid_json_with_gemini(text)\n",
    "    return json_captions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "替换成 Replica 的 LlaVa\n",
    "\"\"\"\n",
    "def get_tweet_image_captioning(image_url, author_name, text):\n",
    "    image_prompt = f\"\"\" \n",
    "    This is a tweet from {author_name}, \n",
    "    with the following text:\n",
    "    {text}.\n",
    "\n",
    "    Below is an image from the tweet.\n",
    "    Your task is to caption the image and provide a description. reference the text or author if necessary. \n",
    "\n",
    "    Output rules you MUST follow:\n",
    "    - \"Summary\": A brief high level, big picture description of the image. What is this and what is this for?\n",
    "    - \"Description\": A detailed description of the image. If there are text in the image, please include all of it in the description. (in its original language)\n",
    "    - \"Tags\": a brief list of high level tags that describe the image. (e.g. \"cat\", \"anime girl\", \"food\")\n",
    "\n",
    "    Return results in a JSON format (with \"Answer\" as key, contains list of  dictionary with exactly these key names):    \n",
    "\"\"\"\n",
    "    output = replicate.run(\n",
    "        \"yorickvp/llava-v1.6-34b:41ecfbfb261e6c1adf3ad896c9066ca98346996d7c4045c5bc944a79d430f174\",   \n",
    "        input={\n",
    "            \"image\": image_url,\n",
    "            \"temperature\": 0.8,\n",
    "            \"top_p\": 0.9,\n",
    "            \"top_k\": 10,\n",
    "            \"prompt\": image_prompt,\n",
    "            \"history\": [],\n",
    "            \"max_tokens\": 1024\n",
    "        }\n",
    "    )\n",
    "    \n",
    "    for item in output:\n",
    "        print(item)\n",
    "        return item\n",
    "\n",
    "    # Post process the response with gemini json mode\n",
    "    json_captions = get_valid_json_with_gemini(text)\n",
    "    return json_captions\n",
    "\"\"\"    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_image_df = df[df.media_type=='Image'].head(30)\n",
    "sample_image_urls = sample_image_df.images_urls.tolist()\n",
    "sample_authors = sample_image_df.author_name.tolist()\n",
    "sample_texts = sample_image_df.text.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://pbs.twimg.com/media/GHy9ZhmaoAA24mJ?format=jpg&name=medium\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import PIL.Image\n",
    "from IPython.display import Image, display\n",
    "\n",
    "cur_idx = 26\n",
    "image_url = sample_image_urls[cur_idx][0]\n",
    "author_name = sample_authors[cur_idx]\n",
    "text = sample_texts[cur_idx]\n",
    "\n",
    "display(Image(url=image_url))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image url: https://pbs.twimg.com/media/GHy9ZhmaoAA24mJ?format=jpg&name=medium\n",
      "Author name: ばむ\n",
      "Text: セクシーなのキュートなの\n",
      "どっちが好きなの〜？\n"
     ]
    }
   ],
   "source": [
    "print(f\"Image url: {image_url}\\n\"\n",
    "      f\"Author name: {author_name}\\n\"\n",
    "      f\"Text: {text}\"   )\n",
    "\n",
    "result = get_tweet_image_captioning(image_url, author_name, text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ```json\n",
      "{\n",
      "  \"Answer\": [\n",
      "    {\n",
      "      \"Summary\": \"A photo of a woman in a revealing cheongsam.\",\n",
      "      \"Description\": \"The woman is kneeling on the ground with her hands on her knees. She is wearing a blue and green cheongsam with a very low neckline and a high slit, showing off her cleavage and thighs. Her hair is long and black, and she is wearing a pearl necklace and earrings. She has a seductive expression on her face and is looking at the camera.\",\n",
      "      \"Tags\": [\"woman\", \"cheongsam\", \"sexy\", \"cleavage\", \"thighs\"]\n",
      "    }\n",
      "  ]\n",
      "}\n",
      "```\n"
     ]
    }
   ],
   "source": [
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7s/pwtjw5yx6cng0_cqs3j44_4w0000gn/T/ipykernel_62210/3040141009.py:21: FutureWarning:\n",
      "\n",
      "this method is deprecated in favour of `Styler.hide(axis=\"index\")`\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_6d245_row0_col0, #T_6d245_row0_col1, #T_6d245_row0_col2 {\n",
       "  text-align: left;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_6d245\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_6d245_level0_col0\" class=\"col_heading level0 col0\" >Summary</th>\n",
       "      <th id=\"T_6d245_level0_col1\" class=\"col_heading level0 col1\" >Description</th>\n",
       "      <th id=\"T_6d245_level0_col2\" class=\"col_heading level0 col2\" >Tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_6d245_row0_col0\" class=\"data row0 col0\" >A photo of a woman in a revealing cheongsam.</td>\n",
       "      <td id=\"T_6d245_row0_col1\" class=\"data row0 col1\" >The woman is kneeling on the ground with her hands on her knees. She is wearing a blue and green cheongsam with a very low neckline and a high slit, showing off her cleavage and thighs. Her hair is long and black, and she is wearing a pearl necklace and earrings. She has a seductive expression on her face and is looking at the camera.</td>\n",
       "      <td id=\"T_6d245_row0_col2\" class=\"data row0 col2\" >['woman', 'cheongsam', 'sexy', 'cleavage', 'thighs']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b232cc50>"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "result_str = result\n",
    "\n",
    "# 数据预处理：移除可能导致解析错误的字符\n",
    "result_str_clean = result_str.strip().strip('```')  # 首先去除字符串两端的空白字符，然后去除首尾的```\n",
    "result_str_clean = result_str_clean.replace('json\\n', '')  # 移除json字符及后面的换行符\n",
    "\n",
    "# 解析JSON\n",
    "data = json.loads(result_str_clean)\n",
    "\n",
    "answers = data['Answer']\n",
    "\n",
    "df = pd.DataFrame(answers, columns=['Summary', 'Description', 'Tags'])\n",
    "\n",
    "# 设置显示选项以允许长文本自动换行\n",
    "pd.set_option('display.max_colwidth', None)  # None 表示自动调整列宽以适应内容\n",
    "\n",
    "# 这里直接应用样式设置，确保文本自动换行\n",
    "# 隐藏默认索引\n",
    "df_styled = df.style.set_properties(**{'text-align': 'left', 'white-space': 'pre-wrap'}).hide_index()\n",
    "\n",
    "df_styled"
   ]
  }
 ],
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