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skill-seekers-reference/tests/test_video_scraper.py
YusufKaraaslanSpyke 62071c4aa9 feat: add video tutorial scraping pipeline with per-panel OCR and AI enhancement 
Add complete video tutorial extraction system that converts YouTube videos
and local video files into AI-consumable skills. The pipeline extracts
transcripts, performs visual OCR on code editor panels independently,
tracks code evolution across frames, and generates structured SKILL.md output.

Key features:
- Video metadata extraction (YouTube, local files, playlists)
- Multi-source transcript extraction (YouTube API, yt-dlp, Whisper fallback)
- Chapter-based and time-window segmentation
- Visual extraction: keyframe detection, frame classification, panel detection
- Per-panel sub-section OCR (each IDE panel OCR'd independently)
- Parallel OCR with ThreadPoolExecutor for multi-panel frames
- Narrow panel filtering (300px min width) to skip UI chrome
- Text block tracking with spatial panel position matching
- Code timeline with edit tracking across frames
- Audio-visual alignment (code + narrator pairs)
- Video-specific AI enhancement prompt for OCR denoising and code reconstruction
- video-tutorial.yaml workflow with 4 stages (OCR cleanup, language detection,
  tutorial synthesis, skill polish)
- CLI integration: skill-seekers video --url/--video-file/--playlist
- MCP tool: scrape_video for automation
- 161 tests passing

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-27 23:10:19 +03:00

3120 lines
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#!/usr/bin/env python3
"""
Tests for Video Scraper (cli/video_scraper.py)
Tests cover:
- Data models (enums, dataclasses, serialization)
- Metadata extraction (YouTube URL parsing, video ID extraction)
- Transcript extraction (SRT/VTT parsing, fallback chain)
- Segmentation (chapter-based, time-window)
- Full pipeline (VideoToSkillConverter)
- Source detection (SourceDetector video patterns)
- CLI argument parsing
- Create command routing
"""
import os
import shutil
import tempfile
import unittest
# Video-specific deps are optional
try:
import yt_dlp # noqa: F401
HAS_YTDLP = True
except ImportError:
HAS_YTDLP = False
try:
from youtube_transcript_api import YouTubeTranscriptApi # noqa: F401
HAS_YOUTUBE_TRANSCRIPT = True
except ImportError:
HAS_YOUTUBE_TRANSCRIPT = False
# =============================================================================
# Helper: Build mock data
# =============================================================================
def _make_sample_video_info():
"""Build a minimal VideoInfo dict for testing."""
from skill_seekers.cli.video_models import (
TranscriptSource,
VideoInfo,
VideoSourceType,
Chapter,
)
return VideoInfo(
video_id="abc123def45",
source_type=VideoSourceType.YOUTUBE,
source_url="https://www.youtube.com/watch?v=abc123def45",
title="Test Video Tutorial",
description="A test video for unit testing.",
duration=600.0,
upload_date="2026-01-15",
language="en",
channel_name="Test Channel",
channel_url="https://youtube.com/@testchannel",
view_count=100000,
like_count=5000,
tags=["test", "tutorial", "python"],
categories=["Education"],
chapters=[
Chapter(title="Intro", start_time=0.0, end_time=60.0),
Chapter(title="Setup", start_time=60.0, end_time=180.0),
Chapter(title="Main Content", start_time=180.0, end_time=500.0),
Chapter(title="Wrap Up", start_time=500.0, end_time=600.0),
],
transcript_source=TranscriptSource.YOUTUBE_MANUAL,
)
def _make_sample_transcript_segments():
"""Build a list of TranscriptSegment objects for testing."""
from skill_seekers.cli.video_models import TranscriptSegment, TranscriptSource
return [
TranscriptSegment(
text="Welcome to this tutorial.",
start=0.0,
end=3.0,
confidence=1.0,
source=TranscriptSource.YOUTUBE_MANUAL,
),
TranscriptSegment(
text="Today we'll learn about Python.",
start=3.0,
end=6.0,
confidence=1.0,
source=TranscriptSource.YOUTUBE_MANUAL,
),
TranscriptSegment(
text="Let's set up our environment.",
start=60.0,
end=65.0,
confidence=1.0,
source=TranscriptSource.YOUTUBE_MANUAL,
),
TranscriptSegment(
text="First install Python from python.org.",
start=65.0,
end=70.0,
confidence=1.0,
source=TranscriptSource.YOUTUBE_MANUAL,
),
TranscriptSegment(
text="Now let's write some code.",
start=180.0,
end=185.0,
confidence=1.0,
source=TranscriptSource.YOUTUBE_MANUAL,
),
TranscriptSegment(
text="def hello(): return 'world'",
start=185.0,
end=190.0,
confidence=0.95,
source=TranscriptSource.YOUTUBE_MANUAL,
),
TranscriptSegment(
text="Thanks for watching, subscribe for more.",
start=500.0,
end=510.0,
confidence=1.0,
source=TranscriptSource.YOUTUBE_MANUAL,
),
]
def _make_sample_srt_content():
"""Build sample SRT subtitle content."""
return """1
00:00:00,000 --> 00:00:03,000
Welcome to this tutorial.
2
00:00:03,000 --> 00:00:06,000
Today we'll learn about Python.
3
00:01:00,000 --> 00:01:05,000
Let's set up our environment.
"""
def _make_sample_vtt_content():
"""Build sample WebVTT subtitle content."""
return """WEBVTT
00:00:00.000 --> 00:00:03.000
Welcome to this tutorial.
00:00:03.000 --> 00:00:06.000
Today we'll learn about Python.
00:01:00.000 --> 00:01:05.000
Let's set up our environment.
"""
# =============================================================================
# Test: Data Models
# =============================================================================
class TestVideoModels(unittest.TestCase):
"""Test video data models (enums + dataclasses)."""
def test_video_source_type_enum(self):
from skill_seekers.cli.video_models import VideoSourceType
self.assertEqual(VideoSourceType.YOUTUBE.value, "youtube")
self.assertEqual(VideoSourceType.LOCAL_FILE.value, "local_file")
self.assertEqual(VideoSourceType.VIMEO.value, "vimeo")
def test_transcript_source_enum(self):
from skill_seekers.cli.video_models import TranscriptSource
self.assertEqual(TranscriptSource.YOUTUBE_MANUAL.value, "youtube_manual")
self.assertEqual(TranscriptSource.WHISPER.value, "whisper")
self.assertEqual(TranscriptSource.NONE.value, "none")
def test_segment_content_type_enum(self):
from skill_seekers.cli.video_models import SegmentContentType
self.assertEqual(SegmentContentType.LIVE_CODING.value, "live_coding")
self.assertEqual(SegmentContentType.EXPLANATION.value, "explanation")
def test_chapter_serialization(self):
from skill_seekers.cli.video_models import Chapter
ch = Chapter(title="Intro", start_time=0.0, end_time=60.0)
d = ch.to_dict()
self.assertEqual(d["title"], "Intro")
self.assertEqual(d["start_time"], 0.0)
self.assertEqual(d["end_time"], 60.0)
ch2 = Chapter.from_dict(d)
self.assertEqual(ch2.title, "Intro")
self.assertAlmostEqual(ch2.duration, 60.0)
def test_transcript_segment_serialization(self):
from skill_seekers.cli.video_models import TranscriptSegment, TranscriptSource
seg = TranscriptSegment(
text="Hello world",
start=0.0,
end=2.5,
confidence=0.95,
source=TranscriptSource.YOUTUBE_MANUAL,
)
d = seg.to_dict()
self.assertEqual(d["text"], "Hello world")
self.assertEqual(d["source"], "youtube_manual")
seg2 = TranscriptSegment.from_dict(d)
self.assertEqual(seg2.text, "Hello world")
self.assertEqual(seg2.source, TranscriptSource.YOUTUBE_MANUAL)
def test_video_segment_serialization(self):
from skill_seekers.cli.video_models import SegmentContentType, VideoSegment
seg = VideoSegment(
index=0,
start_time=0.0,
end_time=60.0,
duration=60.0,
transcript="Hello world",
chapter_title="Intro",
content_type=SegmentContentType.INTRO,
confidence=0.9,
)
d = seg.to_dict()
self.assertEqual(d["chapter_title"], "Intro")
self.assertEqual(d["content_type"], "intro")
seg2 = VideoSegment.from_dict(d)
self.assertEqual(seg2.chapter_title, "Intro")
self.assertEqual(seg2.content_type, SegmentContentType.INTRO)
def test_video_segment_timestamp_display(self):
from skill_seekers.cli.video_models import VideoSegment
