snooper-ai 0.0.2

Debug your Python code with AI assistance

snooper-ai 🔍

snooper-ai is a simple fork of PySnooper. It sends the entire execution trace (variable values you'll typically examine in a debugger) to an LLM for debugging, so it fully understands what happened in your code.

Disclaimer: This was implemented simply and may not be very robust. Feel free to submit issues.

Usage:

1. Install

pip install snooper-ai

2. Store your LLM api key (either anthropic or openai):

ANTHROPIC_API_KEY=xxx
OPENAI_API_KEY=xxx

3. Add a decorator to the function you want to inquire:

from snooper_ai import snoop

@snoop()
def number_to_bits(number):
    if number:
        bits = []
        while number:
            number, remainder = divmod(number, 2)
            bits.insert(0, remainder)
        return bits
    else:
        return [0]

number_to_bits(6)

4. Run the file

snoop run file.py

5. Tell the LLM what you're confused about:

╭─────────────────────────────────────────────────╮
│  🔍 snooper-ai: Debug your Python code with AI  │
╰─────────────────────────────────────────────────╯

What would you like to know about the code execution? (e.g. Error messages, unexpected behavior, etc.):

6. Run it and get a detailed explanation!

7. To choose which model you're using (This will be saved in your pyproject.toml):

snoop config

More info

This is what gets sent to the LLM. Refer to the original PySnooper for more details

New var:....... i = 9
New var:....... lst = [681, 267, 74, 832, 284, 678, ...]
09:37:35.881721 line        10         lower = min(lst)
New var:....... lower = 74
09:37:35.882137 line        11         upper = max(lst)
New var:....... upper = 832
09:37:35.882304 line        12         mid = (lower + upper) / 2
74 453.0 832
New var:....... mid = 453.0
09:37:35.882486 line        13         print(lower, mid, upper)
Elapsed time: 00:00:00.000344