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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 | |
| def SolutionIntent(): | |
| markdown=""" | |
| # Solution Intent | |
| Intent of this space is to provide AI models around maps and **Earth** **Modeling** pursuits which solves for the following pains experienced by people traveling around an area. | |
| **Long Term Plan** is to create an interactive 3D laden view in real time which can easily show walking and driving routes to points of interest. | |
| # Problems solved include: | |
| 1. When visiting a new place the **Route** from start to finish includes some difficult challenges including knowing where optimal **Parking** resides.. | |
| 2. Entrances for large building such as **Hospitals**, **Clinics**, **Urgent Care Facilities** pose the problem where to get from **Parking** to the office doors, | |
| 3. It is hard to predict which route to take where **Handicap** accessible routes lay. | |
| 4. For maximum enjoyment and intellectual stimulation, **Routes** which are ideal should include information on **Points of Interest** or **POI** reside. | |
| 5. For memory impaired individuals it sometimes is problematic to understand what the route to **Home** is. | |
| 6. Each person has particular places they enjoy or would like to visit. This personalized **Favorites** list would have order and correlation between any two places. | |
| 7. Knowing how to process **Automated** information gathering such as mapped **Latitude and Longitude** and distance calculations or regional routes with time expectations. | |
| # Information Layers and Maps | |
| Information layers such as rental prices of places to stay can be shown on a map which allow people looking for locality or commonality of walking distance or driving distance factors allows people plan and understand optimal alignment of wants, needs and offerings in a location. | |
| The snapshot below shows the places available in **New York** specifically **Manhattan** and **Central Park** which contains a **Museum** route. | |
|  | |
| # Knowledge on High-Demand Properties and Areas or Points of Interest 🏢 | |
| In dynamic worlds of real estate and practice, staying ahead means making informed decisions. | |
| This article offers insights for Knowledge Seekers and Entrepreneurs for locating properties in high-demand areas. | |
| The knowledge provided by this app should highlight the best points of interest and publications regarding an area. | |
| ## Understanding the Landscape 🌆 | |
| 1. **High-Demand Areas** 🏙️: | |
| - *Location is Key*: Invest in properties situated in neighborhoods with high demand for medical facilities and housing. | |
| - *Data-Driven Decisions*: Utilize data analytics to identify areas with growing populations and increasing long term health needs. | |
| - *Market Research*: Stay updated on real estate market trends and medical services demand in your personalized target locations. | |
| 2. **Points of Interest (POI)** 📍: | |
| - *Cultural Hubs*: Properties near museums, theaters, and cultural landmarks often attract higher rental rates and patient traffic. | |
| - *Proximity to Parks*: Green spaces enhance property value and offer patients a serene environment for recovery. | |
| - *Accessibility**: Prioritize locations with easy access to public transport, major highways, and medical facilities. | |
| 3. **Publications and Resources** 📚: | |
| - *Real Estate Journals*: Stay informed with publications like [Realtor Magazine](https://magazine.realtor/). | |
| - *Medical Journals*: Explore healthcare trends through respected journals like [NEJM](https://www.nejm.org/). | |
| ## Navigating the Medical Real Estate Nexus 🏥 | |
| 1. **Optimal Clinic Locations** 🏨: | |
| - *Proximity to Hospitals*: Consider **properties** near established **hospitals** to tap into **referral networks**. | |
| - *Community Needs Analysis*: Assess **healthcare gaps** in the area and tailor your **services** to meet local demands. | |
| - *Zoning Regulations*: Familiarize yourself with **zoning laws** affecting **medical practices** in different **neighborhoods**. | |
| 2. **Real Estate Investment Strategies** 🏠: | |
| - *Diversification*: Diversify your **property portfolio** by investing in various **property types**, including **commercial spaces** near **medical centers**. | |
| - *Long-Term Vision*: Think about **long-term growth** and **adaptability** when acquiring properties. | |
| - *Property Management*: Ensure efficient **property management** for both **medical facilities** and **rental units**. | |
| 3. **Building Strong Networks** 🤝: | |
| - *Medical Associations*: Join local **medical associations** and **networks** to foster **collaborations with peers and specialists**. | |
| - *Real Estate Professionals*: Connect with **real estate** agents and **property management** experts for guidance. | |
| ## Staying Informed and Ahead 🚀 | |
| 1. **Market Intelligence** 📊: | |
| - *Real Estate Analytics*: Leverage tools like [Zillow](https://www.zillow.com/) for real-time property data. | |
| - *Medical Insights*: Stay updated on medical advancements and trends through [PubMed](https://pubmed.ncbi.nlm.nih.gov/). | |
| 2. **Continuous Learning** 📖: | |
| - *Real Estate Courses*: Invest in your real estate knowledge with online **courses** like: | |
| - [Real Estate Investing for Beginners on Udemy](https://www.udemy.com/course/real-estate-investing-for-beginners-an-intro-to-investment/) | |
| - [Coursera Real Estate Specialization](https://www.coursera.org/specializations/real-estate) | |
