colors, argparse

docugen-chatgpt.py

@@ -1,40 +1,44 @@
+#!/usr/bin/env python3
+
 import json
 import yaml
 from yaml import CLoader, CDumper
 from revChatGPT.revChatGPT import Chatbot
 import os
 from sys import argv
+import fmtutil
 from parse import py
+from argparse import ArgumentParser
 
-# from argparse import ArgumentParser
+argparser = ArgumentParser(
-
-# argparser = ArgumentParser(
 	# prog="docugen",
-# 	description="generate docs with le AI(tm)",
+	description="generate docs with le AI(tm)",
-# 	epilog="https://docugen.com"
+	epilog="https://github.com/turtlebasket/docugen"
-# )
+)
 
-# argparser.add_argument("file", required=True)
+argparser.add_argument("filename")
+argparser.add_argument("-o", dest="output directory",  help="directory to write docs to")
+argparser.add_argument("-m", dest="model", help="model to use to generate documentation", choices=["chatgpt"])
+argparser.add_argument("-f", dest="format", help="formatting of output documentation", choices=["md"])
+args = argparser.parse_args()
 
 file_path = os.path.dirname(os.path.realpath(__file__))
 with open(f"{file_path}/config.yaml", "r") as file:
     config = yaml.load(file, Loader=CLoader)
 
-infile = open(argv[1], "r")
+infile = open(args.filename, "r")
-# print(infile)
 
-# top level functions only for now
+# python & top level functions only for now
 functions = py.find_toplevel_funcs(infile)
 
 print(f"Found {len(functions)} functions.")
 
+if len(functions) >= 3:
+	print(f"Grab a cup of coffee, this could take a while.")
+
 doc_prompt_head = "Explain what this function does in one short sentence, then give example code that uses the function:\n"
 # doc_prompt_example = "Return only example code using this function:\n"
 
-# for function in functions:
-# 	print(function["content"])
-# 	print("------------------")
-
 bot = Chatbot({
     'Authorization': config['Authorization'],
     'session_token': config['session_token'],
@@ -42,12 +46,12 @@ bot = Chatbot({
 
 bot.refresh_session()
 
-with open(f"{argv[1].split('.')[0]}-doc.md", "w") as outfile:
+with open(f"{args.filename.split('.')[0]}-doc.md", "w") as outfile:
 	outfile.write(f"# Documentation for `{argv[1]}`\n\n")
 	for function in functions:
 		head_ask = doc_prompt_head + function["content"]
 		resp = bot.get_chat_response(head_ask, output="text")
 		print(f'Generated documentation for {function["head"]}.')
 		# append results to doc
-		output = f"## `{function['head']}`\n" + resp['message'] + "\n\n"
+		output = f"### `{function['head']}`\n" + fmtutil.highlight_multiline_code_md(resp['message'], "python") + "\n\n"
 		outfile.write(output)

example/example_code-doc.md

@@ -1,101 +1,94 @@
 # Documentation for `example/example_code.py`
 
-## `remove_dirt(image)`
+### `remove_dirt(image)`
-This function removes small elements from an image using area closing morphological operation.
+The `remove_dirt` function removes small objects from the input image using area closing. Here is an example of how to use the `remove_dirt` function:
 
-Example:
+```python
+import skimage.morphology as morphology
 
-```
+# Load an image using some library (e.g. Pillow, OpenCV, etc.)
-# Import the required module
+image = ...
-from skimage import morphology
 
-# Load the image
+# Remove small objects from the image
-image = skimage.io.imread('image.jpg')
-
-# Apply the function to remove small elements from the image
 image = remove_dirt(image)
-
-# Show the result
-skimage.io.imshow(image)
 ```
 
-## `calculate_area(countour)`
-This function calculates the area of a contour in an image.
 
-Example:
+### `calculate_area(countour)`
+The `calculate_area` function calculates the area of a contour in an image using OpenCV. Here is an example of how to use the `calculate_area` function:
 
-```
+```python
-# Import the required modules
-import cv2 as cv
 import numpy as np
+import cv2 as cv
 
-# Load the image and find the contours
+# Load an image using some library (e.g. Pillow, OpenCV, etc.)
-image = cv.imread('image.jpg')
+image = ...
-contours, _ = cv.findContours(image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
 
-# Iterate over the contours and calculate their areas
+# Find contours in the image using OpenCV
+contours = cv.findContours(image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE)
+
+# Calculate the area of each contour
 for contour in contours:
     area = calculate_area(contour)
-    print('Area of contour:', area)
+    print(area)
 ```
 
-## `center_of_mass(X)`
-This function calculates the center of mass of a set of points.
 
