Make clickable

app.ipynb

@@ -8,44 +8,47 @@
    "source": [
     "# |export\n",
     "import gradio as gr\n",
-    "import pandas as pd"
+    "import pandas as pd\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "# |export\n",
-    "df = pd.read_csv(\"https://docs.google.com/spreadsheets/d/e/2PACX-1vSC40sszorOjHfozmNqJT9lFiJhG94u3fbr3Ss_7fzcU3xqqJQuW1Ie_SNcWEB-uIsBi9NBUK7-ddet/pub?output=csv\", skiprows=1)"
+    "df = pd.read_csv(\n",
+    "    \"https://docs.google.com/spreadsheets/d/e/2PACX-1vSC40sszorOjHfozmNqJT9lFiJhG94u3fbr3Ss_7fzcU3xqqJQuW1Ie_SNcWEB-uIsBi9NBUK7-ddet/pub?output=csv\",\n",
+    "    skiprows=1,\n",
+    ")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "# |export\n",
     "# Drop footers\n",
-    "df = df.copy()[~df[\"Model\"].isna()]"
+    "df = df.copy()[~df[\"Model\"].isna()]\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "# |export\n",
     "# Drop TBA models\n",
-    "df = df.copy()[df[\"Parameters \\n(B)\"] != \"TBA\"]"
+    "df = df.copy()[df[\"Parameters \\n(B)\"] != \"TBA\"]\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -79,14 +82,14 @@
        "      <th>Announced\\n▼</th>\n",
        "      <th>Public?</th>\n",
        "      <th>Released</th>\n",
-       "      <th>Paper/\\nRepo</th>\n",
+       "      <th>Paper / Repo</th>\n",
        "      <th>Notes</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>KOSMOS-1</td>\n",
+       "      <th>3</th>\n",
+       "      <td>Kosmos-1</td>\n",
        "      <td>Microsoft</td>\n",
        "      <td>NaN</td>\n",
        "      <td>1.6</td>\n",
@@ -96,14 +99,14 @@
        "      <td>Feb/2023</td>\n",
        "      <td>🔴</td>\n",
        "      <td>Feb/2023</td>\n",
-       "      <td>🔗</td>\n",
+       "      <td>https://arxiv.org/abs/2302.14045</td>\n",
        "      <td>Multimodal large language model (MLLM). Raven’...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>4</th>\n",
        "      <td>LLaMA-65B</td>\n",
        "      <td>Meta AI</td>\n",
-       "      <td>https://research.facebook.com/publications/lla...</td>\n",
+       "      <td>Weights leaked: https://github.com/facebookres...</td>\n",
        "      <td>65</td>\n",
        "      <td>1400</td>\n",
        "      <td>22:1</td>\n",
@@ -111,11 +114,11 @@
        "      <td>Feb/2023</td>\n",
        "      <td>🟡</td>\n",
        "      <td>Feb/2023</td>\n",
-       "      <td>🔗</td>\n",
+       "      <td>https://research.facebook.com/publications/lla...</td>\n",
        "      <td>Researchers only, noncommercial only. 'LLaMA-6...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>5</th>\n",
        "      <td>MOSS</td>\n",
        "      <td>Fudan University</td>\n",
        "      <td>https://moss.fastnlp.top/</td>\n",
@@ -126,11 +129,26 @@
        "      <td>Feb/2023</td>\n",
        "      <td>🟢</td>\n",
        "      <td>Feb/2023</td>\n",
-       "      <td>🔗</td>\n",
+       "      <td>https://txsun1997.github.io/blogs/moss.html</td>\n",
        "      <td>Major bandwidth issues: https://www.reuters.co...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <td>Palmyra</td>\n",
+       "      <td>Writer</td>\n",
+       "      <td>https://huggingface.co/models?search=palmyra</td>\n",
+       "      <td>20</td>\n",
+       "      <td>300</td>\n",
+       "      <td>15:1</td>\n",
+       "      <td>🌋</td>\n",
+       "      <td>Feb/2023</td>\n",
+       "      <td>🟢</td>\n",
+       "      <td>Feb/2023</td>\n",
+       "      <td>https://writer.com/blog/palmyra/</td>\n",
+       "      <td>Only up to 5B available open-source 'trained o...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
        "      <td>Luminous Supreme Control</td>\n",
        "      <td>Aleph Alpha</td>\n",
        "      <td>https://app.aleph-alpha.com/playground/completion</td>\n",
@@ -141,77 +159,69 @@
        "      <td>Feb/2023</td>\n",
        "      <td>🟢</td>\n",
        "      <td>Feb/2023</td>\n",
-       "      <td>🔗</td>\n",
+       "      <td>https://docs.aleph-alpha.com/docs/introduction...</td>\n",
        "      <td>‘Control’ means instruction tuned</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Multimodal-CoT</td>\n",
-       "      <td>Amazon</td>\n",
-       "      <td>https://github.com/amazon-science/mm-cot</td>\n",
-       "      <td>0.738</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>🌋</td>\n",
-       "      <td>Feb/2023</td>\n",
-       "      <td>🟢</td>\n",
