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run.ipynb
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{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: line_plot"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio vega_datasets pandas"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from vega_datasets import data\n", "\n", "stocks = data.stocks()\n", "gapminder = data.gapminder()\n", "gapminder = gapminder.loc[\n", " gapminder.country.isin([\"Argentina\", \"Australia\", \"Afghanistan\"])\n", "]\n", "climate = data.climate()\n", "seattle_weather = data.seattle_weather()\n", "\n", "## Or generate your own fake data, here's an example for stocks:\n", "#\n", "# import pandas as pd\n", "# import random\n", "#\n", "# stocks = pd.DataFrame(\n", "# {\n", "# \"symbol\": [\n", "# random.choice(\n", "# [\n", "# \"MSFT\",\n", "# \"AAPL\",\n", "# \"AMZN\",\n", "# \"IBM\",\n", "# \"GOOG\",\n", "# ]\n", "# )\n", "# for _ in range(120)\n", "# ],\n", "# \"date\": [\n", "# pd.Timestamp(year=2000 + i, month=j, day=1)\n", "# for i in range(10)\n", "# for j in range(1, 13)\n", "# ],\n", "# \"price\": [random.randint(10, 200) for _ in range(120)],\n", "# }\n", "# )\n", "\n", "
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{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: line_plot"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio vega_datasets pandas"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from vega_datasets import data\n", "\n", "stocks = data.stocks()\n", "gapminder = data.gapminder()\n", "gapminder = gapminder.loc[\n", " gapminder.country.isin([\"Argentina\", \"Australia\", \"Afghanistan\"])\n", "]\n", "climate = data.climate()\n", "seattle_weather = data.seattle_weather()\n", "\n", "## Or generate your own fake data, here's an example for stocks:\n", "#\n", "# import pandas as pd\n", "# import random\n", "#\n", "# stocks = pd.DataFrame(\n", "# {\n", "# \"symbol\": [\n", "# random.choice(\n", "# [\n", "# \"MSFT\",\n", "# \"AAPL\",\n", "# \"AMZN\",\n", "# \"IBM\",\n", "# \"GOOG\",\n", "# ]\n", "# )\n", "# for _ in range(120)\n", "# ],\n", "# \"date\": [\n", "# pd.Timestamp(year=2000 + i, month=j, day=1)\n", "# for i in range(10)\n", "# for j in range(1, 13)\n", "# ],\n", "# \"price\": [random.randint(10, 200) for _ in range(120)],\n", "# }\n", "# )\n", "\n", "def line_plot_fn(dataset):\n", " if dataset == \"stocks\":\n", " return gr.LinePlot(\n", " stocks,\n", " x=\"date\",\n", " y=\"price\",\n", " color=\"symbol\",\n", " color_legend_position=\"bottom\",\n", " title=\"Stock Prices\",\n", " tooltip=[\"date\", \"price\", \"symbol\"],\n", " height=300,\n", " width=500,\n", " )\n", " elif dataset == \"climate\":\n", " return gr.LinePlot(\n", " climate,\n", " x=\"DATE\",\n", " y=\"HLY-TEMP-NORMAL\",\n", " y_lim=[250, 500],\n", " title=\"Climate\",\n", " tooltip=[\"DATE\", \"HLY-TEMP-NORMAL\"],\n", " height=300,\n", " width=500,\n", " )\n", " elif dataset == \"seattle_weather\":\n", " return gr.LinePlot(\n", " seattle_weather,\n", " x=\"date\",\n", " y=\"temp_min\",\n", " tooltip=[\"weather\", \"date\"],\n", " overlay_point=True,\n", " title=\"Seattle Weather\",\n", " height=300,\n", " width=500,\n", " )\n", " elif dataset == \"gapminder\":\n", " return gr.LinePlot(\n", " gapminder,\n", " x=\"year\",\n", " y=\"life_expect\",\n", " color=\"country\",\n", " title=\"Life expectancy for countries\",\n", " stroke_dash=\"cluster\",\n", " x_lim=[1950, 2010],\n", " tooltip=[\"country\", \"life_expect\"],\n", " stroke_dash_legend_title=\"Country Cluster\",\n", " height=300,\n", " width=500,\n", " )\n", "\n", "with gr.Blocks() as line_plot:\n", " with gr.Row():\n", " with gr.Column():\n", " dataset = gr.Dropdown(\n", " choices=[\"stocks\", \"climate\", \"seattle_weather\", \"gapminder\"],\n", " value=\"stocks\",\n", " )\n", " with gr.Column():\n", " plot = gr.LinePlot()\n", " dataset.change(line_plot_fn, inputs=dataset, outputs=plot)\n", " line_plot.load(fn=line_plot_fn, inputs=dataset, outputs=plot)\n", "\n", "if __name__ == \"__main__\":\n", " line_plot.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
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run.py
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# }
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# )
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def line_plot_fn(dataset):
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if dataset == "stocks":
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return gr.LinePlot(
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width=500,
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)
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with gr.Blocks() as line_plot:
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with gr.Row():
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with gr.Column():
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dataset.change(line_plot_fn, inputs=dataset, outputs=plot)
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line_plot.load(fn=line_plot_fn, inputs=dataset, outputs=plot)
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if __name__ == "__main__":
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line_plot.launch()
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# }
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# )
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def line_plot_fn(dataset):
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if dataset == "stocks":
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return gr.LinePlot(
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width=500,
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)
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with gr.Blocks() as line_plot:
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with gr.Row():
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with gr.Column():
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dataset.change(line_plot_fn, inputs=dataset, outputs=plot)
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line_plot.load(fn=line_plot_fn, inputs=dataset, outputs=plot)
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if __name__ == "__main__":
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line_plot.launch()
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