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run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: map_airbnb\n", "### Display an interactive map of AirBnB locations with Plotly. Data is hosted on HuggingFace Datasets. \n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# type: ignore\n", "import gradio as gr\n", "import plotly.graph_objects as go\n", "from datasets import load_dataset\n", "\n", "dataset = load_dataset(\"gradio/NYC-Airbnb-Open-Data\", split=\"train\")\n", "df = dataset.to_pandas()\n", "\n", "def filter_map(min_price, max_price, boroughs):\n", "\n", "    filtered_df = df[(df['neighbourhood_group'].isin(boroughs)) & \n", "          (df['price'] > min_price) & (df['price'] < max_price)]\n", "    names = filtered_df[\"name\"].tolist()\n", "    prices = filtered_df[\"price\"].tolist()\n", "    text_list = [(names[i], prices[i]) for i in range(0, len(names))]\n", "    fig = go.Figure(go.Scattermapbox(\n", "            customdata=text_list,\n", "            lat=filtered_df['latitude'].tolist(),\n", "            lon=filtered_df['longitude'].tolist(),\n", "            mode='markers',\n", "            marker=go.scattermapbox.Marker(\n", "                size=6\n", "            ),\n", "            hoverinfo=\"text\",\n", "            hovertemplate='<b>Name</b>: %{customdata[0]}<br><b>Price</b>: $%{customdata[1]}'\n", "        ))\n", "\n", "    fig.update_layout(\n", "        mapbox_style=\"open-street-map\",\n", "        hovermode='closest',\n", "        mapbox=dict(\n", "            bearing=0,\n", "            center=go.layout.mapbox.Center(\n", "                lat=40.67,\n", "                lon=-73.90\n", "            ),\n", "            pitch=0,\n", "            zoom=9\n", "        ),\n", "    )\n", "\n", "    return fig\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Column():\n", "        with gr.Row():\n", "            min_price = gr.Number(value=250, label=\"Minimum Price\")\n", "            max_price = gr.Number(value=1000, label=\"Maximum Price\")\n", "        boroughs = gr.CheckboxGroup(choices=[\"Queens\", \"Brooklyn\", \"Manhattan\", \"Bronx\", \"Staten Island\"], value=[\"Queens\", \"Brooklyn\"], label=\"Select Boroughs:\")\n", "        btn = gr.Button(value=\"Update Filter\")\n", "        map = gr.Plot()\n", "    demo.load(filter_map, [min_price, max_price, boroughs], map)\n", "    btn.click(filter_map, [min_price, max_price, boroughs], map)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: map_airbnb\n", "### Display an interactive map of AirBnB locations with Plotly. Data is hosted on HuggingFace Datasets. \n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# type: ignore\n", "import gradio as gr\n", "import plotly.graph_objects as go\n", "from datasets import load_dataset\n", "\n", "dataset = load_dataset(\"gradio/NYC-Airbnb-Open-Data\", split=\"train\")\n", "df = dataset.to_pandas()\n", "\n", "def filter_map(min_price, max_price, boroughs):\n", "\n", "    filtered_df = df[(df['neighbourhood_group'].isin(boroughs)) &\n", "          (df['price'] > min_price) & (df['price'] < max_price)]\n", "    names = filtered_df[\"name\"].tolist()\n", "    prices = filtered_df[\"price\"].tolist()\n", "    text_list = [(names[i], prices[i]) for i in range(0, len(names))]\n", "    fig = go.Figure(go.Scattermapbox(\n", "            customdata=text_list,\n", "            lat=filtered_df['latitude'].tolist(),\n", "            lon=filtered_df['longitude'].tolist(),\n", "            mode='markers',\n", "            marker=go.scattermapbox.Marker(\n", "                size=6\n", "            ),\n", "            hoverinfo=\"text\",\n", "            hovertemplate='<b>Name</b>: %{customdata[0]}<br><b>Price</b>: $%{customdata[1]}'\n", "        ))\n", "\n", "    fig.update_layout(\n", "        mapbox_style=\"open-street-map\",\n", "        hovermode='closest',\n", "        mapbox=dict(\n", "            bearing=0,\n", "            center=go.layout.mapbox.Center(\n", "                lat=40.67,\n", "                lon=-73.90\n", "            ),\n", "            pitch=0,\n", "            zoom=9\n", "        ),\n", "    )\n", "\n", "    return fig\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Column():\n", "        with gr.Row():\n", "            min_price = gr.Number(value=250, label=\"Minimum Price\")\n", "            max_price = gr.Number(value=1000, label=\"Maximum Price\")\n", "        boroughs = gr.CheckboxGroup(choices=[\"Queens\", \"Brooklyn\", \"Manhattan\", \"Bronx\", \"Staten Island\"], value=[\"Queens\", \"Brooklyn\"], label=\"Select Boroughs:\")\n", "        btn = gr.Button(value=\"Update Filter\")\n", "        map = gr.Plot()\n", "    demo.load(filter_map, [min_price, max_price, boroughs], map)\n", "    btn.click(filter_map, [min_price, max_price, boroughs], map)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -8,7 +8,7 @@ df = dataset.to_pandas()
 
 def filter_map(min_price, max_price, boroughs):
 
-    filtered_df = df[(df['neighbourhood_group'].isin(boroughs)) & 
+    filtered_df = df[(df['neighbourhood_group'].isin(boroughs)) &
           (df['price'] > min_price) & (df['price'] < max_price)]
     names = filtered_df["name"].tolist()
     prices = filtered_df["price"].tolist()
@@ -53,4 +53,4 @@ with gr.Blocks() as demo:
     btn.click(filter_map, [min_price, max_price, boroughs], map)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()