seg = VideoSegment(index=0, start_time=330.0, end_time=495.0, duration=165.0)
self.assertEqual(seg.timestamp_display, "05:30 - 08:15")
def test_video_segment_timestamp_display_hours(self):
from skill_seekers.cli.video_models import VideoSegment
seg = VideoSegment(index=0, start_time=3661.0, end_time=7200.0, duration=3539.0)
self.assertIn("1:", seg.timestamp_display)
def test_video_info_serialization(self):
info = _make_sample_video_info()
d = info.to_dict()
self.assertEqual(d["video_id"], "abc123def45")
self.assertEqual(d["source_type"], "youtube")
self.assertEqual(len(d["chapters"]), 4)
from skill_seekers.cli.video_models import VideoInfo
info2 = VideoInfo.from_dict(d)
self.assertEqual(info2.video_id, "abc123def45")
self.assertEqual(len(info2.chapters), 4)
def test_video_source_config_validation(self):
from skill_seekers.cli.video_models import VideoSourceConfig
# No source specified
config = VideoSourceConfig()
errors = config.validate()
self.assertTrue(len(errors) > 0)
# Valid config
config = VideoSourceConfig(url="https://youtube.com/watch?v=test")
errors = config.validate()
self.assertEqual(len(errors), 0)
# Multiple sources
config = VideoSourceConfig(url="test", path="test.mp4")
errors = config.validate()
self.assertTrue(len(errors) > 0)
def test_video_scraper_result_serialization(self):
from skill_seekers.cli.video_models import VideoScraperResult
result = VideoScraperResult(
total_duration_seconds=600.0,
total_segments=4,
warnings=["Test warning"],
)
d = result.to_dict()
self.assertEqual(d["total_segments"], 4)
self.assertEqual(d["warnings"], ["Test warning"])
result2 = VideoScraperResult.from_dict(d)
self.assertEqual(result2.total_segments, 4)
def test_word_timestamp_serialization(self):
from skill_seekers.cli.video_models import WordTimestamp
wt = WordTimestamp(word="hello", start=0.0, end=0.5, probability=0.95)
d = wt.to_dict()
self.assertEqual(d["word"], "hello")
wt2 = WordTimestamp.from_dict(d)
self.assertEqual(wt2.word, "hello")
def test_code_block_serialization(self):
from skill_seekers.cli.video_models import CodeBlock, CodeContext
cb = CodeBlock(
code="print('hi')", language="python", context=CodeContext.EDITOR, confidence=0.9
)
d = cb.to_dict()
self.assertEqual(d["context"], "editor")
cb2 = CodeBlock.from_dict(d)
self.assertEqual(cb2.context, CodeContext.EDITOR)
# =============================================================================
# Test: Metadata
# =============================================================================
class TestVideoMetadata(unittest.TestCase):
"""Test video metadata extraction functions."""
def test_extract_video_id_standard_url(self):
from skill_seekers.cli.video_metadata import extract_video_id
self.assertEqual(
extract_video_id("https://www.youtube.com/watch?v=dQw4w9WgXcQ"),
"dQw4w9WgXcQ",
)
def test_extract_video_id_short_url(self):
from skill_seekers.cli.video_metadata import extract_video_id
self.assertEqual(
extract_video_id("https://youtu.be/dQw4w9WgXcQ"),
"dQw4w9WgXcQ",
)
def test_extract_video_id_embed_url(self):
from skill_seekers.cli.video_metadata import extract_video_id
self.assertEqual(
extract_video_id("https://www.youtube.com/embed/dQw4w9WgXcQ"),
"dQw4w9WgXcQ",
)
def test_extract_video_id_shorts_url(self):
from skill_seekers.cli.video_metadata import extract_video_id
self.assertEqual(
extract_video_id("https://www.youtube.com/shorts/dQw4w9WgXcQ"),
"dQw4w9WgXcQ",
)
def test_extract_video_id_not_youtube(self):
from skill_seekers.cli.video_metadata import extract_video_id
self.assertIsNone(extract_video_id("https://vimeo.com/123456"))
self.assertIsNone(extract_video_id("https://example.com"))
def test_detect_video_source_type_youtube(self):
from skill_seekers.cli.video_metadata import detect_video_source_type
from skill_seekers.cli.video_models import VideoSourceType
self.assertEqual(
detect_video_source_type("https://www.youtube.com/watch?v=test"),
VideoSourceType.YOUTUBE,
)
self.assertEqual(
detect_video_source_type("https://youtu.be/test"),
VideoSourceType.YOUTUBE,
)
def test_detect_video_source_type_vimeo(self):
from skill_seekers.cli.video_metadata import detect_video_source_type
from skill_seekers.cli.video_models import VideoSourceType
self.assertEqual(
detect_video_source_type("https://vimeo.com/123456"),
VideoSourceType.VIMEO,
)
def test_extract_local_metadata(self):
from skill_seekers.cli.video_metadata import extract_local_metadata
# Create a temp file
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp:
tmp_name = tmp.name
try:
info = extract_local_metadata(tmp_name)
self.assertEqual(info.source_type.value, "local_file")
self.assertIsNotNone(info.video_id)
self.assertIsNotNone(info.file_path)
finally:
os.unlink(tmp_name)
# =============================================================================
# Test: Transcript
# =============================================================================
class TestVideoTranscript(unittest.TestCase):
"""Test transcript extraction functions."""
def test_parse_srt(self):
from skill_seekers.cli.video_transcript import parse_srt
with tempfile.NamedTemporaryFile(
mode="w", suffix=".srt", delete=False, encoding="utf-8"
) as tmp:
tmp.write(_make_sample_srt_content())
tmp_name = tmp.name
try:
segments = parse_srt(tmp_name)
self.assertEqual(len(segments), 3)
self.assertEqual(segments[0].text, "Welcome to this tutorial.")
self.assertAlmostEqual(segments[0].start, 0.0)
self.assertAlmostEqual(segments[0].end, 3.0)
self.assertEqual(segments[0].source.value, "subtitle_file")
finally:
os.unlink(tmp_name)
def test_parse_vtt(self):
from skill_seekers.cli.video_transcript import parse_vtt
with tempfile.NamedTemporaryFile(
mode="w", suffix=".vtt", delete=False, encoding="utf-8"
) as tmp:
tmp.write(_make_sample_vtt_content())
tmp_name = tmp.name
try:
segments = parse_vtt(tmp_name)
self.assertEqual(len(segments), 3)
self.assertEqual(segments[0].text, "Welcome to this tutorial.")
self.assertAlmostEqual(segments[2].start, 60.0)
finally:
os.unlink(tmp_name)
def test_parse_srt_with_html_tags(self):
from skill_seekers.cli.video_transcript import parse_srt
content = """1
00:00:00,000 --> 00:00:03,000
<b>Bold text</b> and <i>italic</i>
"""
with tempfile.NamedTemporaryFile(
mode="w", suffix=".srt", delete=False, encoding="utf-8"
) as tmp:
tmp.write(content)
tmp_name = tmp.name
try:
segments = parse_srt(tmp_name)
self.assertEqual(len(segments), 1)
self.assertEqual(segments[0].text, "Bold text and italic")
finally:
os.unlink(tmp_name)
def test_whisper_stub_raises(self):
from skill_seekers.cli.video_transcript import transcribe_with_whisper, HAS_WHISPER
if not HAS_WHISPER:
with self.assertRaises(RuntimeError) as ctx:
transcribe_with_whisper("test.wav")
self.assertIn("faster-whisper", str(ctx.exception))
def test_get_transcript_fallback_to_subtitle(self):
"""Test that get_transcript falls back to subtitle files."""
from skill_seekers.cli.video_transcript import get_transcript
from skill_seekers.cli.video_models import (
TranscriptSource,
VideoInfo,
VideoSourceConfig,
VideoSourceType,
)
tmp_dir = tempfile.mkdtemp()
try:
# Create a fake video file and matching SRT
video_path = os.path.join(tmp_dir, "test.mp4")
srt_path = os.path.join(tmp_dir, "test.srt")
with open(video_path, "w") as f:
f.write("fake")
with open(srt_path, "w", encoding="utf-8") as f:
f.write(_make_sample_srt_content())
video_info = VideoInfo(
video_id="local123",
source_type=VideoSourceType.LOCAL_FILE,
file_path=video_path,
)
config = VideoSourceConfig()
segments, source = get_transcript(video_info, config)
self.assertEqual(source, TranscriptSource.SUBTITLE_FILE)
self.assertEqual(len(segments), 3)
finally:
shutil.rmtree(tmp_dir)
# =============================================================================
# Test: Segmenter
# =============================================================================
class TestVideoSegmenter(unittest.TestCase):
"""Test video segmentation."""
