💬 AI Real Estate Assistant App

Requirements

Develop an AI-powered assistant for a real estate agency that assists potential buyers and renters in finding their ideal property.

The assistant should engage users in a conversation, asking questions about their preferences such as:

• location (city, neighborhood)
• budget range
• property type (apartment, house, condo)
• number of bedrooms and bathrooms
• desired amenities (parking, garden, pool)
• proximity to schools or public transportation.

Description

POC app will have simple UI and will use local csv with possibility to specify list of external CSV files.

Demo

Formatted dataset will contain fake extra fields based on the task requirements, for demo purposes.

DataFrame Columns

This table describes the columns in the DataFrame:

Column Name	Description
id	Unique identifier for each record.
city	Name of the city where the property is located.
type	Type of property (e.g., apartment, house).
square_meters	Area of the property in square meters.
rooms	Number of rooms in the property.
floor	Floor number where the property is located.
floor_count	Total number of floors in the building.
build_year	Year the building was constructed.
latitude	Latitude coordinate of the property.
longitude	Longitude coordinate of the property.
centre_distance	Distance from the property to the city center.
poi_count	Number of Points of Interest nearby.
school_distance	Distance to the nearest school.
clinic_distance	Distance to the nearest clinic.
post_office_distance	Distance to the nearest post office.
kindergarten_distance	Distance to the nearest kindergarten.
restaurant_distance	Distance to the nearest restaurant.
college_distance	Distance to the nearest college.
pharmacy_distance	Distance to the nearest pharmacy.
ownership	Type of ownership (e.g., condominium).
building_material	Material used in the construction of the building.
condition	Condition of the property (e.g., new, good).
has_parking_space	Whether the property has a parking space (True/False).
has_balcony	Whether the property has a balcony (True/False).
has_elevator	Whether the building has an elevator (True/False).
has_security	Whether the property has security (True/False).
has_storage_room	Whether the property has a storage room (True/False).
price	Price of the property.
price_media	Median price of similar properties.
price_delta	Difference between the property's price and price_media.
negotiation_rate	Possibility of negotiation (e.g., High, Medium, Low).
bathrooms	Number of bathrooms in the property.
owner_name	Name of the property owner.
owner_phone	Contact phone number of the property owner.
has_garden	Whether the property has a garden (True/False).
has_pool	Whether the property has a pool (True/False).
has_garage	Whether the property has a garage (True/False).
has_bike_room	Whether the property has a bike room (True/False).

Init project for development

# Install pip and poetry
python -m ensurepip --upgrade
curl -sSL https://install.python-poetry.org | python3 - --version 1.7.0
# Init poetry virtual env
poetry init
poetry env use 3.11
poetry config virtualenvs.in-project true
source .venv/bin/activate
poetry config virtualenvs.prompt 'ai-real-estate-assistant'
poetry config --list
# Add deps
poetry add ...
poetry lock

Run project for development

git clone https://github.com/AleksNeStu/ai-real-estate-assistant.git
poetry install --no-root
source .venv/bin/activate

Run app

Local run

Deploy app

Streamlit Deploy