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README.md

@@ -1,12 +1,11 @@
+
 ---
-title: Line Plot
-emoji: 😻
-colorFrom: red
-colorTo: red
+title: line_plot 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
 sdk_version: 3.36.0
-app_file: app.py
+app_file: run.py
 pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

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+vega_datasets
+pandas

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", "\n", "def line_plot_fn(dataset):\n", "    if dataset == \"stocks\":\n", "        return gr.LinePlot.update(\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.update(\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.update(\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.update(\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", "\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", "\n", "if __name__ == \"__main__\":\n", "    line_plot.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

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+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()
+
+## Or generate your own fake data, here's an example for stocks:
+#
+# import pandas as pd
+# import random
+#
+# stocks = pd.DataFrame(
+#     {
+#         "symbol": [
+#             random.choice(
+#                 [
+#                     "MSFT",
+#                     "AAPL",
+#                     "AMZN",
+#                     "IBM",
+#                     "GOOG",
+#                 ]
+#             )
+#             for _ in range(120)
+#         ],
+#         "date": [
+#             pd.Timestamp(year=2000 + i, month=j, day=1)
+#             for i in range(10)
+#             for j in range(1, 13)
+#         ],
+#         "price": [random.randint(10, 200) for _ in range(120)],
+#     }
+# )
+
+
+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()
+    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()