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run.ipynb
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{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chicago-bikeshare-dashboard"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio psycopg2 matplotlib SQLAlchemy "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import os\n", "import gradio as gr\n", "import pandas as pd\n", "\n", "DB_USER = os.getenv(\"DB_USER\")\n", "DB_PASSWORD = os.getenv(\"DB_PASSWORD\")\n", "DB_HOST = os.getenv(\"DB_HOST\")\n", "PORT = 8080\n", "DB_NAME = \"bikeshare\"\n", "\n", "connection_string = (\n", " f\"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}\"\n", ")\n", "\n", "
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{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chicago-bikeshare-dashboard"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio psycopg2 matplotlib SQLAlchemy "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import os\n", "import gradio as gr\n", "import pandas as pd\n", "\n", "DB_USER = os.getenv(\"DB_USER\")\n", "DB_PASSWORD = os.getenv(\"DB_PASSWORD\")\n", "DB_HOST = os.getenv(\"DB_HOST\")\n", "PORT = 8080\n", "DB_NAME = \"bikeshare\"\n", "\n", "connection_string = (\n", " f\"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}\"\n", ")\n", "\n", "def get_count_ride_type():\n", " df = pd.read_sql(\n", " \"\"\"\n", " SELECT COUNT(ride_id) as n, rideable_type\n", " FROM rides\n", " GROUP BY rideable_type\n", " ORDER BY n DESC\n", " \"\"\",\n", " con=connection_string,\n", " )\n", " return df\n", "\n", "def get_most_popular_stations():\n", "\n", " df = pd.read_sql(\n", " \"\"\"\n", " SELECT COUNT(ride_id) as n, MAX(start_station_name) as station\n", " FROM RIDES\n", " WHERE start_station_name is NOT NULL\n", " GROUP BY start_station_id\n", " ORDER BY n DESC\n", " LIMIT 5\n", " \"\"\",\n", " con=connection_string,\n", " )\n", " return df\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\n", " \"\"\"\n", " # Chicago Bike Share Dashboard\n", " \n", " This demo pulls Chicago bike share data for March 2022 from a postgresql database hosted on AWS.\n", " This demo uses psycopg2 but any postgresql client library (SQLAlchemy)\n", " is compatible with gradio.\n", " \n", " Connection credentials are handled by environment variables\n", " defined as secrets in the Space.\n", "\n", " If data were added to the database, the plots in this demo would update\n", " whenever the webpage is reloaded.\n", " \n", " This demo serves as a starting point for your database-connected apps!\n", " \"\"\"\n", " )\n", " with gr.Row():\n", " bike_type = gr.BarPlot(\n", " x=\"rideable_type\",\n", " y='n',\n", " title=\"Number of rides per bicycle type\",\n", " y_title=\"Number of Rides\",\n", " x_title=\"Bicycle Type\",\n", " vertical=False,\n", " tooltip=['rideable_type', \"n\"],\n", " height=300,\n", " width=300,\n", " )\n", " station = gr.BarPlot(\n", " x='station',\n", " y='n',\n", " title=\"Most Popular Stations\",\n", " y_title=\"Number of Rides\",\n", " x_title=\"Station Name\",\n", " vertical=False,\n", " tooltip=['station', 'n'],\n", " height=300,\n", " width=300\n", " )\n", "\n", " demo.load(get_count_ride_type, inputs=None, outputs=bike_type)\n", " demo.load(get_most_popular_stations, inputs=None, outputs=station)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
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run.py
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f"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}"
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)
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def get_count_ride_type():
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df = pd.read_sql(
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"""
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)
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return df
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def get_most_popular_stations():
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df = pd.read_sql(
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)
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return df
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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f"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}"
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)
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def get_count_ride_type():
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df = pd.read_sql(
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"""
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)
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return df
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def get_most_popular_stations():
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df = pd.read_sql(
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)
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return df
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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