def test_segment_by_chapters(self):
from skill_seekers.cli.video_segmenter import segment_by_chapters
video_info = _make_sample_video_info()
transcript = _make_sample_transcript_segments()
segments = segment_by_chapters(video_info, transcript)
self.assertEqual(len(segments), 4)
self.assertEqual(segments[0].chapter_title, "Intro")
self.assertEqual(segments[1].chapter_title, "Setup")
self.assertIn("Welcome", segments[0].transcript)
def test_segment_by_time_window(self):
from skill_seekers.cli.video_segmenter import segment_by_time_window
video_info = _make_sample_video_info()
transcript = _make_sample_transcript_segments()
segments = segment_by_time_window(video_info, transcript, window_seconds=300.0)
# With 600s duration and 300s windows, expect 2 segments
self.assertTrue(len(segments) >= 1)
self.assertIsNone(segments[0].chapter_title)
def test_segment_video_uses_chapters(self):
from skill_seekers.cli.video_segmenter import segment_video
from skill_seekers.cli.video_models import VideoSourceConfig
video_info = _make_sample_video_info()
transcript = _make_sample_transcript_segments()
config = VideoSourceConfig()
segments = segment_video(video_info, transcript, config)
# Should use chapters since they're available
self.assertEqual(len(segments), 4)
self.assertEqual(segments[0].chapter_title, "Intro")
def test_segment_video_fallback_to_time_window(self):
from skill_seekers.cli.video_segmenter import segment_video
from skill_seekers.cli.video_models import VideoInfo, VideoSourceConfig, VideoSourceType
video_info = VideoInfo(
video_id="no_chapters",
source_type=VideoSourceType.YOUTUBE,
duration=300.0,
)
transcript = _make_sample_transcript_segments()
config = VideoSourceConfig(time_window_seconds=120.0)
segments = segment_video(video_info, transcript, config)
self.assertTrue(len(segments) >= 1)
# No chapters, so chapter_title should be None
for seg in segments:
self.assertIsNone(seg.chapter_title)
def test_segment_content_type_classification(self):
from skill_seekers.cli.video_segmenter import _classify_content_type
from skill_seekers.cli.video_models import SegmentContentType
self.assertEqual(
_classify_content_type("Welcome to this tutorial, today we"),
SegmentContentType.INTRO,
)
self.assertEqual(
_classify_content_type("import os\ndef process_data(): return result"),
SegmentContentType.LIVE_CODING,
)
self.assertEqual(
_classify_content_type("thanks for watching subscribe for more"),
SegmentContentType.OUTRO,
)
# =============================================================================
# Test: Source Detection
# =============================================================================
class TestVideoSourceDetection(unittest.TestCase):
"""Test SourceDetector recognizes video URLs and file extensions."""
def test_detect_youtube_url(self):
from skill_seekers.cli.source_detector import SourceDetector
info = SourceDetector.detect("https://www.youtube.com/watch?v=dQw4w9WgXcQ")
self.assertEqual(info.type, "video")
self.assertEqual(info.parsed["source_kind"], "url")
def test_detect_youtube_short_url(self):
from skill_seekers.cli.source_detector import SourceDetector
info = SourceDetector.detect("https://youtu.be/dQw4w9WgXcQ")
self.assertEqual(info.type, "video")
def test_detect_youtube_playlist(self):
from skill_seekers.cli.source_detector import SourceDetector
info = SourceDetector.detect("https://www.youtube.com/playlist?list=PLtest123")
self.assertEqual(info.type, "video")
self.assertEqual(info.suggested_name, "youtube_playlist")
def test_detect_youtube_channel(self):
from skill_seekers.cli.source_detector import SourceDetector
info = SourceDetector.detect("https://www.youtube.com/@testchannel")
self.assertEqual(info.type, "video")
self.assertEqual(info.suggested_name, "youtube_channel")
def test_detect_vimeo_url(self):
from skill_seekers.cli.source_detector import SourceDetector
info = SourceDetector.detect("https://vimeo.com/123456789")
self.assertEqual(info.type, "video")
self.assertEqual(info.suggested_name, "vimeo_video")
def test_detect_mp4_file(self):
from skill_seekers.cli.source_detector import SourceDetector
info = SourceDetector.detect("recording.mp4")
self.assertEqual(info.type, "video")
self.assertEqual(info.suggested_name, "recording")
self.assertEqual(info.parsed["source_kind"], "file")
def test_detect_mkv_file(self):
from skill_seekers.cli.source_detector import SourceDetector
info = SourceDetector.detect("tutorial.mkv")
self.assertEqual(info.type, "video")
def test_detect_webm_file(self):
from skill_seekers.cli.source_detector import SourceDetector
info = SourceDetector.detect("screencast.webm")
self.assertEqual(info.type, "video")
def test_detect_avi_file(self):
from skill_seekers.cli.source_detector import SourceDetector
info = SourceDetector.detect("old-recording.avi")
self.assertEqual(info.type, "video")
def test_detect_mov_file(self):
from skill_seekers.cli.source_detector import SourceDetector
info = SourceDetector.detect("screen.mov")
self.assertEqual(info.type, "video")
def test_validate_video_file_exists(self):
from skill_seekers.cli.source_detector import SourceDetector, SourceInfo
info = SourceInfo(
type="video",
parsed={"file_path": "/nonexistent/file.mp4", "source_kind": "file"},
suggested_name="file",
raw_input="file.mp4",
)
with self.assertRaises(ValueError):
SourceDetector.validate_source(info)
def test_validate_video_url_no_error(self):
"""URL-based video sources should not raise during validation."""
from skill_seekers.cli.source_detector import SourceDetector, SourceInfo
info = SourceInfo(
type="video",
parsed={"url": "https://youtube.com/watch?v=test", "source_kind": "url"},
suggested_name="test",
raw_input="https://youtube.com/watch?v=test",
)
# Should not raise
SourceDetector.validate_source(info)
# =============================================================================
# Test: CLI Arguments
# =============================================================================
class TestVideoArguments(unittest.TestCase):
"""Test video CLI argument definitions."""
def test_video_arguments_dict(self):
from skill_seekers.cli.arguments.video import VIDEO_ARGUMENTS
self.assertIn("url", VIDEO_ARGUMENTS)
self.assertIn("video_file", VIDEO_ARGUMENTS)
self.assertIn("playlist", VIDEO_ARGUMENTS)
self.assertIn("languages", VIDEO_ARGUMENTS)
self.assertIn("visual", VIDEO_ARGUMENTS)
self.assertIn("whisper_model", VIDEO_ARGUMENTS)
self.assertIn("from_json", VIDEO_ARGUMENTS)
def test_add_video_arguments(self):
import argparse
from skill_seekers.cli.arguments.video import add_video_arguments
parser = argparse.ArgumentParser()
add_video_arguments(parser)
# Should parse without error
args = parser.parse_args(["--url", "https://youtube.com/watch?v=test"])
self.assertEqual(args.url, "https://youtube.com/watch?v=test")
def test_enhance_level_defaults_to_zero(self):
import argparse
from skill_seekers.cli.arguments.video import add_video_arguments
parser = argparse.ArgumentParser()
add_video_arguments(parser)
args = parser.parse_args([])
self.assertEqual(args.enhance_level, 0)
def test_unified_parser_has_video(self):
"""Test video subcommand is registered in main parser."""
from skill_seekers.cli.main import create_parser
parser = create_parser()
args = parser.parse_args(["video", "--url", "https://youtube.com/watch?v=test"])
self.assertEqual(args.url, "https://youtube.com/watch?v=test")
# =============================================================================
# Test: VideoToSkillConverter
# =============================================================================
class TestVideoToSkillConverter(unittest.TestCase):
"""Test the main VideoToSkillConverter class."""
