Simplest Alfred Client that I use my own projects.
Read this blog => Developing alfred5 workflow using alfred5 and python
pip3 install alfred5 --target=src/libs- Projects dir structure
- Put your codes and
requirements.txttosrcfolders - Install
alfred5viapip3 install alfred5 --target=src/libs
- Add the top of the
main.pyimport sys sys.path.insert(0, "src/libs")
- If u want to use different
--targetfor ex.useWorkflowClient.run(packagedir=".") - Sample of default structure:
- If u install all of requirements, dont need to create
requirements.txtfile insrc - If you use
vscode, add the code that below to.vscode/settings.jsonto debug your file{ "python.analysis.extraPaths": [ "./src/libs" ], "python.analysis.exclude": [ "./src/libs" ] }
- Put your codes and
- Via
SnippetsClientAPI create custom snippets programmaically - Via
WorkflowClientAPI create custom alfred workflow- Craete
requirements.txtfile for your python project to letalfred5installs them if needed 🙃 - To install
from requirements.txtdo all import packages insidemain- Useglobalkeyword to access imported packages globallyclient.queryis the query stringclient.page_countis the page count for pagination results
- Dont need to add
alfred5torequirements.txt
- Craete
- Use
WorkflowClient.logto log your message to alfred debugger - Use
WorkflowClient(main, cache=True)method to use caching system- Just do it for static (not timebased nor any dynamic stuff) response
- Db path is
db/results.ymlalso you can see it from workflow debug panel
from re import sub
from urllib.parse import quote_plus
import sys
sys.path.insert(0, "src/libs")
from alfred5 import WorkflowClient
async def main(client: WorkflowClient):
# To auto install requirements all import operation must be in here
global get
from requests import get
query = client.query
client.log(f"my query: {query}") # use it to see your log in workflow debug panel
# (use cache=True) Use cache system to quick response instead of old style that below
# if client.load_cached_response():
# return
char_count = str(len( query))
word_count = str(len(query.split(" ")))
line_count = str(len(query.split("\n")))
encoded_string = quote_plus(query)
remove_dublication = " ".join(dict.fromkeys(query.split(" ")))
upper_case = query.upper()
lower_case = query.lower()
capitalized = query.capitalize()
template = sub(r"[a-zA-Z0-9]", "X", query)
client.add_result(encoded_string, "Encoded", arg=encoded_string)
client.add_result(remove_dublication, "Remove dublication", arg=remove_dublication)
client.add_result(upper_case, "Upper Case", arg=upper_case)
client.add_result(lower_case, "Lower Case", arg=lower_case)
client.add_result(capitalized, "Capitalized", arg=capitalized)
client.add_result(template, "Template", arg=template)
client.add_result(char_count, "Characters", arg=char_count)
client.add_result(word_count, "Words", arg=word_count)
client.add_result(line_count, "Lines", arg=line_count)
# (use cache=True) to cache result for query instead of old style that below
# if u work with static results (not dynamic; coin price etc.)
# client.cache_response()
if __name__ == "__main__":
WorkflowClient.run(main) # WorkflowClient.run(main, cache=True)Copyright 2023 Yunus Emre Ak ~ YEmreAk.com
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
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