warmchang / apartment-finder Goto Github PK

This repo contains the code for a bot that will scrape Craigslist for real-time listings matching specific criteria, then alert you in Slack. This will let you quickly see the best new listings, and contact the owners. You can adjust the settings to change your price range, what neighborhoods you want to look in, and what transit stations and other points of interest you'd like to be close to.

I successfully used this tool to find an apartment when I moved from Boston to SF. It saved a good amount of time and money. Read more about it here.

It's recommended to follow the Docker installation and usage instructions.

Look in settings.py for a full list of all the configuration options. Here's a high level overview:

• MIN_PRICE -- the minimum listing price you want to search for.
• MAX_PRICE -- the minimum listing price you want to search for.
• CRAIGSLIST_SITE -- the regional Craigslist site you want to search in.
• AREAS -- a list of areas of the regional Craiglist site that you want to search in.
• BOXES -- coordinate boxes of the neighborhoods you want to look in.
• NEIGHBORHOODS -- if the listing doesn't have coordinates, a list of neighborhoods to match on.
• MAX_TRANSIT_DISTANCE -- the farthest you want to be from a transit station.
• TRANSIT_STATIONS -- the coordinates of transit stations.
• CRAIGSLIST_HOUSING_SECTION -- the subsection of Craigslist housing that you want to look in.
• SLACK_CHANNEL -- the Slack channel you want the bot to post in.

Before using this bot, you'll need a Slack team, a channel for the bot to post into, and a Slack API key:

• Create a Slack team, which you can do here.
• Create a channel for the listings to be posted into. Here's help on this. It's suggested to use #housing as the name of the channel.
• Get a Slack API token, which you can do here. Here's more information on the process.

• Create a folder called config, then put a file called private.py inside.
• Specify new values for any of the settings above in private.py.
  • For example, you could put AREAS = ['sfc'] in private.py to only look in San Francisco.
  • If you want to post into a Slack channel not called housing, add an entry for SLACK_CHANNEL.
  • If you don't want to look in the Bay Area, you'll need to update the following settings at the minimum:
    • CRAIGSLIST_SITE
    • AREAS
    • BOXES
    • NEIGHBORHOODS
    • TRANSIT_STATIONS
    • CRAIGSLIST_HOUSING_SECTION
    • MIN_PRICE
    • MAX_PRICE

• Create a file called private.py in this folder.
  • Add a value called SLACK_TOKEN that contains your Slack API token.
  • Add any other values you want to private.py.

• Make sure to do the steps in the configuration section above first.
• Install Docker by following these instructions.
• To run the program with the default configuration:
  • docker run -d -e SLACK_TOKEN={YOUR_SLACK_TOKEN} dataquestio/apartment-finder
• To run the program with your own configuration:
  • docker run -d -e SLACK_TOKEN={YOUR_SLACK_TOKEN} -v {ABSOLUTE_PATH_TO_YOUR_CONFIG_FOLDER}:/opt/wwc/apartment-finder/config dataquestio/apartment-finder

• Look in the Dockerfile, and make sure you install any of the apt packages listed there.
• Install Python 3 using Anaconda or another method.
• Install the Python requirements with pip install -r requirements.txt.
• Run the program with python main_loop.py. Results will be posted to your #Housing channel if successful.

• Use docker ps to get the id of the container running the bot.
• Run docker exec -it {YOUR_CONTAINER_ID} /bin/bash to get a command shell inside the container.
• Run sqlite listings.db to run the sqlite command line tool and inspect the database state (the only table is also called listings).
  • select * from listings will get all of the stored listings.
  • If nothing is in the database, you may need to wait for a bit, or verify that your settings aren't too restrictive and aren't finding any listings.
  • You can see how many listings are being found by looking at the logs.
• Inspect the logs using tail -f -n 1000 /opt/wwc/logs/afinder.log.

• Look at the stdout of the main program.
• Inspect listings.db to ensure listings are being added.

• Create a server that has Docker installed. It's suggested to use Digital Ocean.
• Follow the configuration + installation instructions for Docker above.