Duplicate from awacke1/Gradio-Maps-Latitude-Longitude

Co-authored-by: Aaron C Wacker <[email protected]>

.gitattributes

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+NPI_2023_01_17-05.10.57.PM.csv filter=lfs diff=lfs merge=lfs -text
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NPI_2023_01_17-05.10.57.PM.csv

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+oid sha256:2ff483d2562dc7e15b1e100bdcc47352ba65818acec0dbc6a65c576192173cfd
+size 1650616400

README.md

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+---
+title: Gradio Maps Latitude Longitude
+emoji: 🌖City
+colorFrom: red
+colorTo: green
+sdk: gradio
+sdk_version: 3.16.2
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: awacke1/Gradio-Maps-Latitude-Longitude
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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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()

requirements.txt

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+plotly