mdtidy 0.4.1

A Python library for processing conversation data, creating Jupyter Notebooks, handling Google Drive uploads, and updating Google Sheets.

mdtidy

mdtidy is a Python library designed to process conversational AI outputs (specifically GPT and Gemini) into Jupyter Notebooks (.ipynb), while also counting code errors within the content. It's an intuitive tool for transforming raw markdown, code blocks, and conversation data into well-structured Jupyter notebooks, specifically tailored for reviewing AI-generated conversation data and integrating with Google Drive and Google Sheets for enhanced data management.

Features

• Markdown to Jupyter Notebook: Convert markdown content with embedded Python code blocks into a Jupyter Notebook file.
• Error Counting: Identify and count common Python error types within the content.
• Conversation Analysis: Supports parsing and formatting conversation data from both GPT and Gemini models.
• Google Drive Integration: Upload processed notebooks to Google Drive.
• Google Sheets Integration: Update Google Sheets with processed conversation data and error counts.

Installation

Install mdtidy using pip:

pip install mdtidy

Usage

Convert GPT Conversations to Notebook

This example shows how to process GPT conversation data into a structured Jupyter notebook.

from mdtidy.process_gpt_conversation import process_gpt_conversation

# Trigger the processing of a GPT conversation, prompting for required details
process_gpt_conversation()

Convert Gemini Conversations to Notebook

This example details processing Gemini model conversations into a notebook format, integrating error analysis.

from mdtidy.process_gemini_conversation import process_gemini_conversation

input_string_1 = """
# Insert the content of the first input string here, preserving its structure.
"""

input_string_2 = """
# Insert the content of the second input string here, preserving its structure.
"""

# Add more input strings as needed.

# Prepare your input strings for the Gemini model conversation
input_strings = [input_string_1, input_string_2]  # Extend this list as needed.
process_gemini_conversation(input_strings)

Count Code Errors

This function identifies and counts typical Python errors within given content.

from mdtidy.process_gemini_conversation import count_code_errors

# Example content with typical Python errors
input_strings_with_errors = [
    """
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'variable' is not defined

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for +: 'int' and 'str'
"""
]

# Count and display code errors
error_counts = count_code_errors(input_strings_with_errors)
print(error_counts)

Update Google Sheets

This example shows how to update Google Sheets with processed conversation data and error counts. The first time you run this function, you will be prompted to provide the name of the Google Sheet and the path to your Google Sheets API credentials. These details will be saved for future use, but you will have the option to update them if needed.

from mdtidy.update_google_sheets import update_google_sheet

# Update the Google Sheet
update_google_sheet()

The update_google_sheet function will automatically use the default directory comp_ana_json for JSON files and the stored Google Sheets credentials and spreadsheet name from the configuration file (google_sheets.ini).

License

This project is licensed under the MIT License - see the LICENSE file for details.