Upload folder using huggingface_hub

requirements.txt

@@ -1,2 +1,2 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@83c709ab2e17a140a8b22c30a0f10e7dedfe17cd#subdirectory=client/python
-https://gradio-pypi-previews.s3.amazonaws.com/83c709ab2e17a140a8b22c30a0f10e7dedfe17cd/gradio-5.23.3-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@36da6d0d5466dd251f46359019959702523f1afc#subdirectory=client/python
+https://gradio-pypi-previews.s3.amazonaws.com/36da6d0d5466dd251f46359019959702523f1afc/gradio-5.23.3-py3-none-any.whl

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dataframe_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "import numpy as np\n", "\n", "def update_dataframe():\n", "    regular_df = pd.DataFrame(np.random.randint(1, 10, size=(5, 5)), columns=pd.Index([str(i) for i in range(5)]))\n", "    wide_df = pd.DataFrame([\n", "        [5, 22, 91, 17, 73, 38, 84, 46, 65, 10, 155, 122, 11, 144, 133],\n", "        [81, 42, 13, 97, 33, 77, 59, 100, 29, 61, 213, 195, 142, 118, 127],\n", "        [37, 71, 63, 102, 28, 94, 19, 55, 88, 44, 116, 139, 122, 150, 147],\n", "        [104, 52, 49, 26, 83, 67, 31, 92, 79, 18, 241, 115, 159, 123, 137],\n", "        [16, 95, 74, 68, 43, 101, 27, 85, 39, 57, 129, 148, 132, 111, 156]\n", "    ], columns=pd.Index([f\"col_{i}\" for i in range(15)]))\n", "    tall_df = pd.DataFrame(np.random.randint(1, 10, size=(50, 3)), columns=pd.Index([\"A\", \"B\", \"C\"]))\n", "    return regular_df, wide_df, tall_df\n", "\n", "def clear_dataframes():\n", "    regular_empty_df = pd.DataFrame([], columns=pd.Index([str(i) for i in range(5)]))\n", "    wide_empty_df = pd.DataFrame([], columns=pd.Index([f\"col_{i}\" for i in range(15)]))\n", "    tall_empty_df = pd.DataFrame([], columns=pd.Index([\"A\", \"B\", \"C\"]))\n", "    return regular_empty_df, wide_empty_df, tall_empty_df\n", "\n", "def increment_select_counter(evt: gr.SelectData, count):\n", "    count_val = 1 if count is None else count + 1\n", "    return count_val, evt.index, evt.value\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column(scale=1):\n", "            initial_regular_df = pd.DataFrame(np.zeros((5, 5), dtype=int), columns=pd.Index([str(i) for i in range(5)]))\n", "\n", "            df = gr.Dataframe(\n", "                value=initial_regular_df,\n", "                interactive=True,\n", "                label=\"Interactive Dataframe\",\n", "                show_label=True,\n", "                elem_id=\"dataframe\",\n", "                show_search=\"filter\",\n", "                show_copy_button=True,\n", "                show_row_numbers=True,\n", "                static_columns=[4]\n", "            )\n", "\n", "        with gr.Column(scale=1):\n", "            initial_wide_df = pd.DataFrame(np.zeros((5, 15), dtype=int), columns=pd.Index([f\"col_{i}\" for i in range(15)]))\n", "\n", "            df_view = gr.Dataframe(\n", "                value=initial_wide_df,\n", "                interactive=False,\n", "                label=\"Non-Interactive View (Scroll Horizontally)\",\n", "                show_label=True,\n", "                show_search=\"search\",\n", "                elem_id=\"non-interactive-dataframe\",\n", "                show_copy_button=True,\n", "                show_row_numbers=True,\n", "                show_fullscreen_button=True,\n", "            )\n", "\n", "    with gr.Row():\n", "        initial_tall_df = pd.DataFrame(np.zeros((50, 3), dtype=int), columns=pd.Index([\"A\", \"B\", \"C\"]))\n", "\n", "        df_tall = gr.Dataframe(\n", "            value=initial_tall_df,\n", "            interactive=False,\n", "            label=\"Tall Dataframe (Scroll Vertically)\",\n", "            show_label=True,\n", "            elem_id=\"dataframe_tall\",\n", "            show_copy_button=True,\n", "            show_row_numbers=True,\n", "            max_height=300,\n", "        )\n", "\n", "    with gr.Row():\n", "        with gr.Column():\n", "            update_btn = gr.Button(\"Update dataframe\", elem_id=\"update_btn\")\n", "            clear_btn = gr.Button(\"Clear dataframe\", elem_id=\"clear_btn\")\n", "\n", "    with gr.Row():\n", "        change_events = gr.Number(\n", "            value=0, label=\"Change events\", elem_id=\"change_events\"\n", "        )\n", "        input_events = gr.Number(value=0, label=\"Input events\", elem_id=\"input_events\")\n", "        select_events = gr.Number(\n", "            value=0, label=\"Select events\", elem_id=\"select_events\"\n", "        )\n", "\n", "    with gr.Row():\n", "        selected_cell_index = gr.Textbox(\n", "            label=\"Selected cell index\", elem_id=\"selected_cell_index\"\n", "        )\n", "        selected_cell_value = gr.Textbox(\n", "            label=\"Selected cell value\", elem_id=\"selected_cell_value\"\n", "        )\n", "\n", "    update_btn.click(fn=update_dataframe, outputs=[df, df_view, df_tall])\n", "    clear_btn.click(fn=clear_dataframes, outputs=[df, df_view, df_tall])\n", "    df.change(fn=lambda x: x + 1, inputs=[change_events], outputs=[change_events])\n", "    df.input(fn=lambda x: x + 1, inputs=[input_events], outputs=[input_events])\n", "    df.select(\n", "        fn=increment_select_counter,\n", "        inputs=[select_events],\n", "        outputs=[select_events, selected_cell_index, selected_cell_value],\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dataframe_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "import numpy as np\n", "\n", "def update_dataframe():\n", "    regular_df = pd.DataFrame(np.random.randint(1, 10, size=(5, 5)), columns=pd.Index([str(i) for i in