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| import gradio as gr | |
| import pandas as pd | |
| import plotly.graph_objects as go | |
| from datasets import load_dataset | |
| dataset = load_dataset('text', data_files={'train': ['NPI_2023_01_17-05.10.57.PM.csv'], 'test': 'NPI_2023_01_17-05.10.57.PM.csv'}) | |
| #1.6GB NPI file with MH therapy taxonomy provider codes (NUCC based) with human friendly replacement labels (e.g. Counselor rather than code) | |
| datasetNYC = load_dataset("gradio/NYC-Airbnb-Open-Data", split="train") | |
| df = datasetNYC.to_pandas() | |
| def MatchText(pddf, name): | |
| pd.set_option("display.max_rows", None) | |
| data = pddf | |
| swith=data.loc[data['text'].str.contains(name, case=False, na=False)] | |
| return swith | |
| def getDatasetFind(findString): | |
| #finder = dataset.filter(lambda example: example['text'].find(findString)) | |
| finder = dataset['train'].filter(lambda example: example['text'].find(findString)) | |
| finder = finder = finder.to_pandas() | |
| g1=MatchText(finder, findString) | |
| return g1 | |
| def filter_map(min_price, max_price, 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() | |
| text_list = [(names[i], prices[i]) for i in range(0, len(names))] | |
| fig = go.Figure(go.Scattermapbox( | |
| customdata=text_list, | |
| lat=filtered_df['latitude'].tolist(), | |
| lon=filtered_df['longitude'].tolist(), | |
| mode='markers', | |
| marker=go.scattermapbox.Marker( | |
| size=6 | |
| ), | |
| hoverinfo="text", | |
| hovertemplate='Name: %{customdata[0]}Price: $%{customdata[1]}' | |
| )) | |
| fig.update_layout( | |
| mapbox_style="open-street-map", | |
| hovermode='closest', | |
| mapbox=dict( | |
| bearing=0, | |
| center=go.layout.mapbox.Center( | |
| lat=40.67, | |
| lon=-73.90 | |
| ), | |
| pitch=0, | |
| zoom=9 | |
| ), | |
| ) | |
| return fig | |
| def centerMap(min_price, max_price, 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() | |
| text_list = [(names[i], prices[i]) for i in range(0, len(names))] | |
| latitude = 44.9382 | |
| longitude = -93.6561 | |
| fig = go.Figure(go.Scattermapbox( | |
| customdata=text_list, | |
| lat=filtered_df['latitude'].tolist(), | |
| lon=filtered_df['longitude'].tolist(), mode='markers', | |
| marker=go.scattermapbox.Marker( | |
| size=6 | |
| ), | |
| hoverinfo="text", | |
| #hovertemplate='Lat: %{lat} Long:%{lng} City: %{cityNm}' | |
| )) | |
| fig.update_layout( | |
| mapbox_style="open-street-map", | |
| hovermode='closest', | |
| mapbox=dict( | |
| bearing=0, | |
| center=go.layout.mapbox.Center( | |
| lat=latitude, | |
| lon=longitude | |
| ), | |
| pitch=0, | |
| zoom=9 | |
| ), | |
| ) | |
| return fig | |
| with gr.Blocks() as demo: | |
| with gr.Column(): | |
| # Price/Boroughs/Map/Filter for AirBnB | |
| with gr.Row(): | |
| min_price = gr.Number(value=250, label="Minimum Price") | |
| max_price = gr.Number(value=1000, label="Maximum Price") | |
| boroughs = gr.CheckboxGroup(choices=["Queens", "Brooklyn", "Manhattan", "Bronx", "Staten Island"], value=["Queens", "Brooklyn"], label="Select Boroughs:") | |
| btn = gr.Button(value="Update Filter") | |
| map = gr.Plot().style() | |
| # Mental Health Provider Finder | |
| with gr.Row(): | |
| df20 = gr.Textbox(lines=4, default="", label="Find Mental Health Provider e.g. City/State/Name/License:") | |
| btn2 = gr.Button(value="Find") | |
| with gr.Row(): | |
| df4 = gr.Dataframe(wrap=True, max_rows=10000, overflow_row_behaviour= "paginate") | |
| # City Map | |
| with gr.Row(): | |
| df2 = gr.Textbox(lines=1, default="Mound", label="Find City:") | |
| latitudeUI = gr.Textbox(lines=1, default="44.9382", label="Latitude:") | |
| longitudeUI = gr.Textbox(lines=1, default="-93.6561", label="Longitude:") | |
| btn3 = gr.Button(value="Lat-Long") | |
| demo.load(filter_map, [min_price, max_price, boroughs], map) | |
| btn.click(filter_map, [min_price, max_price, boroughs], map) | |
| btn2.click(getDatasetFind,df20,df4 ) | |
| # Lookup on US once you have city to get lat/long | |
| # US 55364 Mound Minnesota MN Hennepin 053 44.9382 -93.6561 4 | |
| #latitude = 44.9382 | |
| #longitude = -93.6561 | |
| #btn3.click(centerMap, map) | |
| btn3.click(centerMap, [min_price, max_price, boroughs], map) | |
| demo.launch() |