-Example:
+### `center_of_mass(X)`
+The `center_of_mass` function calculates the center of mass of a 2D shape defined by a set of points. Here is an example of how to use the `center_of_mass` function:
 
-```
+```python
 import numpy as np
 
-# Define a set of points
+# Define a set of points that define a shape
-X = np.array([[1,2], [3,4], [5,6]])
+X = np.array([[0,0], [0,1], [1,1], [1,0]])
 
-# Calculate the center of mass of the points
+# Calculate the center of mass of the shape
-center = center_of_mass(X)
+com = center_of_mass(X)
 
-# Print the result
+# Print the center of mass
-print('Center of mass:', center)
+print(com)
 ```
 
-The output will be `Center of mass: [3. 4.]`.
+In this example, the output would be `[0.5, 0.5]`, which is the center of the square defined by the points `X`.
 
-## `center_of_mass(X)`
-This function calculates the center of mass of a set of points.
 
-Example:
+### `center_of_mass(X)`
+The `center_of_mass` function calculates the center of mass of a 2D shape defined by a set of points. Here is an example of how to use the `center_of_mass` function:
 
-```
+```python
 import numpy as np
 
-# Define a set of points
+# Define a set of points that define a shape
-X = np.array([[1,2], [3,4], [5,6]])
+X = np.array([[0,0], [0,1], [1,1], [1,0]])
 
-# Calculate the center of mass of the points
+# Calculate the center of mass of the shape
-center = center_of_mass(X)
+com = center_of_mass(X)
 
-# Print the result
+# Print the center of mass
-print('Center of mass:', center)
+print(com)
 ```
 
-The output will be `Center of mass: [3. 4.]`.
+In this example, the output would be `[0.5, 0.5]`, which is the center of the square defined by the points `X`.
 
-## `rg_ratio_normalize(imgarr)`
-This function normalizes the temperature values in an image array using the RG ratio and a pyrometry calibration formula.
 
-Example:
+### `rg_ratio_normalize(imgarr)`
+The `rg_ratio_normalize` function applies a normalization function to the red and green channels of a 2D image, then applies a camera calibration formula to the resulting normalized values and returns the resulting image. Here is an example of how to use the `rg_ratio_normalize` function:
 
-```
+```python
-# Import the required modules
 import numpy as np
 
-# Load the image array
+# Load an image using some library (e.g. Pillow, OpenCV, etc.)
-imgarr = np.array(...)
+image = ...
 
-# Normalize the temperature values in the image
+# Convert the image to a NumPy array
+imgarr = np.array(image)
+
+# Apply the normalization and calibration to the image
 imgnew, tmin, tmax = rg_ratio_normalize(imgarr)
 
-# Print the resulting minimum and maximum temperature values
+# Print the minimum and maximum temperature values in the image
-print('Minimum temperature:', tmin)
+print(tmin, tmax)
-print('Maximum temperature:', tmax)
 ```
 
+

fmtutil/__init__.py

@@ -0,0 +1,29 @@
+import re
+
+ext_lang_map = {
+    "py": "python",
+    "java": "java",
+    "js": "javascript",
+    "cpp": "cpp",
+    "cxx": "cpp",
+    "hpp": "cpp",
+}
+
+
+def highlight_multiline_code_md(md_text: str, language_str: str) -> str:
+    """
+    Highlight markdown-embedded code blocks.
+    """
+    out = ""
+    in_code_block = False
+    for line in md_text.split("\n"):
+        if not in_code_block and re.search("^```(.*)$", line):
+            in_code_block = True
+            if line.strip() == "```":
+                line = f"```{language_str}"
+        elif in_code_block and line.strip() == "```":
+            in_code_block = False
+
+        out += line + "\n"
+
+    return out

parse/js.py

@@ -0,0 +1 @@
+# add later