Upload folder using huggingface_hub

README.md

@@ -1,12 +1,12 @@
+
 ---
-title: Dataframe Custom Styling
-emoji: 🏆
-colorFrom: blue
-colorTo: red
+title: dataframe_custom_styling 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
-sdk_version: 5.18.0
-app_file: app.py
+sdk_version: 5.19.0
+app_file: run.py
 pinned: false
+hf_oauth: true
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

run.ipynb

@@ -0,0 +1 @@
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dataframe_custom_styling"]}, {"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", "\n", "data = [\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", "headers = [\"Model\", \"% Correct (LeetCode Hard)\"]\n", "\n", "def get_styling(values):\n", "    return [[\"\", f\"background: linear-gradient(90deg, rgba(220, 242, 220) {row[1]}%, transparent {row[1]}%)\"] for row in values]\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", "styling = get_styling(data)\n", "display_value = get_display_value(data)\n", "\n", "\n", "value = {\n", "    \"data\": data,\n", "    \"headers\": headers,\n", "    \"metadata\": {\n", "        \"styling\": styling,\n", "        \"display_value\": display_value,\n", "    },\n", "}\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Dataframe(value, show_search=\"search\")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -0,0 +1,43 @@
+import gradio as gr
+
+data = [
+    ["DeepSeek Coder", 79.3],
+    ["Llama 3.3", 68.9],
+    ["Qwen 2.5", 61.9],
+    ["Gemma 2", 59.5],
+    ["GPT 2", 18.3],
+]
+
+headers = ["Model", "% Correct (LeetCode Hard)"]
+
+def get_styling(values):
+    return [["", f"background: linear-gradient(90deg, rgba(220, 242, 220) {row[1]}%, transparent {row[1]}%)"] for row in values]
+
+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
+
+styling = get_styling(data)
+display_value = get_display_value(data)
+
+
+value = {
+    "data": data,
+    "headers": headers,
+    "metadata": {
+        "styling": styling,
+        "display_value": display_value,
+    },
+}
+
+with gr.Blocks() as demo:
+    gr.Dataframe(value, show_search="search")
+
+if __name__ == "__main__":
+    demo.launch()