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README.md

@@ -1,12 +1,11 @@
+
 ---
-title: Scatter Plot Main
-emoji: 🐠
-colorFrom: red
-colorTo: yellow
+title: scatter_plot_main 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
 sdk_version: 3.35.2
-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
+https://gradio-main-build.s3.amazonaws.com/44c766bd1a19e6c9195aed298ee6423c5e0116e3/gradio-3.35.2-py3-none-any.whl

run.ipynb

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+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: scatter_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", "cars = data.cars()\n", "iris = data.iris()\n", "\n", "# # Or generate your own fake data\n", "\n", "# import pandas as pd\n", "# import random\n", "\n", "# cars_data = {\n", "#     \"Name\": [\"car name \" + f\" {int(i/10)}\" for i in range(400)],\n", "#     \"Miles_per_Gallon\": [random.randint(10, 30) for _ in range(400)],\n", "#     \"Origin\": [random.choice([\"USA\", \"Europe\", \"Japan\"]) for _ in range(400)],\n", "#     \"Horsepower\": [random.randint(50, 250) for _ in range(400)],\n", "# }\n", "\n", "# iris_data = {\n", "#     \"petalWidth\": [round(random.uniform(0, 2.5), 2) for _ in range(150)],\n", "#     \"petalLength\": [round(random.uniform(0, 7), 2) for _ in range(150)],\n", "#     \"species\": [\n", "#         random.choice([\"setosa\", \"versicolor\", \"virginica\"]) for _ in range(150)\n", "#     ],\n", "# }\n", "\n", "# cars = pd.DataFrame(cars_data)\n", "# iris = pd.DataFrame(iris_data)\n", "\n", "\n", "def scatter_plot_fn(dataset):\n", "    if dataset == \"iris\":\n", "        return gr.ScatterPlot.update(\n", "            value=iris,\n", "            x=\"petalWidth\",\n", "            y=\"petalLength\",\n", "            color=\"species\",\n", "            title=\"Iris Dataset\",\n", "            color_legend_title=\"Species\",\n", "            x_title=\"Petal Width\",\n", "            y_title=\"Petal Length\",\n", "            tooltip=[\"petalWidth\", \"petalLength\", \"species\"],\n", "            caption=\"\",\n", "        )\n", "    else:\n", "        return gr.ScatterPlot.update(\n", "            value=cars,\n", "            x=\"Horsepower\",\n", "            y=\"Miles_per_Gallon\",\n", "            color=\"Origin\",\n", "            tooltip=\"Name\",\n", "            title=\"Car Data\",\n", "            y_title=\"Miles per Gallon\",\n", "            color_legend_title=\"Origin of Car\",\n", "            caption=\"MPG vs Horsepower of various cars\",\n", "        )\n", "\n", "\n", "with gr.Blocks() as scatter_plot:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            dataset = gr.Dropdown(choices=[\"cars\", \"iris\"], value=\"cars\")\n", "        with gr.Column():\n", "            plot = gr.ScatterPlot()\n", "    dataset.change(scatter_plot_fn, inputs=dataset, outputs=plot)\n", "    scatter_plot.load(fn=scatter_plot_fn, inputs=dataset, outputs=plot)\n", "\n", "if __name__ == \"__main__\":\n", "    scatter_plot.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

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+import gradio as gr
+from vega_datasets import data
+
+cars = data.cars()
+iris = data.iris()
+
+# # Or generate your own fake data
+
+# import pandas as pd
+# import random
+
+# cars_data = {
+#     "Name": ["car name " + f" {int(i/10)}" for i in range(400)],
+#     "Miles_per_Gallon": [random.randint(10, 30) for _ in range(400)],
+#     "Origin": [random.choice(["USA", "Europe", "Japan"]) for _ in range(400)],
+#     "Horsepower": [random.randint(50, 250) for _ in range(400)],
+# }
+
+# iris_data = {
+#     "petalWidth": [round(random.uniform(0, 2.5), 2) for _ in range(150)],
+#     "petalLength": [round(random.uniform(0, 7), 2) for _ in range(150)],
+#     "species": [
+#         random.choice(["setosa", "versicolor", "virginica"]) for _ in range(150)
+#     ],
+# }
+
+# cars = pd.DataFrame(cars_data)
+# iris = pd.DataFrame(iris_data)
+
+
+def scatter_plot_fn(dataset):
+    if dataset == "iris":
+        return gr.ScatterPlot.update(
+            value=iris,
+            x="petalWidth",
+            y="petalLength",
+            color="species",
+            title="Iris Dataset",
+            color_legend_title="Species",
+            x_title="Petal Width",
+            y_title="Petal Length",
+            tooltip=["petalWidth", "petalLength", "species"],
+            caption="",
+        )
+    else:
+        return gr.ScatterPlot.update(
+            value=cars,
+            x="Horsepower",
+            y="Miles_per_Gallon",
+            color="Origin",
+            tooltip="Name",
+            title="Car Data",
+            y_title="Miles per Gallon",
+            color_legend_title="Origin of Car",
+            caption="MPG vs Horsepower of various cars",
+        )
+
+
+with gr.Blocks() as scatter_plot:
+    with gr.Row():
+        with gr.Column():
+            dataset = gr.Dropdown(choices=["cars", "iris"], value="cars")
+        with gr.Column():
+            plot = gr.ScatterPlot()
+    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()