Update app.py

app.py

@@ -4,12 +4,7 @@ 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()
 
@@ -24,15 +19,14 @@ def getDatasetFind(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(),
@@ -58,11 +52,48 @@ def filter_map(min_price, max_price, boroughs):
             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")
@@ -70,16 +101,30 @@ with gr.Blocks() as demo:
         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 Text:")
+            df20 = gr.Textbox(lines=4, default="", label="Find Mental Health Provider e.g. City/State/Name/License:")
             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")
 
+        # 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)
     
+    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()