def setUp(self):
self.temp_dir = tempfile.mkdtemp()
def tearDown(self):
shutil.rmtree(self.temp_dir, ignore_errors=True)
# Clean up output dirs that may have been created
for d in ["output/test_video", "output/test_video_video_extracted.json"]:
if os.path.exists(d):
if os.path.isdir(d):
shutil.rmtree(d, ignore_errors=True)
else:
os.unlink(d)
def test_init_with_url(self):
from skill_seekers.cli.video_scraper import VideoToSkillConverter
config = {"name": "test_video", "url": "https://youtube.com/watch?v=test"}
converter = VideoToSkillConverter(config)
self.assertEqual(converter.name, "test_video")
def test_init_with_video_file(self):
from skill_seekers.cli.video_scraper import VideoToSkillConverter
config = {"name": "test_video", "video_file": "test.mp4"}
converter = VideoToSkillConverter(config)
self.assertEqual(converter.config["video_file"], "test.mp4")
def test_build_skill_from_loaded_data(self):
"""Test build_skill works with pre-loaded result data."""
from skill_seekers.cli.video_scraper import VideoToSkillConverter
from skill_seekers.cli.video_models import (
VideoScraperResult,
VideoInfo,
VideoSourceType,
TranscriptSource,
VideoSegment,
SegmentContentType,
)
config = {
"name": "test_video",
"output": os.path.join(self.temp_dir, "test_video"),
}
converter = VideoToSkillConverter(config)
# Manually set result
converter.result = VideoScraperResult(
videos=[
VideoInfo(
video_id="test123",
source_type=VideoSourceType.YOUTUBE,
source_url="https://youtube.com/watch?v=test123",
title="Test Video",
description="A test video.",
duration=120.0,
channel_name="Test",
view_count=1000,
transcript_source=TranscriptSource.YOUTUBE_MANUAL,
segments=[
VideoSegment(
index=0,
start_time=0.0,
end_time=60.0,
duration=60.0,
transcript="Hello world test content.",
chapter_title="Intro",
content="### Intro (00:00 - 01:00)\n\nHello world test content.",
content_type=SegmentContentType.INTRO,
confidence=0.9,
),
VideoSegment(
index=1,
start_time=60.0,
end_time=120.0,
duration=60.0,
transcript="Main content here.",
chapter_title="Main",
content="### Main (01:00 - 02:00)\n\nMain content here.",
content_type=SegmentContentType.EXPLANATION,
confidence=0.9,
),
],
),
],
total_duration_seconds=120.0,
total_segments=2,
)
skill_dir = converter.build_skill()
self.assertTrue(os.path.isdir(skill_dir))
self.assertTrue(os.path.isfile(os.path.join(skill_dir, "SKILL.md")))
self.assertTrue(os.path.isdir(os.path.join(skill_dir, "references")))
self.assertTrue(os.path.isdir(os.path.join(skill_dir, "video_data")))
# Check SKILL.md content
with open(os.path.join(skill_dir, "SKILL.md"), encoding="utf-8") as f:
skill_content = f.read()
self.assertIn("Test Video", skill_content)
self.assertIn("Video Tutorials", skill_content)
def test_save_and_load_extracted_data(self):
"""Test JSON save/load roundtrip."""
from skill_seekers.cli.video_scraper import VideoToSkillConverter
from skill_seekers.cli.video_models import VideoScraperResult, VideoInfo, VideoSourceType
config = {"name": "test_video"}
converter = VideoToSkillConverter(config)
converter.result = VideoScraperResult(
videos=[VideoInfo(video_id="test", source_type=VideoSourceType.YOUTUBE, title="Test")],
total_duration_seconds=60.0,
)
# Save
data_file = converter.save_extracted_data()
self.assertTrue(os.path.isfile(data_file))
# Load into new converter
converter2 = VideoToSkillConverter(config)
converter2.load_extracted_data(data_file)
self.assertEqual(len(converter2.result.videos), 1)
self.assertEqual(converter2.result.videos[0].title, "Test")
# Clean up
os.unlink(data_file)
# =============================================================================
# Test: Visual Extraction Stubs
# =============================================================================
class TestVideoVisualStubs(unittest.TestCase):
"""Test Tier 2 visual extraction stubs raise proper errors."""
def test_check_visual_dependencies(self):
from skill_seekers.cli.video_visual import check_visual_dependencies
deps = check_visual_dependencies()
self.assertIn("opencv", deps)
self.assertIn("scenedetect", deps)
self.assertIn("easyocr", deps)
def test_detect_scenes_raises_without_deps(self):
from skill_seekers.cli.video_visual import detect_scenes, HAS_OPENCV
if not HAS_OPENCV:
with self.assertRaises(RuntimeError):
detect_scenes("test.mp4")
def test_extract_keyframes_raises_without_deps(self):
from skill_seekers.cli.video_visual import extract_keyframes, HAS_OPENCV
if not HAS_OPENCV:
with self.assertRaises(RuntimeError):
extract_keyframes("test.mp4", [0.0, 1.0])
def test_classify_frame_raises_without_deps(self):
from skill_seekers.cli.video_visual import classify_frame, HAS_OPENCV
if not HAS_OPENCV:
with self.assertRaises(RuntimeError):
classify_frame("frame.png")
def test_extract_text_raises_without_deps(self):
from skill_seekers.cli.video_visual import extract_text_from_frame, HAS_EASYOCR
if not HAS_EASYOCR:
with self.assertRaises(RuntimeError):
extract_text_from_frame("frame.png")
# =============================================================================
# Test: Create Command Integration
# =============================================================================
class TestVideoCreateCommandIntegration(unittest.TestCase):
"""Test create command routes video sources correctly."""
def test_create_command_routing_youtube_url(self):
"""Test that CreateCommand routes YouTube URLs to video scraper."""
from skill_seekers.cli.source_detector import SourceDetector
# Detect source
info = SourceDetector.detect("https://www.youtube.com/watch?v=dQw4w9WgXcQ")
self.assertEqual(info.type, "video")
def test_create_command_routing_video_file(self):
"""Test that CreateCommand routes video files to video scraper."""
from skill_seekers.cli.source_detector import SourceDetector
info = SourceDetector.detect("tutorial.mp4")
self.assertEqual(info.type, "video")
def test_create_arguments_include_video(self):
"""Test that create arguments include video mode."""
from skill_seekers.cli.arguments.create import get_source_specific_arguments
video_args = get_source_specific_arguments("video")
self.assertIn("video_url", video_args)
self.assertIn("visual", video_args)
self.assertIn("whisper_model", video_args)
# =============================================================================
# Test: Config Validator
# =============================================================================
class TestVideoConfigValidator(unittest.TestCase):
"""Test that video is a valid source type in config validator."""
def test_video_in_valid_source_types(self):
from skill_seekers.cli.config_validator import ConfigValidator
self.assertIn("video", ConfigValidator.VALID_SOURCE_TYPES)
# =============================================================================
# Test: Helper Functions
# =============================================================================
class TestVideoHelperFunctions(unittest.TestCase):
"""Test module-level helper functions."""
def test_sanitize_filename(self):
from skill_seekers.cli.video_scraper import _sanitize_filename
self.assertEqual(
_sanitize_filename("React Hooks Tutorial for Beginners"),
"react-hooks-tutorial-for-beginners",
)
self.assertEqual(
_sanitize_filename("Test!!! Video---Title"),
"test-video-title",
)
def test_sanitize_filename_max_length(self):
from skill_seekers.cli.video_scraper import _sanitize_filename
result = _sanitize_filename("a" * 100, max_length=20)
self.assertLessEqual(len(result), 20)
def test_format_duration(self):
from skill_seekers.cli.video_scraper import _format_duration
self.assertEqual(_format_duration(65), "01:05")
self.assertEqual(_format_duration(3661), "1:01:01")
self.assertEqual(_format_duration(0), "00:00")
def test_format_count(self):
from skill_seekers.cli.video_scraper import _format_count
self.assertEqual(_format_count(1500000), "1,500,000")
self.assertEqual(_format_count(None), "N/A")
def test_infer_description_from_video(self):
from skill_seekers.cli.video_scraper import infer_description_from_video
info = _make_sample_video_info()
desc = infer_description_from_video(info)
self.assertTrue(desc.startswith("Use when"))
# =============================================================================
# Test: OCR Preprocessing (Phase 1)
# =============================================================================
class TestOCRPreprocessing(unittest.TestCase):
"""Test frame-type-aware OCR preprocessing functions."""