range(5)]))\n", "    wide_df = pd.DataFrame([\n", "        [5, 22, 91, 17, 73, 38, 84, 46, 65, 10, 155, 122, 11, 144, 133],\n", "        [81, 42, 13, 97, 33, 77, 59, 100, 29, 61, 213, 195, 142, 118, 127],\n", "        [37, 71, 63, 102, 28, 94, 19, 55, 88, 44, 116, 139, 122, 150, 147],\n", "        [104, 52, 49, 26, 83, 67, 31, 92, 79, 18, 241, 115, 159, 123, 137],\n", "        [16, 95, 74, 68, 43, 101, 27, 85, 39, 57, 129, 148, 132, 111, 156]\n", "    ], columns=pd.Index([f\"col_{i}\" for i in range(15)]))\n", "    return regular_df, wide_df\n", "\n", "def clear_dataframes():\n", "    regular_empty_df = pd.DataFrame([], columns=pd.Index([str(i) for i in range(5)]))\n", "    wide_empty_df = pd.DataFrame([], columns=pd.Index([f\"col_{i}\" for i in range(15)]))\n", "    return regular_empty_df, wide_empty_df\n", "\n", "def increment_select_counter(evt: gr.SelectData, count):\n", "    count_val = 1 if count is None else count + 1\n", "    return count_val, evt.index, evt.value\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column(scale=1):\n", "            initial_regular_df = pd.DataFrame(np.zeros((5, 5), dtype=int), columns=pd.Index([str(i) for i in range(5)]))\n", "\n", "            df = gr.Dataframe(\n", "                value=initial_regular_df,\n", "                interactive=True,\n", "                label=\"Interactive Dataframe\",\n", "                show_label=True,\n", "                elem_id=\"dataframe\",\n", "                show_search=\"filter\",\n", "                show_copy_button=True,\n", "                show_row_numbers=True,\n", "                static_columns=[4]\n", "            )\n", "\n", "        with gr.Column(scale=1):\n", "            initial_wide_df = pd.DataFrame(np.zeros((5, 15), dtype=int), columns=pd.Index([f\"col_{i}\" for i in range(15)]))\n", "\n", "            df_view = gr.Dataframe(\n", "                value=initial_wide_df,\n", "                interactive=False,\n", "                label=\"Non-Interactive View (Scroll Horizontally)\",\n", "                show_label=True,\n", "                show_search=\"search\",\n", "                elem_id=\"non-interactive-dataframe\",\n", "                show_copy_button=True,\n", "                show_row_numbers=True,\n", "                show_fullscreen_button=True,\n", "            )\n", "\n", "    tall_df_value = [\n", "        [\"DeepSeek Coder\", 79.3],\n", "        [\"Llama 3.3\", 68.9],\n", "        [\"Qwen 2.5\", 61.9],\n", "        [\"Gemma 2\", 59.5],\n", "        [\"GPT 2\", 18.3],\n", "    ]\n", "\n", "    def get_display_value(values):\n", "        display_values = []\n", "        medals = [\"\ud83e\udd47\", \"\ud83e\udd48\", \"\ud83e\udd49\"]\n", "        for i, row in enumerate(values):\n", "            if i < 3:\n", "                display_values.append([f\"{medals[i]} {row[0]}\", row[1]])\n", "            else:\n", "                display_values.append([row[0], row[1]])\n", "        return display_values\n", "\n", "    display_value = get_display_value(tall_df_value)\n", "\n", "    tall_df_value = {\n", "        \"data\": tall_df_value,\n", "        \"headers\": [\"Model\", \"% Correct (LeetCode Hard)\"],\n", "        \"metadata\": {\n", "            \"display_value\": display_value\n", "        }\n", "    }\n", "\n", "    with gr.Row():\n", "        df_tall = gr.Dataframe(\n", "            value=tall_df_value,\n", "            interactive=False,\n", "            label=\"Tall Dataframe (Scroll Vertically)\",\n", "            show_label=True,\n", "            elem_id=\"dataframe_tall\",\n", "            show_copy_button=True,\n", "            show_row_numbers=True,\n", "            max_height=300,\n", "        )\n", "\n", "    with gr.Row():\n", "        with gr.Column():\n", "            update_btn = gr.Button(\"Update dataframe\", elem_id=\"update_btn\")\n", "            clear_btn = gr.Button(\"Clear dataframe\", elem_id=\"clear_btn\")\n", "\n", "    with gr.Row():\n", "        change_events = gr.Number(\n", "            value=0, label=\"Change events\", elem_id=\"change_events\"\n", "        )\n", "        input_events = gr.Number(value=0, label=\"Input events\", elem_id=\"input_events\")\n", "        select_events = gr.Number(\n", "            value=0, label=\"Select events\", elem_id=\"select_events\"\n", "        )\n", "\n", "    with gr.Row():\n", "        selected_cell_index = gr.Textbox(\n", "            label=\"Selected cell index\", elem_id=\"selected_cell_index\"\n", "        )\n", "        selected_cell_value = gr.Textbox(\n", "            label=\"Selected cell value\", elem_id=\"selected_cell_value\"\n", "        )\n", "\n", "    update_btn.click(fn=update_dataframe, outputs=[df, df_view])\n", "    clear_btn.click(fn=clear_dataframes, outputs=[df, df_view, df_tall])\n", "    df.change(fn=lambda x: x + 1, inputs=[change_events], outputs=[change_events])\n", "    df.input(fn=lambda x: x + 1, inputs=[input_events], outputs=[input_events])\n", "    df.select(\n", "        fn=increment_select_counter,\n", "        inputs=[select_events],\n", "        outputs=[select_events, selected_cell_index, selected_cell_value],\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -11,14 +11,12 @@ def update_dataframe():
         [104, 52, 49, 26, 83, 67, 31, 92, 79, 18, 241, 115, 159, 123, 137],
         [16, 95, 74, 68, 43, 101, 27, 85, 39, 57, 129, 148, 132, 111, 156]
     ], columns=pd.Index([f"col_{i}" for i in range(15)]))
-    tall_df = pd.DataFrame(np.random.randint(1, 10, size=(50, 3)), columns=pd.Index(["A", "B", "C"]))
-    return regular_df, wide_df, tall_df
+    return regular_df, wide_df
 
