See update. Update.md

1.Install docker desktop

2.1 Create a docker-compose.yml file (preferably in a new work directory)

version: "3.8"
services:
  web:
    image: falantasw/nt-tool:${FILL ME WITH LATEST TAG}
    pull_policy: if_not_present
    ports:
      - 8050:8050
    volumes:
      - ./input:/code/input
      - ./output:/code/output

Visit falanta's docker hub registry and update the image above with the latest tag.

Sample compose file

version: "3.8"
services:
  web:
    image: falantasw/nt-tool:0.0.1
    pull_policy: if_not_present
    ports:
      - 8050:8050
    volumes:
      - ./input:/code/input
      - ./output:/code/output

2.2. Create an input directory and an output directory under the directory where the docker-compose.yml stays.

2.3. Use the input json as templates to create some input json files in the input directory you just created.

3.If you are using linux or mac, open a terminal. If you are using windows, open a command prompt or PowerShell window. Navigate to the directory where you created the docker compose yaml file. Run the compose up command

docker-compose up

Note: make sure your docker desktop is running

4.1 If you would like to use the web UI search, open a browser and go to

https://127.0.0.1:8050

4.2 If you would like to generate a excel output, you may use the following docker command

docker exec -i $(docker container ls --filter "ancestor=falantasw/nt-tool:0.0.1" --format "{{.ID}}" | head -n 1 | xargs) python /code/src/main.py ${FILL ME WITH AN AIRLINE} --input_file /code/input/${${FILL ME WITH YOU INPUT FILE NAME}} --output_dir /code/output/

Eligible airline functions

use_aa
use_dl
use_ac

A sample docker command looks like

docker exec -i $(docker container ls --filter "ancestor=tool:latest" --format "{{.ID}}" | head -n 1 | xargs) python /code/src/main.py use_aa --input_file /code/input/aa_or_dl_input.json --output_dir /code/output/

1. install requirements

pip install -r requirements.txt

2. In use_aa.py or use_ac.py or use_dl.py set the conditions you want.

    origins = ['HKG']
    destinations = ['KUL']
    start_dt = '2023-03-31'
    end_dt = '2023-03-31'
    dates = date_range(start_dt, end_dt)
    #  means eco, pre, biz and first
    cabin_class = [
        "ECO",
        "PRE",
        "BIZ",
        "FIRST"
    ]
    airbound_filter = AirBoundFilter(
        max_stops=1,
        airline_include=[],
        airline_exclude=['MH'],
    )
    price_filter = PriceFilter(
        min_quota=1,
        max_miles_per_person=999999,
        preferred_classes=[CabinClass.J, CabinClass.F, CabinClass.Y],
        mixed_cabin_accepted=True
    )

3. Run use_aa.py or use_ac.py and you will see the output file.

4. You can also run web_branch.py and go through with a web view. Currently the app wiil use both engines to search results.

If you like this project, welcome to buy me a coffee.