fix python parsing, update deps

python parsing was broken & openai changed their internal API

Pipfile.lock

@@ -299,11 +299,11 @@
         },
         "revchatgpt": {
             "hashes": [
-                "sha256:36860b0297d5e846172c2fcfcdf27b4f19b80eac299b4e00c93a5dccf27f18c1",
+                "sha256:6217e51914300123bc01f1519de68eeece8bdca0aa68bca71e540d293cbbc23c",
-                "sha256:3fce7e348a8817e4335130a9ab2e523633c8c49cad1d2ca577571aeaa4a00115"
+                "sha256:fcebca9815761b6fcb5abbfb8b28bbee810d2a01fc148c19429066818a29d933"
             ],
             "index": "pypi",
-            "version": "==0.0.23.5"
+            "version": "==0.0.30"
         },
         "six": {
             "hashes": [

docugen-chatgpt.py

@@ -1,9 +1,10 @@
+import json
 import yaml
 from yaml import CLoader, CDumper
 from revChatGPT.revChatGPT import Chatbot
 import os
 from sys import argv
-import re
+from parse import py
 
 # from argparse import ArgumentParser
 
@@ -23,39 +24,20 @@ infile = open(argv[1], "r")
 # print(infile)
 
 # top level functions only for now
-functions = []
+functions = py.find_toplevel_funcs(infile)
-function_head = ""
-function_content = ""
-function_body_indent_level = 0
-for line in infile:
-	curr_indent_level = len(line) - len(line.lstrip())
-	function_found = re.search('^def(\s+)(.*)\(.*\)(\s*):$', line.strip())
-	if function_found and len(function_head) == 0:
-		function_head = re.search("(\s+)(.*)\(.*\)", line).string.replace('def', '').replace('):', ')').strip()
-		function_content = line
-		function_body_indent_level = curr_indent_level + 1
-
-	elif len(function_head) > 0 and (curr_indent_level < function_body_indent_level):
-		functions.append({
-			"head": function_head,
-			"content": function_content
-		})
-		if function_found:
-			function_head = ""
-			function_content = ""
-
-	elif len(function_head) > 0 and curr_indent_level >= function_body_indent_level:
-		function_content += line
 
 print(f"Found {len(functions)} functions.")
 
 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'],
-    # 'session': config['password']
 }, conversation_id=None)
 
 bot.refresh_session()

example/example_code-doc.md

@@ -1,58 +1,101 @@
-# Documentation for `example_code.py`
+# Documentation for `example/example_code.py`
 
 ## `remove_dirt(image)`
-This function removes small dirt and noise from a binary image by closing small holes and removing small objects.
+This function removes small elements from an image using area closing morphological operation.
 
-Here is an example of how to use the function:
+Example:
 
 ```
-import skimage.morphology as morphology
+# Import the required module
-from skimage import data
+from skimage import morphology
 
-# Load a binary image
+# Load the image
-image = data.coins() > 100
+image = skimage.io.imread('image.jpg')
 
-# Remove dirt from the image
+# Apply the function to remove small elements from the image
-cleaned_image = remove_dirt(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:
+
+```
+# Import the required modules
+import cv2 as cv
+import numpy as np
+
+# Load the image and find the contours
+image = cv.imread('image.jpg')
+contours, _ = cv.findContours(image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+
+# Iterate over the contours and calculate their areas
+for contour in contours:
+    area = calculate_area(contour)
+    print('Area of contour:', area)
 ```
 
 ## `center_of_mass(X)`
-This function calculates the center of mass of a 2D shape defined by a set of points.
+This function calculates the center of mass of a set of points.
 
-Here is an example of how to use the function:
+Example:
 
 ```
 import numpy as np
 
-# Define a set of points that define a shape
+# Define a set of points
-X = np.array([[0, 0], [0, 1], [1, 1], [1, 0]])
+X = np.array([[1,2], [3,4], [5,6]])
 
-# Calculate the center of mass of the shape
+# Calculate the center of mass of the points
-center_of_mass = center_of_mass(X)
+center = center_of_mass(X)
 
-# Print the center of mass
+# Print the result
-print(center_of_mass)
+print('Center of mass:', center)
 ```
 
-The output will be `[0.5, 0.5]`, which is the coordinates of the center of the square defined by the points `X`.
+The output will be `Center of mass: [3. 4.]`.
 
 ## `center_of_mass(X)`
-This function calculates the center of mass of a 2D shape defined by a set of points.
+This function calculates the center of mass of a set of points.
 
-Here is an example of how to use the function:
+Example:
 
 ```
 import numpy as np
 
-# Define a set of points that define a shape
+# Define a set of points
-X = np.array([[0, 0], [0, 1], [1, 1], [1, 0]])
+X = np.array([[1,2], [3,4], [5,6]])
 
-# Calculate the center of mass of the shape
+# Calculate the center of mass of the points
-center_of_mass = center_of_mass(X)
+center = center_of_mass(X)
 
-# Print the center of mass
+# Print the result
-print(center_of_mass)
+print('Center of mass:', center)
 ```
 
-The output will be `[0.5, 0.5]`, which is the coordinates of the center of the square defined by the points `X`.
+The output will be `Center of mass: [3. 4.]`.
+
+## `rg_ratio_normalize(imgarr)`
+This function normalizes the temperature values in an image array using the RG ratio and a pyrometry calibration formula.
+
+Example:
+
+```
+# Import the required modules
+import numpy as np
+
+# Load the image array
+imgarr = np.array(...)
+
+# Normalize the temperature values in the image
+imgnew, tmin, tmax = rg_ratio_normalize(imgarr)
+
+# Print the resulting minimum and maximum temperature values
+print('Minimum temperature:', tmin)
+print('Maximum temperature:', tmax)
+```
 

example/example_code.py

@@ -4,12 +4,14 @@ def remove_dirt(image):
 
     return image
 
+
 def calculate_area(countour):
     c = np.expand_dims(countour.astype(np.float32), 1)
     c = cv.UMat(c)
    
     return cv.contourArea(c)
 
+
 def center_of_mass(X):
     x = X[:,0]
     y = X[:,1]
@@ -22,6 +24,7 @@ def center_of_mass(X):
 
 img = remove_dirt(thresh_gray)
 
+
 def rg_ratio_normalize(imgarr):
     # set max & min to most extreme values, 
     # work up & down respectively from there

parse/py.py

@@ -0,0 +1,30 @@
+import re
+
+def find_toplevel_funcs(lines):
+    functions = []
+    function_head = ""
+    function_content = ""
+    function_body_indent_level = 0
+
+    for line in lines:
+        curr_indent_level = len(line) - len(line.lstrip())
+        function_found = re.search('^def(\s+)(.*)\(.*\)(\s*):(\s*)$', line.strip())
+
+        if len(function_head.strip()) > 0 and (curr_indent_level < function_body_indent_level):
+            functions.append({
+                "head": function_head,
+                "content": function_content
+            })
+            if function_found:
+                function_head = ""
+                function_content = ""
+
+        if function_found and len(function_head) == 0:
+            function_head = re.search("(\s+)(.*)\(.*\)", line).string.replace('def', '').replace('):', ')').strip()
+            function_content = line
+            function_body_indent_level = curr_indent_level + 1
+
+        elif not function_found and len(function_head.strip()) > 0 and curr_indent_level >= function_body_indent_level:
+            function_content += line
+
+    return functions