dsa-tracker 0.3.0

Log DSA prep solves from a Colab/Jupyter notebook to your DSA Tracker instance.

dsa-tracker

Tiny Python client for DSA Tracker. Log a solve from a Colab/Jupyter notebook in one call — same SM-2 scheduling and GitHub auto-push you get from the web app.

Install

pip install dsa-tracker

Get an API key

In your DSA Tracker dashboard → Settings → API keys → Generate new key. The plaintext value is shown exactly once — copy it and treat it like a password. You can name keys per device/notebook so revoking one doesn't break the others.

Use it

from dsa_tracker import Tracker

tracker = Tracker(
    api_url="https://your-dsa-tracker.com",
    api_key="dsa_xxxxxxxxxxxxxxxxxxxxxx",
)

# Solve the problem like you always do
def two_sum(nums, target):
    seen = {}
    for i, n in enumerate(nums):
        if target - n in seen:
            return [seen[target - n], i]
        seen[n] = i

# Log it without leaving the notebook
import inspect

result = tracker.log(
    title="Two Sum",
    confidence=4,                            # 1 (Forgot) → 5 (Easy)
    code=inspect.getsource(two_sum),
    language="python",
    difficulty="easy",
    topics=["Array", "HashMap"],
    pattern="Hash Lookup",
    time_complexity="O(n)",
    space_complexity="O(n)",
    playlist="leetcode-75",                  # optional: push to this playlist's GitHub config
)

print(f"→ next review: {result['next_review_at']} · status: {result['status']}")
if result.get('git_push_queued'):
    print("→ pushed to GitHub via playlist 'leetcode-75'")
elif result.get('git_push_skip_reason'):
    print(f"→ no GitHub push: {result['git_push_skip_reason']}")

What happens on the server

1. Two Sum is looked up by slug. Created if new, otherwise reused.
2. A new Solution row captures your code, language, complexities.
3. A new Review row runs through SM-2 with your confidence rating.
4. problem.next_review_at and problem.status are updated.
5. If you have GitHub auto-push enabled, the solution is committed to your repo in the background.

The return value tells you exactly what happened:

{
    'problem_id': 42,
    'solution_id': 87,
    'review_id': 105,
    'created': True,                # False if you've logged this title before
    'next_review_at': '2026-05-27',
    'status': 'learning',
    'reps': 1,
    'interval_days': 1,
}

All parameters

Required	Optional
title, confidence	code, language, difficulty, topics, pattern, platform, problem_url, approach, time_complexity, space_complexity, notes

Defaults: language="python", difficulty="medium", approach="optimal". Anything you omit just isn't sent.

Errors

The client raises TrackerError on any non-2xx response with the server's reason attached:

from dsa_tracker import TrackerError

try:
    tracker.log(title="X", confidence=4)
except TrackerError as e:
    print(f"Couldn't log: {e}")

Common cases: 401 (bad/revoked API key), 422 (missing required field), connection timeout.

Context manager

For scripts that make many calls, use it as a context manager to clean up the underlying connection pool:

with Tracker(api_url=..., api_key=...) as tracker:
    for problem in queue:
        tracker.log(title=problem.name, confidence=problem.rating, code=problem.code)

License

MIT.