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line_plot_demo.py

@@ -0,0 +1,78 @@
+import gradio as gr
+from vega_datasets import data
+
+stocks = data.stocks()
+gapminder = data.gapminder()
+gapminder = gapminder.loc[
+    gapminder.country.isin(["Argentina", "Australia", "Afghanistan"])
+]
+climate = data.climate()
+seattle_weather = data.seattle_weather()
+
+
+def line_plot_fn(dataset):
+    if dataset == "stocks":
+        return gr.LinePlot.update(
+            stocks,
+            x="date",
+            y="price",
+            color="symbol",
+            color_legend_position="bottom",
+            title="Stock Prices",
+            tooltip=['date', 'price', 'symbol'],
+            height=300,
+            width=500
+        )
+    elif dataset == "climate":
+        return gr.LinePlot.update(
+            climate,
+            x="DATE",
+            y="HLY-TEMP-NORMAL",
+            y_lim=[250, 500],
+            title="Climate",
+            tooltip=['DATE', 'HLY-TEMP-NORMAL'],
+            height=300,
+            width=500
+        )
+    elif dataset == "seattle_weather":
+        return gr.LinePlot.update(
+            seattle_weather,
+            x="date",
+            y="temp_min",
+            tooltip=["weather", "date"],
+            overlay_point=True,
+            title="Seattle Weather",
+            height=300,
+            width=500
+        )
+    elif dataset == "gapminder":
+        return gr.LinePlot.update(
+            gapminder,
+            x="year",
+            y="life_expect",
+            color="country",
+            title="Life expectancy for countries",
+            stroke_dash="cluster",
+            x_lim=[1950, 2010],
+            tooltip=['country', 'life_expect'],
+            stroke_dash_legend_title="Country Cluster",
+            height=300,
+            width=500
+        )
+
+
+with gr.Blocks() as line_plot:
+    with gr.Row():
+        with gr.Column():
+            dataset = gr.Dropdown(
+                choices=["stocks", "climate", "seattle_weather", "gapminder"],
+                value="stocks",
+            )
+        with gr.Column():
+            plot = gr.LinePlot(show_label=False).style(container=False)
+    dataset.change(line_plot_fn, inputs=dataset, outputs=plot)
+    line_plot.load(fn=line_plot_fn, inputs=dataset, outputs=plot)
+
+
+if __name__ == "__main__":
+    line_plot.launch()

requirements.txt

@@ -1,2 +1,2 @@
 vega_datasets
-https://gradio-main-build.s3.amazonaws.com/3cb2bb061c8fa42ea1c13d73438dbff132f42983/gradio-3.14.0-py3-none-any.whl
+https://gradio-main-build.s3.amazonaws.com/96297c0bad09ee82e65d56a53f96ee9814bb8360/gradio-3.14.0-py3-none-any.whl

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: native_plots"]}, {"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": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/native_plots/scatter_plot_demo.py"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "from scatter_plot_demo import scatter_plot\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Tabs():\n", "        with gr.TabItem(\"Scatter Plot\"):\n", "            scatter_plot.render()\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: native_plots"]}, {"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": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/native_plots/line_plot_demo.py\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/native_plots/scatter_plot_demo.py"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "from scatter_plot_demo import scatter_plot\n", "from line_plot_demo import line_plot\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Tabs():\n", "        with gr.TabItem(\"Scatter Plot\"):\n", "            scatter_plot.render()\n", "        with gr.TabItem(\"Line Plot\"):\n", "            line_plot.render()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -1,12 +1,15 @@
 import gradio as gr
 
 from scatter_plot_demo import scatter_plot
+from line_plot_demo import line_plot
 
 
 with gr.Blocks() as demo:
     with gr.Tabs():
         with gr.TabItem("Scatter Plot"):
             scatter_plot.render()
+        with gr.TabItem("Line Plot"):
+            line_plot.render()
 
 if __name__ == "__main__":
     demo.launch()

scatter_plot_demo.py

@@ -30,7 +30,7 @@ def scatter_plot_fn(dataset):
             title="Car Data",
             y_title="Miles per Gallon",
             color_legend_title="Origin of Car",
-            caption="MPG vs Horsepower of various cars"
+            caption="MPG vs Horsepower of various cars",
         )