def test_get_ocr_params_code_editor(self):
from skill_seekers.cli.video_visual import _get_ocr_params
from skill_seekers.cli.video_models import FrameType
params = _get_ocr_params(FrameType.CODE_EDITOR)
self.assertEqual(params["decoder"], "beamsearch")
self.assertEqual(params["text_threshold"], 0.4)
self.assertEqual(params["contrast_ths"], 0.3)
self.assertEqual(params["mag_ratio"], 1.0)
def test_get_ocr_params_terminal(self):
from skill_seekers.cli.video_visual import _get_ocr_params
from skill_seekers.cli.video_models import FrameType
params = _get_ocr_params(FrameType.TERMINAL)
self.assertEqual(params["decoder"], "beamsearch")
self.assertEqual(params["low_text"], 0.3)
def test_get_ocr_params_slide(self):
from skill_seekers.cli.video_visual import _get_ocr_params
from skill_seekers.cli.video_models import FrameType
params = _get_ocr_params(FrameType.SLIDE)
self.assertEqual(params["decoder"], "greedy")
self.assertEqual(params["text_threshold"], 0.6)
def test_get_ocr_params_other(self):
from skill_seekers.cli.video_visual import _get_ocr_params
from skill_seekers.cli.video_models import FrameType
params = _get_ocr_params(FrameType.OTHER)
self.assertEqual(params["decoder"], "greedy")
def test_preprocess_returns_original_for_other(self):
from skill_seekers.cli.video_visual import _preprocess_frame_for_ocr
from skill_seekers.cli.video_models import FrameType
result = _preprocess_frame_for_ocr("/nonexistent/path.jpg", FrameType.OTHER)
self.assertEqual(result, "/nonexistent/path.jpg")
def test_preprocess_returns_original_for_webcam(self):
from skill_seekers.cli.video_visual import _preprocess_frame_for_ocr
from skill_seekers.cli.video_models import FrameType
result = _preprocess_frame_for_ocr("/nonexistent/path.jpg", FrameType.WEBCAM)
self.assertEqual(result, "/nonexistent/path.jpg")
# =============================================================================
# Test: Spatial Layout (Phase 2)
# =============================================================================
class TestSpatialLayout(unittest.TestCase):
"""Test OCR spatial layout preservation functions."""
def test_cluster_empty_results(self):
from skill_seekers.cli.video_visual import _cluster_ocr_into_lines
from skill_seekers.cli.video_models import FrameType
regions = _cluster_ocr_into_lines([], FrameType.OTHER)
self.assertEqual(regions, [])
def test_cluster_single_result(self):
from skill_seekers.cli.video_visual import _cluster_ocr_into_lines
from skill_seekers.cli.video_models import FrameType
raw = [([[0, 10], [100, 10], [100, 30], [0, 30]], "hello world", 0.9)]
regions = _cluster_ocr_into_lines(raw, FrameType.OTHER)
self.assertEqual(len(regions), 1)
self.assertEqual(regions[0].text, "hello world")
self.assertAlmostEqual(regions[0].confidence, 0.9)
def test_cluster_two_lines(self):
from skill_seekers.cli.video_visual import _cluster_ocr_into_lines
from skill_seekers.cli.video_models import FrameType
raw = [
([[0, 10], [100, 10], [100, 30], [0, 30]], "line one", 0.9),
([[0, 50], [100, 50], [100, 70], [0, 70]], "line two", 0.8),
]
regions = _cluster_ocr_into_lines(raw, FrameType.CODE_EDITOR)
self.assertEqual(len(regions), 2)
self.assertEqual(regions[0].text, "line one")
self.assertEqual(regions[1].text, "line two")
self.assertTrue(regions[0].is_monospace)
def test_cluster_same_line_fragments(self):
from skill_seekers.cli.video_visual import _cluster_ocr_into_lines
from skill_seekers.cli.video_models import FrameType
raw = [
([[0, 10], [50, 10], [50, 30], [0, 30]], "hello", 0.9),
([[55, 10], [120, 10], [120, 30], [55, 30]], "world", 0.85),
]
regions = _cluster_ocr_into_lines(raw, FrameType.OTHER)
self.assertEqual(len(regions), 1)
self.assertIn("hello", regions[0].text)
self.assertIn("world", regions[0].text)
def test_cluster_monospace_flag(self):
from skill_seekers.cli.video_visual import _cluster_ocr_into_lines
from skill_seekers.cli.video_models import FrameType
raw = [([[0, 0], [100, 0], [100, 20], [0, 20]], "test", 0.9)]
code_regions = _cluster_ocr_into_lines(raw, FrameType.CODE_EDITOR)
self.assertTrue(code_regions[0].is_monospace)
terminal_regions = _cluster_ocr_into_lines(raw, FrameType.TERMINAL)
self.assertTrue(terminal_regions[0].is_monospace)
slide_regions = _cluster_ocr_into_lines(raw, FrameType.SLIDE)
self.assertFalse(slide_regions[0].is_monospace)
def test_assemble_code_editor_newlines(self):
from skill_seekers.cli.video_visual import _assemble_structured_text
from skill_seekers.cli.video_models import FrameType, OCRRegion
regions = [
OCRRegion(text="def hello():", confidence=0.9, bbox=(100, 10, 300, 30)),
OCRRegion(text="return 'world'", confidence=0.9, bbox=(100, 40, 350, 60)),
]
text = _assemble_structured_text(regions, FrameType.CODE_EDITOR)
self.assertIn("\n", text)
self.assertIn("def hello():", text)
self.assertIn("return 'world'", text)
def test_assemble_slide_double_newlines(self):
from skill_seekers.cli.video_visual import _assemble_structured_text
from skill_seekers.cli.video_models import FrameType, OCRRegion
regions = [
OCRRegion(text="Title", confidence=0.9, bbox=(100, 10, 300, 30)),
OCRRegion(text="Subtitle", confidence=0.9, bbox=(100, 80, 350, 100)),
]
text = _assemble_structured_text(regions, FrameType.SLIDE)
self.assertIn("\n\n", text)
def test_assemble_other_flat(self):
from skill_seekers.cli.video_visual import _assemble_structured_text
from skill_seekers.cli.video_models import FrameType, OCRRegion
regions = [
OCRRegion(text="hello", confidence=0.9, bbox=(0, 0, 50, 20)),
OCRRegion(text="world", confidence=0.9, bbox=(0, 30, 50, 50)),
]
text = _assemble_structured_text(regions, FrameType.OTHER)
self.assertEqual(text, "hello world")
self.assertNotIn("\n", text)
def test_assemble_empty_regions(self):
from skill_seekers.cli.video_visual import _assemble_structured_text
from skill_seekers.cli.video_models import FrameType
text = _assemble_structured_text([], FrameType.CODE_EDITOR)
self.assertEqual(text, "")
# =============================================================================
# Test: Cross-Frame Text Continuity (Phase 3)
# =============================================================================
class TestTextContinuity(unittest.TestCase):
"""Test cross-frame text tracking and code block detection."""
def test_text_similarity_identical(self):
from skill_seekers.cli.video_visual import _text_similarity
self.assertAlmostEqual(_text_similarity("hello world", "hello world"), 1.0)
def test_text_similarity_empty(self):
from skill_seekers.cli.video_visual import _text_similarity
self.assertEqual(_text_similarity("", "hello"), 0.0)
self.assertEqual(_text_similarity("hello", ""), 0.0)
self.assertEqual(_text_similarity("", ""), 0.0)
def test_text_similarity_different(self):
from skill_seekers.cli.video_visual import _text_similarity
sim = _text_similarity("hello world", "goodbye universe")
self.assertLess(sim, 0.5)
def test_text_similarity_similar(self):
from skill_seekers.cli.video_visual import _text_similarity
sim = _text_similarity(
"def hello():\n return 'world'",
"def hello():\n return 'world!'",
)
self.assertGreater(sim, 0.8)
def test_tracker_creates_new_block(self):
from skill_seekers.cli.video_visual import TextBlockTracker
from skill_seekers.cli.video_models import FrameType
tracker = TextBlockTracker()
tracker.update(0, 1.0, "def hello():\n return 'world'", 0.9, FrameType.CODE_EDITOR)
blocks = tracker.finalize()
self.assertEqual(len(blocks), 1)
self.assertEqual(blocks[0].first_seen, 1.0)
self.assertEqual(blocks[0].frame_type, FrameType.CODE_EDITOR)
def test_tracker_merges_similar_frames(self):
from skill_seekers.cli.video_visual import TextBlockTracker
from skill_seekers.cli.video_models import FrameType
tracker = TextBlockTracker()
text1 = "def hello():\n return 'world'"
text2 = "def hello():\n return 'world!'"
