Update app.py

app.py

@@ -1,130 +1,185 @@
+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
-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))]
+# 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
+
+
+def store_message(name: str, message: str, outputfileName: str):
+    basedir = os.path.dirname(__file__)
+    savePath = outputfileName
     
-    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 doesnt exist, create it with labels
+    from os.path import exists
+    file_exists = exists(savePath)
+
+
     
-    latitude = 44.9382
-    longitude = -93.6561
+    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
     
-    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
+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]
 
 
-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()
+
+
+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 = []
         
-        # 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)
+    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]))  
     
-    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)
+    df=pd.DataFrame()
+
+    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)
+
+
         
-    btn3.click(centerMap, [min_price, max_price, boroughs], map)
+    return history, df, basedir
 
-demo.launch()
+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)