Update app.py

app.py

@@ -1,181 +1,130 @@
-import googlemaps
-import os
-#GM_TOKEN=os.environ.get("GM_TOKEN") # Get Google Maps Token Here:  https://console.cloud.google.com/google/maps-apis/
-
-from datetime import datetime
-
-gmaps = googlemaps.Client(key='AIzaSyDybq2mxujekZVivmr03Y5-GGHXesn4TLI')
-
-
-def GetMapInfo(inputText):
-    geocode_result = gmaps.geocode('640 Jackson Street, St. Paul, MN 55101')
-    geo_address = geocode_result[0]['formatted_address']
-    geo_directions = geocode_result[0]['geometry']['location']
-    geo_geocode = geocode_result[0]['geometry']['location_type']
-    
-    lat = geo_directions['lat']
-    lng = geo_directions['lng']
-    
-    reverse_geocode_result = gmaps.reverse_geocode((lat, lng))
-    
-    now = datetime.now()
-    directions_result = gmaps.directions("Sydney Town Hall","Parramatta, NSW",mode="transit", departure_time=now)
-    #addressvalidation_result =  gmaps.addressvalidation(['1600 Amphitheatre Pk'], regionCode='US', locality='Mountain View', enableUspsCass=True)
-
-    #return geocode_result, reverse_geocode_result, directions_result, addressvalidation_result
-    #return geo_address, geo_directions, geo_geocode, reverse_geocode_result, directions_result, addressvalidation_result
-    return geo_address, geo_directions, geo_geocode
-
-from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
-import torch
 import gradio as gr
-from datasets import load_dataset
-
-# PersistDataset -----
-import os
-import csv
-from gradio import inputs, outputs
-import huggingface_hub
-from huggingface_hub import Repository, hf_hub_download, upload_file
-from datetime import datetime
-
-#fastapi is where its at:  share your app, share your api
-import fastapi
-
-from typing import List, Dict
-import httpx
 import pandas as pd
-import datasets as ds
-
-UseMemory=True
-HF_TOKEN=os.environ.get("HF_TOKEN")
-
-def SaveResult(text, outputfileName):
-    basedir = os.path.dirname(__file__)
-    savePath = outputfileName
-    print("Saving: " + text + " to " + savePath)
-    from os.path import exists
-    file_exists = exists(savePath)
-    if file_exists:
-        with open(outputfileName, "a") as f: #append
-            f.write(str(text.replace("\n","  ")))
-            f.write('\n')
-    else:
-        with open(outputfileName, "w") as f: #write
-            f.write(str("time, message, text\n")) # one time only to get column headers for CSV file
-            f.write(str(text.replace("\n","  ")))
-            f.write('\n')
-    return
+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))]
     
-def store_message(name: str, message: str, outputfileName: str):
-    basedir = os.path.dirname(__file__)
-    savePath = outputfileName
-    
-    # if file doesnt exist, create it with labels
-    from os.path import exists
-    file_exists = exists(savePath)
+    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))]
     
-    if (file_exists==False):
-        with open(savePath, "w") as f: #write
-            f.write(str("time, message, text\n")) # one time only to get column headers for CSV file
-            if name and message:
-                writer = csv.DictWriter(f, fieldnames=["time", "message", "name"])
-                writer.writerow(
-                    {"time": str(datetime.now()), "message": message.strip(), "name": name.strip()  }
-                )
-        df = pd.read_csv(savePath)
-        df = df.sort_values(df.columns[0],ascending=False)
-    else:
-        if name and message:
-            with open(savePath, "a") as csvfile:
-                writer = csv.DictWriter(csvfile, fieldnames=[ "time", "message", "name", ])
-                writer.writerow(
-                    {"time": str(datetime.now()), "message": message.strip(), "name": name.strip()  }
-                )
-        df = pd.read_csv(savePath)
-        df = df.sort_values(df.columns[0],ascending=False)
-    return df
-
-mname = "facebook/blenderbot-400M-distill"
-model = BlenderbotForConditionalGeneration.from_pretrained(mname)
-tokenizer = BlenderbotTokenizer.from_pretrained(mname)
-
-def take_last_tokens(inputs, note_history, history):
-    if inputs['input_ids'].shape[1] > 128:
-        inputs['input_ids'] = torch.tensor([inputs['input_ids'][0][-128:].tolist()])
-        inputs['attention_mask'] = torch.tensor([inputs['attention_mask'][0][-128:].tolist()])
-        note_history = ['</s> <s>'.join(note_history[0].split('</s> <s>')[2:])]
-        history = history[1:]
-    return inputs, note_history, history
+    latitude = 44.9382
+    longitude = -93.6561
     