tracker.update(0, 1.0, text1, 0.8, FrameType.CODE_EDITOR)
tracker.update(1, 2.0, text2, 0.9, FrameType.CODE_EDITOR)
blocks = tracker.finalize()
self.assertEqual(len(blocks), 1)
self.assertEqual(blocks[0].best_text, text2)
self.assertEqual(blocks[0].best_confidence, 0.9)
self.assertEqual(len(blocks[0].frame_indices), 2)
def test_tracker_creates_separate_blocks_for_different_text(self):
from skill_seekers.cli.video_visual import TextBlockTracker
from skill_seekers.cli.video_models import FrameType
tracker = TextBlockTracker()
tracker.update(0, 1.0, "completely different text about cats", 0.8, FrameType.CODE_EDITOR)
tracker.update(1, 2.0, "unrelated content about dogs and stuff", 0.9, FrameType.CODE_EDITOR)
blocks = tracker.finalize()
self.assertEqual(len(blocks), 2)
def test_tracker_completes_on_non_code_frame(self):
from skill_seekers.cli.video_visual import TextBlockTracker
from skill_seekers.cli.video_models import FrameType
tracker = TextBlockTracker()
tracker.update(0, 1.0, "def hello():\n return 'world'", 0.9, FrameType.CODE_EDITOR)
tracker.update(1, 2.0, "slide text", 0.9, FrameType.SLIDE)
# After slide frame, the code block should be completed
tracker.update(2, 3.0, "def hello():\n return 'world'", 0.9, FrameType.CODE_EDITOR)
blocks = tracker.finalize()
# Should have 2 blocks (before and after the slide)
self.assertEqual(len(blocks), 2)
def test_tracker_ignores_short_text(self):
from skill_seekers.cli.video_visual import TextBlockTracker
from skill_seekers.cli.video_models import FrameType
tracker = TextBlockTracker()
tracker.update(0, 1.0, "short", 0.9, FrameType.CODE_EDITOR)
blocks = tracker.finalize()
self.assertEqual(len(blocks), 0)
def test_extract_code_blocks_filters_short(self):
from skill_seekers.cli.video_visual import _extract_code_blocks, TrackedTextBlock
from skill_seekers.cli.video_models import FrameType
blocks_in = [
TrackedTextBlock(
first_seen=1.0,
last_seen=2.0,
frame_indices=[0],
text_snapshots=["short"],
frame_type=FrameType.CODE_EDITOR,
best_text="short",
best_confidence=0.9,
),
]
code_blocks = _extract_code_blocks(blocks_in)
self.assertEqual(len(code_blocks), 0)
def test_extract_code_blocks_maps_context(self):
from skill_seekers.cli.video_visual import _extract_code_blocks, TrackedTextBlock
from skill_seekers.cli.video_models import CodeContext, FrameType
blocks_in = [
TrackedTextBlock(
first_seen=1.0,
last_seen=2.0,
frame_indices=[0, 1],
text_snapshots=["def hello():\n return 'world'"],
frame_type=FrameType.CODE_EDITOR,
best_text="def hello():\n return 'world'",
best_confidence=0.9,
),
TrackedTextBlock(
first_seen=3.0,
last_seen=4.0,
frame_indices=[2],
text_snapshots=["$ python hello.py\nHello World output"],
frame_type=FrameType.TERMINAL,
best_text="$ python hello.py\nHello World output",
best_confidence=0.8,
),
]
code_blocks = _extract_code_blocks(blocks_in)
self.assertEqual(len(code_blocks), 2)
self.assertEqual(code_blocks[0].context, CodeContext.EDITOR)
self.assertEqual(code_blocks[1].context, CodeContext.TERMINAL)
def test_extract_code_blocks_skips_non_code_frames(self):
from skill_seekers.cli.video_visual import _extract_code_blocks, TrackedTextBlock
from skill_seekers.cli.video_models import FrameType
blocks_in = [
TrackedTextBlock(
first_seen=1.0,
last_seen=2.0,
frame_indices=[0],
text_snapshots=["This is a long slide text with lots of content here"],
frame_type=FrameType.SLIDE,
best_text="This is a long slide text with lots of content here",
best_confidence=0.9,
),
]
code_blocks = _extract_code_blocks(blocks_in)
self.assertEqual(len(code_blocks), 0)
def test_extract_visual_data_returns_tuple(self):
"""Verify extract_visual_data returns (keyframes, code_blocks) tuple."""
from skill_seekers.cli.video_visual import extract_visual_data, HAS_OPENCV
if not HAS_OPENCV:
with self.assertRaises(RuntimeError):
extract_visual_data("test.mp4", [], "/tmp/test")
else:
# If opencv is available, at least verify the signature
import inspect
sig = inspect.signature(extract_visual_data)
# Check the return annotation
self.assertIn("tuple", str(sig.return_annotation).lower())
def test_extract_text_from_frame_returns_tuple(self):
"""Verify extract_text_from_frame returns (raw_results, flat_text) tuple."""
from skill_seekers.cli.video_visual import extract_text_from_frame, HAS_EASYOCR
if not HAS_EASYOCR:
with self.assertRaises(RuntimeError):
extract_text_from_frame("frame.png")
else:
import inspect
sig = inspect.signature(extract_text_from_frame)
self.assertIn("tuple", str(sig.return_annotation).lower())
# =============================================================================
# Test: Output Formatting (Phase 4)
# =============================================================================
class TestOutputFormatting(unittest.TestCase):
"""Test type-aware output formatting in reference markdown."""
def setUp(self):
self.temp_dir = tempfile.mkdtemp()
def tearDown(self):
shutil.rmtree(self.temp_dir, ignore_errors=True)
def test_reference_md_code_block_formatting(self):
"""Test that code editor OCR is wrapped in fenced code blocks."""
from skill_seekers.cli.video_scraper import VideoToSkillConverter
from skill_seekers.cli.video_models import (
CodeBlock,
CodeContext,
FrameType,
KeyFrame,
SegmentContentType,
TranscriptSource,
VideoInfo,
VideoScraperResult,
VideoSegment,
VideoSourceType,
)
config = {
"name": "test_video",
"output": os.path.join(self.temp_dir, "test_video"),
}
converter = VideoToSkillConverter(config)
converter.result = VideoScraperResult(
videos=[
VideoInfo(
video_id="test123",
source_type=VideoSourceType.YOUTUBE,
title="Code Tutorial",
duration=60.0,
transcript_source=TranscriptSource.YOUTUBE_MANUAL,
segments=[
VideoSegment(
index=0,
start_time=0.0,
end_time=60.0,
duration=60.0,
transcript="Some code content.",
content="### Intro (00:00 - 01:00)\n\nSome code content.",
content_type=SegmentContentType.LIVE_CODING,
confidence=0.9,
keyframes=[
KeyFrame(
timestamp=5.0,
image_path="/nonexistent/frame.jpg",
frame_type=FrameType.CODE_EDITOR,
ocr_text="def hello():\n return 'world'",
),
],
detected_code_blocks=[
CodeBlock(
code="def hello():\n return 'world'",
language="python",
source_frame=5.0,
context=CodeContext.EDITOR,
confidence=0.9,
),
],
has_code_on_screen=True,
),
],
),
],
total_duration_seconds=60.0,
total_segments=1,
)
ref_md = converter._generate_reference_md(converter.result.videos[0])
# OCR text should be in a fenced code block with language hint
self.assertIn("```python", ref_md)
self.assertIn("def hello():", ref_md)
# Detected code subsection should exist
self.assertIn("#### Detected Code", ref_md)
def test_reference_md_slide_formatting(self):
"""Test that slide OCR is formatted as blockquotes."""
from skill_seekers.cli.video_scraper import VideoToSkillConverter
from skill_seekers.cli.video_models import (
FrameType,
KeyFrame,
SegmentContentType,
TranscriptSource,
VideoInfo,
VideoScraperResult,
VideoSegment,
VideoSourceType,
)
config = {
"name": "test_video",
"output": os.path.join(self.temp_dir, "test_video"),
}
converter = VideoToSkillConverter(config)
converter.result = VideoScraperResult(
videos=[
VideoInfo(
video_id="test456",
source_type=VideoSourceType.YOUTUBE,
title="Slide Presentation",
duration=60.0,
transcript_source=TranscriptSource.YOUTUBE_MANUAL,
segments=[
VideoSegment(
index=0,
start_time=0.0,
end_time=60.0,
duration=60.0,
content="### Slides\n\nPresentation content.",
content_type=SegmentContentType.SLIDES,
confidence=0.9,
keyframes=[
KeyFrame(
timestamp=5.0,
image_path="/nonexistent/frame.jpg",
frame_type=FrameType.SLIDE,
ocr_text="Title\n\nSubtitle",
),
],
),
],
),
],
total_duration_seconds=60.0,
total_segments=1,
)
ref_md = converter._generate_reference_md(converter.result.videos[0])
self.assertIn("> Title", ref_md)
self.assertIn("> Subtitle", ref_md)
# Should NOT be in a fenced code block
self.assertNotIn("```", ref_md)
def test_skill_md_code_block_count(self):
"""Test that SKILL.md overview includes code block count."""
