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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)
#datasetNPIMH = load_dataset("awacke1/NPI-Providers-And-Facilities-By-Taxonomy", split="train")
#datasetNPIMH = load_dataset("awacke1/NPI-Providers-And-Facilities-By-Taxonomy", split='train[:1%]')
#print(datasetNPIMH.shape)
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
with gr.Blocks() as demo:
with gr.Column():
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()
with gr.Row():
df20 = gr.Textbox(lines=4, default="", label="Find Text:")
btn2 = gr.Button(value="Find")
#df21 = gr.Textbox(lines=4, default="", label="Found:")
with gr.Row():
df4 = gr.Dataframe(wrap=True, max_rows=10000, overflow_row_behaviour= "paginate")
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 )
demo.launch() |