-       "      <td>Feb/2023</td>\n",
-       "      <td>🔗</td>\n",
-       "      <td>Models &lt;1B with vision CoT</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "                      Model               Lab  \\\n",
-       "2                  KOSMOS-1         Microsoft   \n",
-       "3                 LLaMA-65B           Meta AI   \n",
-       "4                      MOSS  Fudan University   \n",
-       "5  Luminous Supreme Control       Aleph Alpha   \n",
-       "6            Multimodal-CoT            Amazon   \n",
+       "3                  Kosmos-1         Microsoft   \n",
+       "4                 LLaMA-65B           Meta AI   \n",
+       "5                      MOSS  Fudan University   \n",
+       "6                   Palmyra            Writer   \n",
+       "7  Luminous Supreme Control       Aleph Alpha   \n",
        "\n",
        "                              Selected \\nplaygrounds Parameters \\n(B)  \\\n",
-       "2                                                NaN              1.6   \n",
-       "3  https://research.facebook.com/publications/lla...               65   \n",
-       "4                          https://moss.fastnlp.top/               20   \n",
-       "5  https://app.aleph-alpha.com/playground/completion               70   \n",
-       "6           https://github.com/amazon-science/mm-cot            0.738   \n",
+       "3                                                NaN              1.6   \n",
+       "4  Weights leaked: https://github.com/facebookres...               65   \n",
+       "5                          https://moss.fastnlp.top/               20   \n",
+       "6       https://huggingface.co/models?search=palmyra               20   \n",
+       "7  https://app.aleph-alpha.com/playground/completion               70   \n",
        "\n",
        "  Tokens \\ntrained (B) Ratio T:P\\n(Chinchilla scaling) Training dataset  \\\n",
-       "2                  360                           225:1         🆆 📚⬆ 🕸 🌋   \n",
-       "3                 1400                            22:1         🆆 📚⬆ 🕸 🌋   \n",
-       "4                  430                            22:1              🕸 🌋   \n",
-       "5                  NaN                             NaN         🆆 📚⬆ 🕸 👥   \n",
-       "6                  NaN                             NaN                🌋   \n",
+       "3                  360                           225:1         🆆 📚⬆ 🕸 🌋   \n",
+       "4                 1400                            22:1         🆆 📚⬆ 🕸 🌋   \n",
+       "5                  430                            22:1              🕸 🌋   \n",
+       "6                  300                            15:1                🌋   \n",
+       "7                  NaN                             NaN         🆆 📚⬆ 🕸 👥   \n",
+       "\n",
+       "  Announced\\n▼ Public?  Released  \\\n",
+       "3     Feb/2023       🔴  Feb/2023   \n",
+       "4     Feb/2023       🟡  Feb/2023   \n",
+       "5     Feb/2023       🟢  Feb/2023   \n",
+       "6     Feb/2023       🟢  Feb/2023   \n",
+       "7     Feb/2023       🟢  Feb/2023   \n",
        "\n",
-       "  Announced\\n▼ Public?  Released Paper/\\nRepo  \\\n",
-       "2     Feb/2023       🔴  Feb/2023            🔗   \n",
-       "3     Feb/2023       🟡  Feb/2023            🔗   \n",
-       "4     Feb/2023       🟢  Feb/2023            🔗   \n",
-       "5     Feb/2023       🟢  Feb/2023            🔗   \n",
-       "6     Feb/2023       🟢  Feb/2023            🔗   \n",
+       "                                        Paper / Repo  \\\n",
+       "3                   https://arxiv.org/abs/2302.14045   \n",
+       "4  https://research.facebook.com/publications/lla...   \n",
+       "5        https://txsun1997.github.io/blogs/moss.html   \n",
+       "6                   https://writer.com/blog/palmyra/   \n",
+       "7  https://docs.aleph-alpha.com/docs/introduction...   \n",
        "\n",
        "                                               Notes  \n",
-       "2  Multimodal large language model (MLLM). Raven’...  \n",
-       "3  Researchers only, noncommercial only. 'LLaMA-6...  \n",
-       "4  Major bandwidth issues: https://www.reuters.co...  \n",
-       "5                  ‘Control’ means instruction tuned  \n",
-       "6                         Models <1B with vision CoT  "
+       "3  Multimodal large language model (MLLM). Raven’...  \n",
+       "4  Researchers only, noncommercial only. 'LLaMA-6...  \n",
