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
http://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/
- install requirements
pip install -r requirements.txt
- 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
)-
Run use_aa.py or use_ac.py and you will see the output file.
-
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.
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