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bar_plot_demo.py

@@ -1,111 +1,77 @@
 import gradio as gr
-import pandas as pd
+import numpy as np
+from data import temp_sensor_data, food_rating_data
 
-from vega_datasets import data
+with gr.Blocks() as bar_plots:
+    with gr.Row():
+        start = gr.DateTime("2021-01-01 00:00:00", label="Start")
+        end = gr.DateTime("2021-01-05 00:00:00", label="End")
+        apply_btn = gr.Button("Apply", scale=0)
+    with gr.Row():
+        group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by")
+        aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation")
 
-barley = data.barley()
-simple = pd.DataFrame({
-    'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
-    'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]
-})
+    temp_by_time = gr.BarPlot(
+        temp_sensor_data,
+        x="time",
+        y="temperature",
+    )
+    temp_by_time_location = gr.BarPlot(
+        temp_sensor_data,
+        x="time",
+        y="temperature",
+        color="location",
+    )
 
-def bar_plot_fn(display):
-    if display == "simple":
-        return gr.BarPlot(
-            simple,
-            x="a",
-            y="b",
-            color=None,
-            group=None,
-            title="Simple Bar Plot with made up data",
-            tooltip=['a', 'b'],
-            y_lim=[20, 100],
-            x_title=None,
-            y_title=None,
-            vertical=True,
-        )
-    elif display == "stacked":
-        return gr.BarPlot(
-            barley,
-            x="variety",
-            y="yield",
-            color="site",
-            group=None,
-            title="Barley Yield Data",
-            tooltip=['variety', 'site'],
-            y_lim=None,
-            x_title=None,
-            y_title=None,
-            vertical=True,
-        )
-    elif display == "grouped":
-        return gr.BarPlot(
-            barley.astype({"year": str}),
-            x="year",
-            y="yield",
-            color="year",
-            group="site",
-            title="Barley Yield by Year and Site",
-            tooltip=["yield", "site", "year"],
-            y_lim=None,
-            x_title=None,
-            y_title=None,
-            vertical=True,
+    time_graphs = [temp_by_time, temp_by_time_location]
+    group_by.change(
+        lambda group: [gr.BarPlot(x_bin=None if group == "None" else group)] * len(time_graphs), 
+        group_by, 
+        time_graphs
+    )
+    aggregate.change(
+        lambda aggregate: [gr.BarPlot(y_aggregate=aggregate)] * len(time_graphs), 
+        aggregate, 
+        time_graphs
+    )
+
+
+    def rescale(select: gr.SelectData):
+        return select.index
+    rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end])
+
+    for trigger in [apply_btn.click, rescale_evt.then]:
+        trigger(
+            lambda start, end: [gr.BarPlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs
         )
-    elif display == "simple-horizontal":
-        return gr.BarPlot(
-            simple,
-            x="a",
-            y="b",
-            color=None,
-            group=None,
-            title="Simple Bar Plot with made up data",
-            tooltip=['a', 'b'],
-            y_lim=[20, 100],
-            x_title="Variable A",
-            y_title="Variable B",
-            vertical=False,
+
+    with gr.Row():
+        price_by_cuisine = gr.BarPlot(
+            food_rating_data,
+            x="cuisine",
+            y="price",
         )
-    elif display == "stacked-horizontal":
-        return gr.BarPlot(
-            barley,
-            x="variety",
-            y="yield",
-            color="site",
-            group=None,
-            title="Barley Yield Data",
-            tooltip=['variety', 'site'],
-            y_lim=None,
-            x_title=None,
-            y_title=None,
-            vertical=False,
+        with gr.Column(scale=0):
+            gr.Button("Sort $ > $$$").click(lambda: gr.BarPlot(sort="y"), None, price_by_cuisine)
+            gr.Button("Sort $$$ > $").click(lambda: gr.BarPlot(sort="-y"), None, price_by_cuisine)
+            gr.Button("Sort A > Z").click(lambda: gr.BarPlot(sort=["Chinese", "Italian", "Mexican"]), None, price_by_cuisine)
+
+    with gr.Row():
+        price_by_rating = gr.BarPlot(
+            food_rating_data,
+            x="rating",
+            y="price",
+            x_bin=1,
         )
-    elif display == "grouped-horizontal":
-        return gr.BarPlot(
-            barley.astype({"year": str}),
-            x="year",
-            y="yield",
-            color="year",
-            group="site",
-            title="Barley Yield by Year and Site",
-            group_title="",
-            tooltip=["yield", "site", "year"],
-            y_lim=None,
-            x_title=None,
-            y_title=None,
-            vertical=False
+        price_by_rating_color = gr.BarPlot(
+            food_rating_data,
+            x="rating",
+            y="price",
+            color="cuisine",
+            x_bin=1,
+            color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"},
         )
 
