Upload folder using huggingface_hub

README.md

@@ -5,7 +5,7 @@ emoji: 🔥
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 3.40.1
+sdk_version: 3.41.2
 app_file: run.py
 pinned: false
 hf_oauth: true

requirements.txt

@@ -1,2 +1,2 @@
-https://gradio-main-build.s3.amazonaws.com/2e720c0c47ea94923ab689f928308157f2c30b2d/gradio-3.40.1-py3-none-any.whl
+https://gradio-main-build.s3.amazonaws.com/d76d50112328711b1a692b80c1e88a085b15b301/gradio-3.41.2-py3-none-any.whl
 vega_datasets

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: scatterplot_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio vega_datasets"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from vega_datasets import data\n", "\n", "cars = data.cars()\n", "\n", "with gr.Blocks() as demo:\n", "    gr.ScatterPlot(show_label=False,\n", "                   value=cars,\n", "                   x=\"Horsepower\",\n", "                   y=\"Miles_per_Gallon\",\n", "                   color=\"Origin\",\n", "                   tooltip=\"Name\",\n", "                   title=\"Car Data\",\n", "                   y_title=\"Miles per Gallon\",\n", "                   color_legend_title=\"Origin of Car\").style(container=False)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: scatterplot_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio vega_datasets"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from vega_datasets import data\n", "\n", "cars = data.cars()\n", "\n", "with gr.Blocks() as demo:\n", "    gr.ScatterPlot(\n", "        value=cars,\n", "        x=\"Horsepower\",\n", "        y=\"Miles_per_Gallon\",\n", "        color=\"Origin\",\n", "        tooltip=\"Name\",\n", "        title=\"Car Data\",\n", "        y_title=\"Miles per Gallon\",\n", "        color_legend_title=\"Origin of Car\",\n", "        container=False,\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -4,15 +4,17 @@ from vega_datasets import data
 cars = data.cars()
 
 with gr.Blocks() as demo:
-    gr.ScatterPlot(show_label=False,
-                   value=cars,
-                   x="Horsepower",
-                   y="Miles_per_Gallon",
-                   color="Origin",
-                   tooltip="Name",
-                   title="Car Data",
-                   y_title="Miles per Gallon",
-                   color_legend_title="Origin of Car").style(container=False)
+    gr.ScatterPlot(
+        value=cars,
+        x="Horsepower",
+        y="Miles_per_Gallon",
+        color="Origin",
+        tooltip="Name",
+        title="Car Data",
+        y_title="Miles per Gallon",
+        color_legend_title="Origin of Car",
+        container=False,
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()