from skill_seekers.cli.video_scraper import VideoToSkillConverter
from skill_seekers.cli.video_models import (
CodeBlock,
CodeContext,
KeyFrame,
SegmentContentType,
TranscriptSource,
VideoInfo,
VideoScraperResult,
VideoSegment,
VideoSourceType,
)
config = {
"name": "test_video",
"output": os.path.join(self.temp_dir, "test_video"),
}
converter = VideoToSkillConverter(config)
converter.result = VideoScraperResult(
videos=[
VideoInfo(
video_id="test789",
source_type=VideoSourceType.YOUTUBE,
title="Code Tutorial",
duration=60.0,
transcript_source=TranscriptSource.YOUTUBE_MANUAL,
segments=[
VideoSegment(
index=0,
start_time=0.0,
end_time=60.0,
duration=60.0,
content="### Code\n\nSome content.",
content_type=SegmentContentType.LIVE_CODING,
confidence=0.9,
keyframes=[
KeyFrame(
timestamp=5.0,
image_path="/nonexistent/frame.jpg",
ocr_text="print('hi')",
),
],
detected_code_blocks=[
CodeBlock(
code="print('hi')",
language="python",
source_frame=5.0,
context=CodeContext.EDITOR,
confidence=0.9,
),
],
),
],
),
],
total_duration_seconds=60.0,
total_segments=1,
total_code_blocks=1,
)
skill_md = converter._generate_skill_md()
self.assertIn("1 code blocks detected", skill_md)
# =============================================================================
# Test: Y-Bucket Consensus Engine (Phase A)
# =============================================================================
class TestYBucketConsensus(unittest.TestCase):
"""Test the Y-bucket consensus engine for multi-frame OCR."""
def test_single_frame_single_region(self):
from skill_seekers.cli.video_visual import YBucketConsensusEngine
from skill_seekers.cli.video_models import OCRRegion
engine = YBucketConsensusEngine(y_tolerance=15.0)
engine.add_frame(
0,
1.0,
[OCRRegion(text="hello world", confidence=0.9, bbox=(10, 100, 200, 120))],
)
buckets = engine.build_consensus()
self.assertEqual(len(buckets), 1)
self.assertEqual(buckets[0].consensus_text, "hello world")
self.assertAlmostEqual(buckets[0].consensus_confidence, 0.9)
def test_consensus_from_multiple_frames(self):
from skill_seekers.cli.video_visual import YBucketConsensusEngine
from skill_seekers.cli.video_models import OCRRegion
engine = YBucketConsensusEngine(y_tolerance=15.0)
# Frame 0: low confidence garbled text
engine.add_frame(
0,
1.0,
[OCRRegion(text="Dlctionary", confidence=0.3, bbox=(10, 100, 200, 120))],
)
# Frame 1: medium confidence
engine.add_frame(
1,
1.5,
[OCRRegion(text="Dictionary", confidence=0.62, bbox=(10, 102, 200, 122))],
)
# Frame 2: good confidence
engine.add_frame(
2,
2.0,
[OCRRegion(text="Dictionary", confidence=0.85, bbox=(10, 101, 200, 121))],
)
buckets = engine.build_consensus()
self.assertEqual(len(buckets), 1)
self.assertEqual(buckets[0].consensus_text, "Dictionary")
self.assertGreater(buckets[0].consensus_confidence, 0.5)
def test_multiple_lines_tracked(self):
from skill_seekers.cli.video_visual import YBucketConsensusEngine
from skill_seekers.cli.video_models import OCRRegion
engine = YBucketConsensusEngine(y_tolerance=15.0)
engine.add_frame(
0,
1.0,
[
OCRRegion(text="line one", confidence=0.9, bbox=(10, 100, 200, 120)),
OCRRegion(text="line two", confidence=0.8, bbox=(10, 150, 200, 170)),
],
)
buckets = engine.build_consensus()
self.assertEqual(len(buckets), 2)
texts = [b.consensus_text for b in buckets]
self.assertIn("line one", texts)
self.assertIn("line two", texts)
def test_low_confidence_single_observation_empty(self):
from skill_seekers.cli.video_visual import YBucketConsensusEngine
from skill_seekers.cli.video_models import OCRRegion
engine = YBucketConsensusEngine(y_tolerance=15.0)
engine.add_frame(
0,
1.0,
[OCRRegion(text="garbled", confidence=0.2, bbox=(10, 100, 200, 120))],
)
buckets = engine.build_consensus()
self.assertEqual(len(buckets), 1)
self.assertEqual(buckets[0].consensus_text, "")
def test_get_consensus_text_joins_lines(self):
from skill_seekers.cli.video_visual import YBucketConsensusEngine
from skill_seekers.cli.video_models import OCRRegion
engine = YBucketConsensusEngine(y_tolerance=15.0)
engine.add_frame(
0,
1.0,
[
OCRRegion(text="def hello():", confidence=0.9, bbox=(10, 100, 200, 120)),
OCRRegion(text=" return 'world'", confidence=0.8, bbox=(10, 140, 250, 160)),
],
)
engine.build_consensus()
text = engine.get_consensus_text()
self.assertIn("def hello():", text)
self.assertIn("return 'world'", text)
self.assertIn("\n", text)
def test_reset_clears_state(self):
from skill_seekers.cli.video_visual import YBucketConsensusEngine
from skill_seekers.cli.video_models import OCRRegion
engine = YBucketConsensusEngine()
engine.add_frame(0, 1.0, [OCRRegion(text="test", confidence=0.9, bbox=(10, 100, 200, 120))])
engine.reset()
self.assertEqual(engine.get_consensus_text(), "")
self.assertEqual(engine.get_consensus_confidence(), 0.0)
def test_get_bucket_y_centers(self):
from skill_seekers.cli.video_visual import YBucketConsensusEngine
from skill_seekers.cli.video_models import OCRRegion
engine = YBucketConsensusEngine(y_tolerance=15.0)
engine.add_frame(
0,
1.0,
[
OCRRegion(text="a", confidence=0.9, bbox=(0, 100, 100, 120)),
OCRRegion(text="b", confidence=0.9, bbox=(0, 200, 100, 220)),
],
)
centers = engine.get_bucket_y_centers()
self.assertEqual(len(centers), 2)
self.assertIn(110.0, centers)
self.assertIn(210.0, centers)
# =============================================================================
# Test: Text Group Lifecycle (Phase B)
# =============================================================================
class TestTextGroupLifecycle(unittest.TestCase):
"""Test text group assignment and edit detection."""