 
-with gr.Blocks() as bar_plot:
-    display = gr.Dropdown(
-        choices=["simple", "stacked", "grouped", "simple-horizontal", "stacked-horizontal", "grouped-horizontal"],
-        value="simple",
-        label="Type of Bar Plot"
-    )
-    plot = gr.BarPlot(show_label=False)
-    display.change(bar_plot_fn, inputs=display, outputs=plot)
-    bar_plot.load(fn=bar_plot_fn, inputs=display, outputs=plot)
-
 if __name__ == "__main__":
-    bar_plot.launch()
+    bar_plots.launch()

data.py

@@ -0,0 +1,20 @@
+import pandas as pd
+from random import randint, choice, random
+
+temp_sensor_data = pd.DataFrame(
+    {
+        "time": pd.date_range("2021-01-01", end="2021-01-05", periods=200),
+        "temperature": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],
+        "humidity": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],
+        "location": ["indoor", "outdoor"] * 100,
+    }
+)
+
+food_rating_data = pd.DataFrame(
+    {
+        "cuisine": [["Italian", "Mexican", "Chinese"][i % 3] for i in range(100)],
+        "rating": [random() * 4 + 0.5 * (i % 3) for i in range(100)],
+        "price": [randint(10, 50) + 4 * (i % 3) for i in range(100)],
+        "wait": [random() for i in range(100)],
+    }
+)

line_plot_demo.py

@@ -1,82 +1,69 @@
 import gradio as gr
-from vega_datasets import data
+import numpy as np
+from data import temp_sensor_data, food_rating_data
 
-stocks = data.stocks()
-gapminder = data.gapminder()
-gapminder = gapminder.loc[
-    gapminder.country.isin(["Argentina", "Australia", "Afghanistan"])
-]
-climate = data.climate()
-seattle_weather = data.seattle_weather()
+with gr.Blocks() as line_plots:
+    with gr.Row():
+        start = gr.DateTime("2021-01-01 00:00:00", label="Start")
+        end = gr.DateTime("2021-01-05 00:00:00", label="End")
+        apply_btn = gr.Button("Apply", scale=0)
+    with gr.Row():
+        group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by")
+        aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation")
 
+    temp_by_time = gr.LinePlot(
+        temp_sensor_data,
+        x="time",
+        y="temperature",
+    )
+    temp_by_time_location = gr.LinePlot(
+        temp_sensor_data,
+        x="time",
+        y="temperature",
+        color="location",
+    )
+
+    time_graphs = [temp_by_time, temp_by_time_location]
+    group_by.change(
+        lambda group: [gr.LinePlot(x_bin=None if group == "None" else group)] * len(time_graphs), 
+        group_by, 
+        time_graphs
+    )
+    aggregate.change(
+        lambda aggregate: [gr.LinePlot(y_aggregate=aggregate)] * len(time_graphs), 
+        aggregate, 
+        time_graphs
+    )
 
