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bar_plot_demo.py

@@ -1,5 +1,4 @@
 import gradio as gr
-import numpy as np
 from data import temp_sensor_data, food_rating_data
 
 with gr.Blocks() as bar_plots:
@@ -25,17 +24,16 @@ with gr.Blocks() as bar_plots:
 
     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, 
+        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, 
+        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])
@@ -72,6 +70,5 @@ with gr.Blocks() as bar_plots:
             color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"},
         )
 
-
 if __name__ == "__main__":
     bar_plots.launch()

data.py

@@ -1,5 +1,5 @@
 import pandas as pd
-from random import randint, choice, random
+from random import randint, random
 
 temp_sensor_data = pd.DataFrame(
     {
@@ -17,4 +17,4 @@ food_rating_data = pd.DataFrame(
         "price": [randint(10, 50) + 4 * (i % 3) for i in range(100)],
         "wait": [random() for i in range(100)],
     }
-)
+)

line_plot_demo.py

@@ -1,5 +1,4 @@
 import gradio as gr
-import numpy as np
 from data import temp_sensor_data, food_rating_data
 
 with gr.Blocks() as line_plots:
@@ -25,17 +24,16 @@ with gr.Blocks() as line_plots:
 
     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, 
+        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, 
+        lambda aggregate: [gr.LinePlot(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])
@@ -64,6 +62,5 @@ with gr.Blocks() as line_plots:
             color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"},
         )
 
-
 if __name__ == "__main__":
     line_plots.launch()

requirements.txt

@@ -1,3 +1,3 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@de997e67c9a7feb9e2eccebf92969366dbd67eba#subdirectory=client/python
-https://gradio-builds.s3.amazonaws.com/de997e67c9a7feb9e2eccebf92969366dbd67eba/gradio-4.39.0-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@9b42ba8f1006c05d60a62450d3036ce0d6784f86#subdirectory=client/python
+https://gradio-builds.s3.amazonaws.com/9b42ba8f1006c05d60a62450d3036ce0d6784f86/gradio-4.39.0-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/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}
+{"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", "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

@@ -4,7 +4,6 @@ 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("Line Plot"):

scatter_plot_demo.py

@@ -1,5 +1,4 @@
 import gradio as gr
-import numpy as np
 from data import temp_sensor_data, food_rating_data
 
 with gr.Blocks() as scatter_plots:
@@ -25,17 +24,16 @@ with gr.Blocks() as scatter_plots:
 
     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, 
+        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, 
+        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])
@@ -66,6 +64,5 @@ with gr.Blocks() as scatter_plots:
             # color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"},
         )
 
-
 if __name__ == "__main__":
     scatter_plots.launch()