+       "5  Major bandwidth issues: https://www.reuters.co...  \n",
+       "6  Only up to 5B available open-source 'trained o...  \n",
+       "7                  ‘Control’ means instruction tuned  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.head()"
+    "df.head()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -245,13 +255,13 @@
        "      <th>Announced\\n▼</th>\n",
        "      <th>Public?</th>\n",
        "      <th>Released</th>\n",
-       "      <th>Paper/\\nRepo</th>\n",
+       "      <th>Paper / Repo</th>\n",
        "      <th>Notes</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>88</th>\n",
+       "      <th>92</th>\n",
        "      <td>Meena</td>\n",
        "      <td>Google</td>\n",
        "      <td>NaN</td>\n",
@@ -262,11 +272,11 @@
        "      <td>Jan/2020</td>\n",
        "      <td>🔴</td>\n",
        "      <td>Jan/2020</td>\n",
-       "      <td>🔗</td>\n",
+       "      <td>https://arxiv.org/abs/2001.09977</td>\n",
        "      <td>Dialogue model. Trained 61B tokens for 164x ep...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>89</th>\n",
+       "      <th>93</th>\n",
        "      <td>RoBERTa</td>\n",
        "      <td>Meta AI</td>\n",
        "      <td>Hugging Face</td>\n",
@@ -277,11 +287,11 @@
        "      <td>Jul/2019</td>\n",
        "      <td>🟢</td>\n",
        "      <td>Jul/2019</td>\n",
-       "      <td>🔗</td>\n",
+       "      <td>https://arxiv.org/abs/1907.11692</td>\n",
        "      <td>See cite ROBERTA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>90</th>\n",
+       "      <th>94</th>\n",
        "      <td>GPT-2</td>\n",
        "      <td>OpenAI</td>\n",
        "      <td>Hugging Face</td>\n",
@@ -292,11 +302,11 @@
        "      <td>Feb/2019</td>\n",
        "      <td>🟢</td>\n",
        "      <td>Nov/2019</td>\n",
-       "      <td>🔗</td>\n",
+       "      <td>https://openai.com/blog/better-language-models/</td>\n",
        "      <td>Reddit outbound only</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>91</th>\n",
+       "      <th>95</th>\n",
        "      <td>GPT-1</td>\n",
        "      <td>OpenAI</td>\n",
        "      <td>Hugging Face</td>\n",
@@ -307,11 +317,11 @@
        "      <td>Jun/2018</td>\n",
        "      <td>🟢</td>\n",
        "      <td>Jun/2018</td>\n",
-       "      <td>🔗</td>\n",
+       "      <td>https://openai.com/blog/language-unsupervised/</td>\n",
        "      <td>Books only</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>92</th>\n",
+       "      <th>96</th>\n",
        "      <td>BERT</td>\n",
        "      <td>Google</td>\n",
        "      <td>Hugging Face</td>\n",
@@ -322,7 +332,7 @@
        "      <td>Oct/2018</td>\n",
        "      <td>🟢</td>\n",
        "      <td>Oct/2018</td>\n",
-       "      <td>🔗</td>\n",
+       "      <td>https://arxiv.org/abs/1810.04805</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -331,58 +341,86 @@
       ],
       "text/plain": [
        "      Model      Lab Selected \\nplaygrounds Parameters \\n(B)  \\\n",
-       "88    Meena   Google                    NaN              2.6   \n",
-       "89  RoBERTa  Meta AI           Hugging Face            0.355   \n",
-       "90    GPT-2   OpenAI           Hugging Face              1.5   \n",
-       "91    GPT-1   OpenAI           Hugging Face              0.1   \n",
-       "92     BERT   Google           Hugging Face              0.3   \n",
+       "92    Meena   Google                    NaN              2.6   \n",
+       "93  RoBERTa  Meta AI           Hugging Face            0.355   \n",
+       "94    GPT-2   OpenAI           Hugging Face              1.5   \n",
+       "95    GPT-1   OpenAI           Hugging Face              0.1   \n",
+       "96     BERT   Google           Hugging Face              0.3   \n",
        "\n",
        "   Tokens \\ntrained (B) Ratio T:P\\n(Chinchilla scaling) Training dataset  \\\n",
-       "88                10000                         3,847:1              👥 🌋   \n",
-       "89                 2200                         6,198:1          🆆 📚 ⬆ 🕸   \n",
-       "90                   10                             7:1                ⬆   \n",
-       "91                  NaN                             NaN                📚   \n",
-       "92                  137                           457:1              🆆 📚   \n",