-def line_plot_fn(dataset):
-    if dataset == "stocks":
-        return gr.LinePlot(
-            stocks,
-            x="date",
-            y="price",
-            color="symbol",
-            x_lim=None,
-            y_lim=None,
-            stroke_dash=None,
-            tooltip=['date', 'price', 'symbol'],
-            overlay_point=False,
-            title="Stock Prices",
-            stroke_dash_legend_title=None,
-        )
-    elif dataset == "climate":
-        return gr.LinePlot(
-            climate,
-            x="DATE",
-            y="HLY-TEMP-NORMAL",
-            color=None,
-            x_lim=None,
-            y_lim=[250, 500],
-            stroke_dash=None,
-            tooltip=['DATE', 'HLY-TEMP-NORMAL'],
-            overlay_point=False,
-            title="Climate",
-            stroke_dash_legend_title=None,
-        )
-    elif dataset == "seattle_weather":
-        return gr.LinePlot(
-            seattle_weather,
-            x="date",
-            y="temp_min",
-            color=None,
-            x_lim=None,
-            y_lim=None,
-            stroke_dash=None,
-            tooltip=["weather", "date"],
-            overlay_point=True,
-            title="Seattle Weather",
-            stroke_dash_legend_title=None,
-        )
-    elif dataset == "gapminder":
-        return gr.LinePlot(
-            gapminder,
-            x="year",
-            y="life_expect",
-            color="country",
-            x_lim=[1950, 2010],
-            y_lim=None,
-            stroke_dash="cluster",
-            tooltip=['country', 'life_expect'],
-            overlay_point=False,
-            title="Life expectancy for countries",
-        )
 
+    def rescale(select: gr.SelectData):
+        return select.index
+    rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end])
+
+    for trigger in [apply_btn.click, rescale_evt.then]:
+        trigger(
+            lambda start, end: [gr.LinePlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs
+        )
 
-with gr.Blocks() as line_plot:
-    dataset = gr.Dropdown(
-        choices=["stocks", "climate", "seattle_weather", "gapminder"],
-        value="stocks",
+    price_by_cuisine = gr.LinePlot(
+        food_rating_data,
+        x="cuisine",
+        y="price",
     )
-    plot = gr.LinePlot()
-    dataset.change(line_plot_fn, inputs=dataset, outputs=plot)
-    line_plot.load(fn=line_plot_fn, inputs=dataset, outputs=plot)
+    with gr.Row():
+        price_by_rating = gr.LinePlot(
+            food_rating_data,
+            x="rating",
+            y="price",
+        )
+        price_by_rating_color = gr.LinePlot(
+            food_rating_data,
+            x="rating",
+            y="price",
+            color="cuisine",
+            color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"},
+        )
 
 
 if __name__ == "__main__":
-    line_plot.launch()
+    line_plots.launch()

requirements.txt

@@ -1,3 +1,3 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@38c2ad425a905431b1eb17b9498669f9e49f0dd5#subdirectory=client/python
-https://gradio-builds.s3.amazonaws.com/38c2ad425a905431b1eb17b9498669f9e49f0dd5/gradio-4.38.1-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@76c175935019833baef709a5cf401d2263ca72ee#subdirectory=client/python
+https://gradio-builds.s3.amazonaws.com/76c175935019833baef709a5cf401d2263ca72ee/gradio-4.38.1-py3-none-any.whl
 vega_datasets

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/bar_plot_demo.py\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", "from bar_plot_demo import bar_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", "        with gr.TabItem(\"Bar Plot\"):\n", "            bar_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/bar_plot_demo.py\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/native_plots/data.py\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_plots\n", "from line_plot_demo import line_plots\n", "from bar_plot_demo import bar_plots\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Tabs():\n", "        with gr.TabItem(\"Line Plot\"):\n", "            line_plots.render()\n", "        with gr.TabItem(\"Scatter Plot\"):\n", "            scatter_plots.render()\n", "        with gr.TabItem(\"Bar Plot\"):\n", "            bar_plots.render()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -1,18 +1,18 @@
 import gradio as gr
 
