Duplicate from awacke1/Google-Maps-Web-Service-Py

Co-authored-by: Aaron C Wacker <awacke1@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+title: GMaps-Geocode-Gradio-Lat-Lon
+emoji: 🌍Map
+colorFrom: gray
+colorTo: purple
+sdk: gradio
+sdk_version: 3.16.2
+app_file: app.py
+pinned: false
+duplicated_from: awacke1/Google-Maps-Web-Service-Py
+---
+
+Naming Scheme:
+3D-GLB-Aframe-GoogleAPI-Map programs on Gradio could be named as "Gradio3DMap" or "GradioMap3D".
+
+Ontology:
+Gradio3DMap or GradioMap3D can be categorized as a type of mapping software that utilizes three-dimensional GLB models to display geographical data on a map. These programs are built using A-Frame, a web framework for building virtual reality experiences, and integrated with GoogleAPI to provide location-based services.
+
+The ontology for Gradio3DMap or GradioMap3D can be further broken down into subcategories, such as:
+
+Data Visualization: These programs can be used to visualize and analyze complex geospatial data in a three-dimensional format, making it easier for users to understand and interpret the information.
+
+Real-time Data Updates: Gradio3DMap or GradioMap3D can be used to display real-time updates for various data, such as weather, traffic, and other live events.
+
+Interactive User Interface: The software can be built with interactive user interfaces that allow users to interact with the map and its elements, such as panning, zooming, and rotating the 3D model.
+
+Cross-Platform Compatibility: Gradio3DMap or GradioMap3D can be built to run on multiple platforms, such as desktops, smartphones, and tablets, making it accessible to a wide range of users.
+
+Customization: The software can be customized to fit specific user requirements and business needs. This includes adding custom markers, colors, and other visual elements to the map.
+
+Integration with other APIs: Gradio3DMap or GradioMap3D can be integrated with other APIs, such as social media APIs, to provide additional functionality, such as real-time feeds of social media data.
+
+Overall, Gradio3DMap or GradioMap3D is a powerful mapping software that provides an immersive and interactive way to visualize and analyze geospatial data.

app.py

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+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
+
+# googlemaps_TOKEN = os.environ.get("googlemaps_TOKEN")
+# gmaps = googlemaps.Client(key=googlemaps_TOKEN)
+gmaps = googlemaps.Client(key='AIzaSyDybq2mxujekZVivmr03Y5-GGHXesn4TLI')
+
+
+def GetMapInfo(inputText):
+    #geocode_result = gmaps.geocode('640 Jackson Street, St. Paul, MN 55101')
+    geocode_result = gmaps.geocode(inputText)
+    
+    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
+
+    
+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)
+    
+    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
+    
+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]
+
+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 = []
+        
+    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()
+    
+    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)
+        
+    return history, df, basedir
+
+
+
+
+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)

maps-platform-101-webgl.zip

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requirements.txt

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+googlemaps
+transformers
+torch