+       "92                10000                         3,847:1              👥 🌋   \n",
+       "93                 2200                         6,198:1          🆆 📚 ⬆ 🕸   \n",
+       "94                   10                             7:1                ⬆   \n",
+       "95                  NaN                             NaN                📚   \n",
+       "96                  137                           457:1              🆆 📚   \n",
+       "\n",
+       "   Announced\\n▼ Public?  Released  \\\n",
+       "92     Jan/2020       🔴  Jan/2020   \n",
+       "93     Jul/2019       🟢  Jul/2019   \n",
+       "94     Feb/2019       🟢  Nov/2019   \n",
+       "95     Jun/2018       🟢  Jun/2018   \n",
+       "96     Oct/2018       🟢  Oct/2018   \n",
        "\n",
-       "   Announced\\n▼ Public?  Released Paper/\\nRepo  \\\n",
-       "88     Jan/2020       🔴  Jan/2020            🔗   \n",
-       "89     Jul/2019       🟢  Jul/2019            🔗   \n",
-       "90     Feb/2019       🟢  Nov/2019            🔗   \n",
-       "91     Jun/2018       🟢  Jun/2018            🔗   \n",
-       "92     Oct/2018       🟢  Oct/2018            🔗   \n",
+       "                                       Paper / Repo  \\\n",
+       "92                 https://arxiv.org/abs/2001.09977   \n",
+       "93                 https://arxiv.org/abs/1907.11692   \n",
+       "94  https://openai.com/blog/better-language-models/   \n",
+       "95   https://openai.com/blog/language-unsupervised/   \n",
+       "96                 https://arxiv.org/abs/1810.04805   \n",
        "\n",
        "                                                Notes  \n",
-       "88  Dialogue model. Trained 61B tokens for 164x ep...  \n",
-       "89                                   See cite ROBERTA  \n",
-       "90                               Reddit outbound only  \n",
-       "91                                         Books only  \n",
-       "92                                                NaN  "
+       "92  Dialogue model. Trained 61B tokens for 164x ep...  \n",
+       "93                                   See cite ROBERTA  \n",
+       "94                               Reddit outbound only  \n",
+       "95                                         Books only  \n",
+       "96                                                NaN  "
       ]
      },
-     "execution_count": 25,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.tail()"
+    "df.tail()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# |export\n",
+    "def make_clickable_cell(cell):\n",
+    "    return f'<a  target=\"_blank\" href=\"{cell}\">{cell}</a>'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# |export\n",
+    "df[\"Paper / Repo\"] = df[\"Paper / Repo\"].apply(make_clickable_cell)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "# |export\n",
     "title = \"\"\"<h1 align=\"center\">The Large Language Models Landscape</h1>\"\"\"\n",
     "description = \"\"\"Large Language Models (LLMs) today come in a variety architectures and capabilities. This interactive landscape provides a visual overview of the most important LLMs, including their training data, size, release date, and whether they are openly accessible or not. It also includes notes on each model to provide additional context. This landscape is derived from data compiled by Dr. Alan D. Thompson at [lifearchitect.ai](https://lifearchitect.ai).\n",
-    "\"\"\""
+    "\"\"\"\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -410,7 +448,7 @@
      "data": {
       "text/plain": []
      },
-     "execution_count": 32,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -420,29 +458,30 @@
     "def value_func():\n",
     "    return df\n",
     "\n",
+    "\n",
     "with gr.Blocks() as demo:\n",
     "    gr.Markdown(title)\n",
     "    gr.Markdown(description)\n",
-    "    gr.DataFrame(value=value_func)\n",
+    "    gr.components.DataFrame(value=value_func)\n",
     "\n",
-    "demo.launch()"
+    "demo.launch()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Closing server running on port: 7862\n"
+      "Closing server running on port: 7861\n"
      ]
     }
    ],
    "source": [
-    "demo.close()"
+    "demo.close()\n"
    ]
   },
   {
@@ -453,7 +492,7 @@
    "source": [
     "from nbdev.export import nb_export\n",
     "\n",
-    "nb_export(\"app.ipynb\", lib_path=\".\", name=\"app\")"
+    "nb_export(\"app.ipynb\", lib_path=\".\", name=\"app\")\n"
    ]
   },
   {