-from scatter_plot_demo import scatter_plot
-from line_plot_demo import line_plot
-from bar_plot_demo import bar_plot
+from scatter_plot_demo import scatter_plots
+from line_plot_demo import line_plots
+from bar_plot_demo import bar_plots
 
 
 with gr.Blocks() as demo:
     with gr.Tabs():
-        with gr.TabItem("Scatter Plot"):
-            scatter_plot.render()
         with gr.TabItem("Line Plot"):
-            line_plot.render()
+            line_plots.render()
+        with gr.TabItem("Scatter Plot"):
+            scatter_plots.render()
         with gr.TabItem("Bar Plot"):
-            bar_plot.render()
+            bar_plots.render()
 
 if __name__ == "__main__":
     demo.launch()

scatter_plot_demo.py

@@ -1,46 +1,71 @@
 import gradio as gr
+import numpy as np
+from data import temp_sensor_data, food_rating_data
 
-from vega_datasets import data
-
-cars = data.cars()
-iris = data.iris()
-
-
-def scatter_plot_fn(dataset):
-    if dataset == "iris":
-        return gr.ScatterPlot(
-            value=iris,
-            x="petalWidth",
-            y="petalLength",
-            color=None,
-            title="Iris Dataset",
-            x_title="Petal Width",
-            y_title="Petal Length",
-            tooltip=["petalWidth", "petalLength", "species"],
-            caption="",
-            height=600,
-            width=600,
+with gr.Blocks() as scatter_plots:
+    with gr.Row():
+        start = gr.DateTime("2021-01-01 00:00:00", label="Start")
+        end = gr.DateTime("2021-01-05 00:00:00", label="End")
+        apply_btn = gr.Button("Apply", scale=0)
+    with gr.Row():
+        group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by")
+        aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation")
+
+    temp_by_time = gr.ScatterPlot(
+        temp_sensor_data,
+        x="time",
+        y="temperature",
+    )
+    temp_by_time_location = gr.ScatterPlot(
+        temp_sensor_data,
+        x="time",
+        y="temperature",
+        color="location",
+    )
+
+    time_graphs = [temp_by_time, temp_by_time_location]
+    group_by.change(
+        lambda group: [gr.ScatterPlot(x_bin=None if group == "None" else group)] * len(time_graphs), 
+        group_by, 
+        time_graphs
+    )
+    aggregate.change(
+        lambda aggregate: [gr.ScatterPlot(y_aggregate=aggregate)] * len(time_graphs), 
+        aggregate, 
+        time_graphs
+    )
+
+
+    # def rescale(select: gr.SelectData):
+    #     return select.index
+    # rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end])
+
+    # for trigger in [apply_btn.click, rescale_evt.then]:
+    #     trigger(
+    #         lambda start, end: [gr.ScatterPlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs
+    #     )
+
+    price_by_cuisine = gr.ScatterPlot(
+        food_rating_data,
+        x="cuisine",
+        y="price",
+    )
+    with gr.Row():
+        price_by_rating = gr.ScatterPlot(
+            food_rating_data,
+            x="rating",
+            y="price",
+            color="wait",
+            show_actions_button=True,
         )
-    else:
-        return gr.ScatterPlot(
-            value=cars,
-            x="Horsepower",
-            y="Miles_per_Gallon",
-            color="Origin",
-            tooltip="Name",
-            title="Car Data",
-            y_title="Miles per Gallon",
-            caption="MPG vs Horsepower of various cars",
-            height=None,
-            width=None,
+        price_by_rating_color = gr.ScatterPlot(
+            food_rating_data,
+            x="rating",
+            y="price",
+            color="cuisine",
+            # color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"},
         )
 
 
-with gr.Blocks() as scatter_plot:
-    dataset = gr.Dropdown(choices=["cars", "iris"], value="cars")
-    plot = gr.ScatterPlot(show_label=False)
-    dataset.change(scatter_plot_fn, inputs=dataset, outputs=plot)
-    scatter_plot.load(fn=scatter_plot_fn, inputs=dataset, outputs=plot)
-
 if __name__ == "__main__":
-    scatter_plot.launch()
+    scatter_plots.launch()