| - [MIT OpenCourseWare Real Estate Economics](https://ocw.mit.edu/courses/urban-studies-and-planning/11-432-real-estate-economics-spring-2013/index.htm) | |
| - *Medical Conferences*: Stay updated with the latest medical research and advancements by attending **conferences** and **seminars** such as: | |
| - [American Medical Association (AMA) Conferences](https://www.ama-assn.org/meetings) | |
| - [World Medical Association (WMA) Events](https://www.wma.net/events/) | |
| - [MedPage Today's Conference Coverage](https://www.medpage.com/meetingcoverage) | |
| 3. **Community Engagement** 🏘️: | |
| - *Local Involvement*: Be an active member of the **communities** where your **properties** are located. | |
| - *Patient Outreach*: Engage with **patients** through **community health programs** and **educational initiatives**. | |
| - | |
| 4. **Community Engagement by State** 🏘️: | |
| - **Minnesota (MN)**: In the Land of 10,000 Lakes, engage with your local community through organizations like the [Minnesota Medical Association](https://www.mnmed.org/), and contribute to healthcare initiatives that matter. | |
| - **California (CA)**: In the Golden State, participate in events organized by the [California Medical Association](https://www.cmadocs.org/), and explore opportunities for medical outreach and community involvement. | |
| - **Washington (WA)**: In the Evergreen State, connect with the [Washington State Medical Association](https://wsma.org/) to join efforts in improving healthcare access and patient education. | |
| - **Massachusetts (MA)**: In the Bay State, become an active member of the [Massachusetts Medical Society](https://www.massmed.org/) and engage in community health programs. | |
| - **Texas (TX)**: In the Lone Star State, collaborate with the [Texas Medical Association](https://www.texmed.org/) on community outreach projects and initiatives. | |
| - **New York (NY)**: In the Empire State, explore opportunities for community engagement through the [Medical Society of the State of New York](https://www.mssny.org/). | |
| - **Florida (FL)**: In the Sunshine State, join efforts led by the [Florida Medical Association](https://www.flmedical.org/) to make a positive impact on local healthcare. | |
| - **Hawaii (HI)**: In the Aloha State, participate in community health programs and initiatives supported by the [Hawaii Medical Association](https://www.hawaiimedicalassociation.org/). | |
| - **Wisconsin (WI)**: In the Badger State, connect with the [Wisconsin Medical Society](https://www.wismed.org/) to engage in community health projects and support local healthcare initiatives. | |
| By combining your **medical expertise** with **strategic real estate decisions** and a commitment to staying informed, you can excel in both fields and build a **valuable property collection** in **high-demand areas**. | |
| # Examples: | |
| ## Doctors in Psychiatry and Psychologists Near Mound, MN | |
|  | |
| """ | |
| return markdown | |
| def GoogleEarth(): | |
| markdown=""" | |
| # Mound and Lake Minnetonka Area (Google Earth project) | |
| Google Earth URL: https://earth.google.com/web/search/4704+cavan+road+mound+mn+55364/@44.91717772,-93.63685698,283.49123605a,6909.34027133d,30.00000001y,-113.43671639h,72.27862456t,0r/data=CigiJgokCUFjHTYfekZAERMrKMEgdUZAGTo4dDObZ1fAIdNa_O3zbFfAMikKJwolCiExM2ZOMTNIcUViQXp1QXM3alRFa3BTUTRKdnhBdUZqRHMgAToDCgEw | |
|  | |
| # East/West View of Phelps Island, Lord Fletchers and Al and Alma's | |
|  | |
| # Historic Places on Lake Minnetonka | |
| # Historic Places on Lake Minnetonka | |
| ## 1. Lord Fletcher's | |
| - **Event**: 53rd Minnesota Bound Crappie Contest | |
| - **Date**: May 6, 2023 | |
| - **Details**: Fishing contest for the biggest crappie, supporting Fishing for Life | |
| - [Read more at kare11.com](https://www.kare11.com/article/news/local/kare11-saturday/annual-big-crappie-contest-on-lake-minnetonka-kicks-off-spring-fishing/89-63eba512-6c21-4bee-b4ea-35df2f463062) | |
| ## 2. Al and Alma's | |
| - **Significance**: Known for supper club and charter cruises | |
| - **Recent Event**: Al and Alma's owner acquired Tonka Bay Marina | |
| - **Details**: This acquisition marks a significant expansion in their lake-based services | |
| - [More information on Al and Alma's](https://bringmethenews.com/minnesota-lifestyle/tonka-bay-marina-sold-to-lakeside-supper-club-cruise-company-owner) | |
| ## 3. Tonka Toys | |
| - **Historical Significance**: Started as Mound Metalcraft in 1946 | |
| - **Name Origin**: 'Tonka' is an homage to Lake Minnetonka | |
| - **Legacy**: Evolved into a globally recognized toy brand under Hasbro | |
| - [Read more at startribune.com](https://www.startribune.com/how-did-tonka-trucks-get-their-start-in-minnesota/600242109/) | |
| - [Additional information at boingboing.net](https://boingboing.net/2023/04/21/tonkas-name-is-an-homage-to-lake-minnetonka.html) | |
| # Historic Places on Lake Minnetonka | |
| ## 1. Lord Fletcher's | |
|  | |
| [Learn more about Lord Fletcher's on Wikipedia](https://en.wikipedia.org/wiki/Lord_Fletcher%27s) | |
| ## 2. Al and Alma's | |
|  | |
| [Learn more about Al and Alma's on Wikipedia](https://en.wikipedia.org/wiki/Al_and_Alma%27s) | |
| ## 3. Tonka Toys in Mound, MN | |
|  | |
| [Learn more about Tonka Toys on Wikipedia](https://en.wikipedia.org/wiki/Tonka) | |
| """ | |
| return markdown | |
| 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) | |
| # Display markdown for content | |
| markdown = SolutionIntent() | |
| gr.Markdown(markdown) | |
| markdown = GoogleEarth() | |
| gr.Markdown(markdown) | |
| demo.launch() | |