 def clear_dataframes():
     regular_empty_df = pd.DataFrame([], columns=pd.Index([str(i) for i in range(5)]))
     wide_empty_df = pd.DataFrame([], columns=pd.Index([f"col_{i}" for i in range(15)]))
-    tall_empty_df = pd.DataFrame([], columns=pd.Index(["A", "B", "C"]))
-    return regular_empty_df, wide_empty_df, tall_empty_df
+    return regular_empty_df, wide_empty_df
 
 def increment_select_counter(evt: gr.SelectData, count):
     count_val = 1 if count is None else count + 1
@@ -56,11 +54,37 @@ with gr.Blocks() as demo:
                 show_fullscreen_button=True,
             )
 
-    with gr.Row():
-        initial_tall_df = pd.DataFrame(np.zeros((50, 3), dtype=int), columns=pd.Index(["A", "B", "C"]))
+    tall_df_value = [
+        ["DeepSeek Coder", 79.3],
+        ["Llama 3.3", 68.9],
+        ["Qwen 2.5", 61.9],
+        ["Gemma 2", 59.5],
+        ["GPT 2", 18.3],
+    ]
+
+    def get_display_value(values):
+        display_values = []
+        medals = ["🥇", "🥈", "🥉"]
+        for i, row in enumerate(values):
+            if i < 3:
+                display_values.append([f"{medals[i]} {row[0]}", row[1]])
+            else:
+                display_values.append([row[0], row[1]])
+        return display_values
+
+    display_value = get_display_value(tall_df_value)
 
+    tall_df_value = {
+        "data": tall_df_value,
+        "headers": ["Model", "% Correct (LeetCode Hard)"],
+        "metadata": {
+            "display_value": display_value
+        }
+    }
+
+    with gr.Row():
         df_tall = gr.Dataframe(
-            value=initial_tall_df,
+            value=tall_df_value,
             interactive=False,
             label="Tall Dataframe (Scroll Vertically)",
             show_label=True,
@@ -92,7 +116,7 @@ with gr.Blocks() as demo:
             label="Selected cell value", elem_id="selected_cell_value"
         )
 
-    update_btn.click(fn=update_dataframe, outputs=[df, df_view, df_tall])
+    update_btn.click(fn=update_dataframe, outputs=[df, df_view])
     clear_btn.click(fn=clear_dataframes, outputs=[df, df_view, df_tall])
     df.change(fn=lambda x: x + 1, inputs=[change_events], outputs=[change_events])
     df.input(fn=lambda x: x + 1, inputs=[input_events], outputs=[input_events])