-def add_note_to_history(note, note_history):# good example of non async since we wait around til we know it went okay.
-    note_history.append(note)
-    note_history = '</s> <s>'.join(note_history)
-    return [note_history]
+    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
 
-title = "💬ChatBack🧠💾"
-description = """Chatbot With persistent memory dataset allowing multiagent system AI to access a shared dataset as memory pool with stored interactions. 
- Current Best SOTA Chatbot:  https://huggingface.co/facebook/blenderbot-400M-distill?text=Hey+my+name+is+ChatBack%21+Are+you+ready+to+rock%3F  """
 
-def get_base(filename): 
-        basedir = os.path.dirname(__file__)
-        print(basedir)
-        #loadPath = basedir + "\\" + filename # works on windows
-        loadPath = basedir + filename # works on ubuntu
-        print(loadPath)
-        return loadPath
-    
-def chat(message, history):
-    history = history or []
-    if history: 
-        history_useful = ['</s> <s>'.join([str(a[0])+'</s> <s>'+str(a[1]) for a in history])]
-    else:
-        history_useful = []
+with gr.Blocks() as demo:
+    with gr.Column():
         
-    history_useful = add_note_to_history(message, history_useful)
-    inputs = tokenizer(history_useful, return_tensors="pt")
-    inputs, history_useful, history = take_last_tokens(inputs, history_useful, history)
-    reply_ids = model.generate(**inputs)
-    response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]
-    history_useful = add_note_to_history(response, history_useful)
-    list_history = history_useful[0].split('</s> <s>')
-    history.append((list_history[-2], list_history[-1]))  
-    
-    df=pd.DataFrame()
+        # 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)
     
-    if UseMemory: 
-        #outputfileName = 'ChatbotMemory.csv'
-        outputfileName = 'ChatbotMemory3.csv' # Test first time file create
-        df = store_message(message, response, outputfileName) # Save to dataset
-        basedir = get_base(outputfileName)
+    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)
         
-    return history, df, basedir
-
-
-
+    btn3.click(centerMap, [min_price, max_price, boroughs], map)
 
-with gr.Blocks() as demo:
-  gr.Markdown("<h1><center>🍰 AI Google Maps Demonstration🎨</center></h1>")
-  
-  with gr.Row():
-    t1 = gr.Textbox(lines=1, default="", label="Chat Text:")
-    b1 = gr.Button("Respond and Retrieve Messages")
-    b2 = gr.Button("Get Map Information")
-    
-  with gr.Row(): # inputs and buttons
-    s1 = gr.State([])
-    df1 = gr.Dataframe(wrap=True, max_rows=1000, overflow_row_behaviour= "paginate")
-  with gr.Row(): # inputs and buttons
-    file = gr.File(label="File")
-    s2 = gr.Markdown()
-  with gr.Row():
-    df21 = gr.Textbox(lines=4, default="", label="Geocode1:")
-    df22 = gr.Textbox(lines=4, default="", label="Geocode2:")
-    df23 = gr.Textbox(lines=4, default="", label="Geocode3:")
-    df3 = gr.Dataframe(wrap=True, max_rows=1000, overflow_row_behaviour= "paginate")
-    df4 = gr.Dataframe(wrap=True, max_rows=1000, overflow_row_behaviour= "paginate")
-  b1.click(fn=chat, inputs=[t1, s1], outputs=[s1, df1, file]) 
-  b2.click(fn=GetMapInfo, inputs=[t1], outputs=[df21, df22, df23]) 
-    
-demo.launch(debug=True, show_error=True)
+demo.launch()