luna/twitter-cli-cookiefile
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feat: add integration smoke tests

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CLI-level smoke tests using --yaml output against real Twitter API.
Default skipped via @pytest.mark.smoke marker + pyproject.toml addopts.
Run locally with: uv run pytest -m smoke -v
This commit is contained in:
jackwener
2026-03-10 22:26:46 +08:00
parent fa6255f2ee
commit 9cf74abd56
13 changed files with 562 additions and 193 deletions
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15
twitter_cli/filter.py
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@@ -8,8 +8,10 @@ from __future__ import annotations
from dataclasses import replace
import math
from typing import Any, Dict, List, Mapping, Optional, Sequence
from .config import _as_float, _as_int
from .models import Tweet
DEFAULT_WEIGHTS = {
"likes": 1.0,
@@ -20,8 +22,7 @@ DEFAULT_WEIGHTS = {
}
def score_tweet(tweet, weights=None):
# type: (Tweet, Optional[Dict[str, float]]) -> float
def score_tweet(tweet: Tweet, weights: Optional[Dict[str, float]] = None) -> float:
"""Calculate engagement score for a single tweet.
Formula:
@@ -45,8 +46,7 @@ def score_tweet(tweet, weights=None):
)
def filter_tweets(tweets, config):
# type: (Sequence[Tweet], Mapping[str, Any]) -> List[Tweet]
def filter_tweets(tweets: Sequence[Tweet], config: Mapping[str, Any]) -> List[Tweet]:
"""Filter and rank tweets according to config.
Config keys:
@@ -74,7 +74,7 @@ def filter_tweets(tweets, config):
scored = [replace(tweet, score=round(score_tweet(tweet, weights), 1)) for tweet in filtered]
# 4. Sort by score (descending)
scored.sort(key=lambda tweet: tweet.score, reverse=True)
scored.sort(key=lambda tweet: tweet.score or 0.0, reverse=True)
# 5. Apply filter mode
mode = str(config.get("mode", "topN"))
@@ -83,12 +83,11 @@ def filter_tweets(tweets, config):
return scored[:top_n]
if mode == "score":
min_score = _as_float(config.get("minScore"), 50.0)
return [tweet for tweet in scored if tweet.score >= min_score]
return [tweet for tweet in scored if (tweet.score or 0.0) >= min_score]
return scored
def _build_weights(raw_weights):
# type: (Mapping[str, Any]) -> Dict[str, float]
def _build_weights(raw_weights: Mapping[str, Any]) -> Dict[str, float]:
"""Merge custom weights with defaults and coerce to float."""
merged = {}
for key, default_value in DEFAULT_WEIGHTS.items():