app.py

@@ -1,35 +1,53 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
 
 # %% auto 0
-__all__ = ['df', 'title', 'description', 'value_func']
+__all__ = ['df', 'title', 'description', 'make_clickable_cell', 'value_func']
 
 # %% app.ipynb 0
 import gradio as gr
 import pandas as pd
 
+
 # %% app.ipynb 1
-df = pd.read_csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vSC40sszorOjHfozmNqJT9lFiJhG94u3fbr3Ss_7fzcU3xqqJQuW1Ie_SNcWEB-uIsBi9NBUK7-ddet/pub?output=csv", skiprows=1)
+df = pd.read_csv(
+    "https://docs.google.com/spreadsheets/d/e/2PACX-1vSC40sszorOjHfozmNqJT9lFiJhG94u3fbr3Ss_7fzcU3xqqJQuW1Ie_SNcWEB-uIsBi9NBUK7-ddet/pub?output=csv",
+    skiprows=1,
+)
+
 
 # %% app.ipynb 2
 # Drop footers
 df = df.copy()[~df["Model"].isna()]
 
+
 # %% app.ipynb 3
 # Drop TBA models
 df = df.copy()[df["Parameters \n(B)"] != "TBA"]
 
+
 # %% app.ipynb 6
+def make_clickable_cell(cell):
+    return f'<a  target="_blank" href="{cell}">{cell}</a>'
+
+
+# %% app.ipynb 7
+df["Paper / Repo"] = df["Paper / Repo"].apply(make_clickable_cell)
+
+# %% app.ipynb 8
 title = """<h1 align="center">The Large Language Models Landscape</h1>"""
 description = """Large Language Models (LLMs) today come in a variety architectures and capabilities. This interactive landscape provides a visual overview of the most important LLMs, including their training data, size, release date, and whether they are openly accessible or not. It also includes notes on each model to provide additional context. This landscape is derived from data compiled by Dr. Alan D. Thompson at [lifearchitect.ai](https://lifearchitect.ai).
 """
 
-# %% app.ipynb 7
+
+# %% app.ipynb 9
 def value_func():
     return df
 
+
 with gr.Blocks() as demo:
     gr.Markdown(title)
     gr.Markdown(description)
-    gr.DataFrame(value=value_func)
+    gr.components.DataFrame(value=value_func)
 
 demo.launch()
+