generate docstrings
Project description
Lockhart
Table of Contents
Installation
pip install lockhart
## or with pipx
pipx install lockhart
Instructions
You will need an openai key, you can obtain one from their
website. Once you have it then set
your OPENAI_API_KEY
environment variable.
export OPENAI_API_KEY='sk-***'
Lockhart it currently very crude, it works out of the clipboard. Copy a
function to your clipboard, run lockhart docstring
then paste the docstring
in.
lockhart tui
lockhart tui
keys
- C - Create new code
- e - edit return value from history item
- E - open an empty edit template
- o - open the current prompt from history
- j - next item from history
- k - prev item from history
- q - quit
- d - toggle dark mode
- b - toggle sidebar of preset prompts
- G - open an empty prompt for writing a git commit
Editing prompts
Currently the tui runs the users configured $EDITOR
in a subprocess, waits for the editor to close, and if the file was changed then it runs the prompt. If the file was not changed then it does not run the prompt.
## mac/linux
export EDITOR=vim
## windows
set EDITOR=notepad
closing vim
:x without a change to the content will not trigger a file change and the prompt will not run. If you want to use the prompt as-is without edit close vim with :wq.
CLI
lockhart prompt run
You can also run stored prompts from the command line without the tui.
## pipe text into your prompt
echo 'write a python3.10 hello world function' | lockhart prompt run code-create
## use the --text flag
lockhart prompt run code-create --text 'write a python3.10 hello world function'
## directly edit your prompt with the editor
lockhart prompt run code-create --edit
The cli also handles text being piped into a prompt run. You can still --edit
it, or pipe the results somewhere. Many of the commit messages in this repo were created using this.
## pre-populate your commit message
git diff --staged | lockhart prompt run commit | git commit -evF -
## write a changelog entry for your current pr
gh pr diff | lockhart prompt run changelog --edit
## write a changelog entry for your current staged changes
git diff --staged | lockhart prompt run changelog --edit
listing prompts
The cli can list all of the prompts configured in ~/.config/lockhart/lockhart.toml
using the cli.
lockhart prompt list
lockhart history
The cli can output your entire history as json.
lockhart history list
Original features that need re-implemented
These shouldn't be too bad to get re-implemented, but I think we need a pre-processor to be able to get this working.
Docstring Examples
def add(a, b):
"""
Add two numbers together.
Args:
a (int): The first number to add.
b (int): The second number to add.
Returns:
int: The sum of the two numbers.
Example:
add(2, 3) -> 5
"""
return a + b
Without even type hinting the arguments lockhart
can see that this function is using pandas.
def get_summary_data(df):
"""
This function takes a dataframe as an argument and returns a summary of the data grouped by column A.
Args:
df (DataFrame): The dataframe to be summarized.
Returns:
DataFrame: A summary of the data grouped by column A, containing the sum of columns C and D.
Example:
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9], 'D': [10, 11, 12]})
get_summary_data(df)
"""
df.groupby("A")[["C", "D"]].sum()
Here is an example from the fastapi docs. It writes a correct docstring to describe the function, but misses out that its a fastapi route without more context than just the function.
@app.put("/items/{item_id}")
def update_item(item_id: int, item: Item):
"""
Update an item in the database.
This function takes an item_id and an item object as parameters and updates the item in the database.
Args:
item_id (int): The id of the item to be updated.
item (Item): The item object to be updated.
Returns:
dict: A dictionary containing the item_id and the updated item.
Example:
results = update_item(1, {'name': 'Foo', 'price': 10.99})
"""
results = {"item_id": item_id, "item": item}
return results
Examples Refactor
def add(a, b):
return a + b
# refactor the following code to compute the ratio of the two numbers
def ratio(a, b):
return a / b
# Before
def get_summary_data(df):
df.groupby("A")[["C", "D"]].sum()
# refactor the following code to also return the averages
# After
def get_summary_data(df):
df.groupby("A")[["C", "D"]].agg(["sum", "mean"])
Run lockhart refactor
, then set the prompt to refactor the following code to accept a parameter item_name
. It did a great job at adding it to the
arguments list, type hinting it to a string, but again missed that this is a
fastapi route and we wanted to also update the decorator.
``` python
# before
@app.put("/items/{item_id}")
def update_item(item_id: int, item: Item):
results = {"item_id": item_id, "item": item}
return results
# refactor the following code to accept a parameter item_name
# after
@app.put("/items/{item_id}")
def update_item(item_id: int, item_name: str, item: Item):
results = {"item_id": item_id, "item_name": item_name, "item": item}
return results
License
lockhart
is distributed under the terms of the
MIT license.
Project details
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