def test_single_block_creates_group(self):
from skill_seekers.cli.video_visual import TextBlockTracker
from skill_seekers.cli.video_models import FrameType, OCRRegion
tracker = TextBlockTracker()
regions = [
OCRRegion(text="def hello():", confidence=0.9, bbox=(10, 100, 200, 120)),
OCRRegion(text=" return 'world'", confidence=0.8, bbox=(10, 140, 250, 160)),
]
tracker.update(
0,
1.0,
"def hello():\n return 'world'",
0.85,
FrameType.CODE_EDITOR,
ocr_regions=regions,
)
tracker.finalize()
groups = tracker.get_text_groups()
self.assertEqual(len(groups), 1)
self.assertEqual(groups[0].group_id, "TG-001")
self.assertEqual(len(groups[0].appearances), 1)
def test_same_text_reappears_same_group(self):
from skill_seekers.cli.video_visual import TextBlockTracker
from skill_seekers.cli.video_models import FrameType, OCRRegion
tracker = TextBlockTracker()
regions = [
OCRRegion(text="def hello():", confidence=0.9, bbox=(10, 100, 200, 120)),
OCRRegion(text=" return 'world'", confidence=0.8, bbox=(10, 140, 250, 160)),
]
text = "def hello():\n return 'world'"
# First appearance
tracker.update(0, 1.0, text, 0.85, FrameType.CODE_EDITOR, ocr_regions=regions)
# Break with non-code frame
tracker.update(1, 5.0, "webcam", 0.5, FrameType.WEBCAM)
# Re-appear
tracker.update(2, 10.0, text, 0.85, FrameType.CODE_EDITOR, ocr_regions=regions)
tracker.finalize()
groups = tracker.get_text_groups()
self.assertEqual(len(groups), 1)
self.assertEqual(len(groups[0].appearances), 2)
def test_different_text_creates_new_group(self):
from skill_seekers.cli.video_visual import TextBlockTracker
from skill_seekers.cli.video_models import FrameType, OCRRegion
tracker = TextBlockTracker()
regions_a = [
OCRRegion(text="def func_a():", confidence=0.9, bbox=(10, 100, 200, 120)),
]
regions_b = [
OCRRegion(text="class TotallyDifferent:", confidence=0.9, bbox=(10, 100, 300, 120)),
]
tracker.update(0, 1.0, "def func_a():", 0.9, FrameType.CODE_EDITOR, ocr_regions=regions_a)
tracker.update(1, 5.0, "webcam", 0.5, FrameType.WEBCAM)
tracker.update(
2, 10.0, "class TotallyDifferent:", 0.9, FrameType.CODE_EDITOR, ocr_regions=regions_b
)
tracker.finalize()
groups = tracker.get_text_groups()
self.assertEqual(len(groups), 2)
def test_edit_detected_between_appearances(self):
from skill_seekers.cli.video_visual import TextBlockTracker
from skill_seekers.cli.video_models import FrameType, OCRRegion
tracker = TextBlockTracker()
regions_v1 = [
OCRRegion(text="def hello():", confidence=0.9, bbox=(10, 100, 200, 120)),
OCRRegion(text=" return 'world'", confidence=0.8, bbox=(10, 140, 250, 160)),
]
regions_v2 = [
OCRRegion(text="def hello():", confidence=0.9, bbox=(10, 100, 200, 120)),
OCRRegion(text=" return 'hello world'", confidence=0.8, bbox=(10, 140, 250, 160)),
]
# First version
tracker.update(
0,
1.0,
"def hello():\n return 'world'",
0.85,
FrameType.CODE_EDITOR,
ocr_regions=regions_v1,
)
tracker.update(1, 5.0, "webcam", 0.5, FrameType.WEBCAM)
# Modified version
tracker.update(
2,
10.0,
"def hello():\n return 'hello world'",
0.85,
FrameType.CODE_EDITOR,
ocr_regions=regions_v2,
)
tracker.finalize()
groups = tracker.get_text_groups()
self.assertEqual(len(groups), 1)
self.assertGreaterEqual(len(groups[0].edits), 1)
def test_tracker_y_bucket_matching(self):
"""Test that y-bucket matching works for consecutive code frames."""
from skill_seekers.cli.video_visual import TextBlockTracker
from skill_seekers.cli.video_models import FrameType, OCRRegion
tracker = TextBlockTracker()
# Two frames with same y-coordinates but slightly different text
regions_1 = [
OCRRegion(text="Dlctionary", confidence=0.3, bbox=(10, 100, 200, 120)),
OCRRegion(text="var x = 1", confidence=0.7, bbox=(10, 140, 200, 160)),
]
regions_2 = [
OCRRegion(text="Dictionary", confidence=0.8, bbox=(10, 101, 200, 121)),
OCRRegion(text="var x = 1", confidence=0.9, bbox=(10, 141, 200, 161)),
]
tracker.update(
0, 1.0, "Dlctionary\nvar x = 1", 0.5, FrameType.CODE_EDITOR, ocr_regions=regions_1
)
tracker.update(
1, 2.0, "Dictionary\nvar x = 1", 0.85, FrameType.CODE_EDITOR, ocr_regions=regions_2
)
blocks = tracker.finalize()
# Should be one block (matched by y-bucket overlap)
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0].frame_indices), 2)
def test_compute_edit_no_changes(self):
from skill_seekers.cli.video_visual import TextBlockTracker
tracker = TextBlockTracker()
result = tracker._compute_edit(["line1", "line2"], ["line1", "line2"], 1.0)
self.assertIsNone(result)
def test_compute_edit_with_additions(self):
from skill_seekers.cli.video_visual import TextBlockTracker
tracker = TextBlockTracker()
result = tracker._compute_edit(["line1"], ["line1", "line2"], 1.0)
self.assertIsNotNone(result)
self.assertIn("line2", result.added_lines)
def test_compute_edit_with_removals(self):
from skill_seekers.cli.video_visual import TextBlockTracker
tracker = TextBlockTracker()
result = tracker._compute_edit(["line1", "line2"], ["line1"], 1.0)
self.assertIsNotNone(result)
self.assertIn("line2", result.removed_lines)
# =============================================================================
# Test: Text Group Timeline (Phase C)
# =============================================================================
class TestTextGroupTimeline(unittest.TestCase):
"""Test TextGroupTimeline data structure."""
def test_timeline_serialization(self):
from skill_seekers.cli.video_models import TextGroup, TextGroupTimeline, FrameType
tg = TextGroup(
group_id="TG-001",
appearances=[(1.0, 5.0), (10.0, 15.0)],
consensus_lines=[
{"y_center": 110.0, "text": "def hello():", "confidence": 0.9},
{"y_center": 150.0, "text": " return 'world'", "confidence": 0.8},
],
edits=[],
frame_type=FrameType.CODE_EDITOR,
)
timeline = TextGroupTimeline(
text_groups=[tg],
total_code_time=9.0,
total_groups=1,
total_edits=0,
)
d = timeline.to_dict()
self.assertEqual(len(d["text_groups"]), 1)
self.assertEqual(d["total_code_time"], 9.0)
timeline2 = TextGroupTimeline.from_dict(d)
self.assertEqual(len(timeline2.text_groups), 1)
self.assertEqual(timeline2.text_groups[0].group_id, "TG-001")
def test_get_groups_at_time(self):
from skill_seekers.cli.video_models import TextGroup, TextGroupTimeline, FrameType
tg1 = TextGroup(
group_id="TG-001",
appearances=[(1.0, 5.0)],
consensus_lines=[{"text": "code1", "y_center": 100.0, "confidence": 0.9}],
edits=[],
frame_type=FrameType.CODE_EDITOR,
)
tg2 = TextGroup(
group_id="TG-002",
appearances=[(3.0, 8.0)],
consensus_lines=[{"text": "code2", "y_center": 100.0, "confidence": 0.9}],
edits=[],
frame_type=FrameType.CODE_EDITOR,
)
timeline = TextGroupTimeline(text_groups=[tg1, tg2])
# At t=4, both should be active
active = timeline.get_groups_at_time(4.0)
self.assertEqual(len(active), 2)
# At t=0, none active
active = timeline.get_groups_at_time(0.0)
self.assertEqual(len(active), 0)
# At t=6, only TG-002
active = timeline.get_groups_at_time(6.0)
self.assertEqual(len(active), 1)
self.assertEqual(active[0].group_id, "TG-002")
def test_text_group_full_text(self):
from skill_seekers.cli.video_models import TextGroup, FrameType
tg = TextGroup(
group_id="TG-001",
consensus_lines=[
{"y_center": 100.0, "text": "line one", "confidence": 0.9},
{"y_center": 120.0, "text": "", "confidence": 0.0},
{"y_center": 140.0, "text": "line three", "confidence": 0.8},
],
edits=[],
frame_type=FrameType.CODE_EDITOR,
)
self.assertEqual(tg.full_text, "line one\nline three")
def test_text_group_serialization(self):
from skill_seekers.cli.video_models import TextGroup, TextGroupEdit, FrameType
edit = TextGroupEdit(
timestamp=5.0,
added_lines=["new line"],
removed_lines=[],
modified_lines=[{"line_num": 0, "old": "x", "new": "y"}],
)
tg = TextGroup(
group_id="TG-001",
appearances=[(1.0, 5.0)],
consensus_lines=[{"y_center": 100.0, "text": "code", "confidence": 0.9}],
edits=[edit],
detected_language="python",
frame_type=FrameType.CODE_EDITOR,
)
d = tg.to_dict()
self.assertEqual(d["group_id"], "TG-001")
self.assertEqual(d["detected_language"], "python")
self.assertEqual(len(d["edits"]), 1)
tg2 = TextGroup.from_dict(d)
self.assertEqual(tg2.group_id, "TG-001")
self.assertEqual(tg2.detected_language, "python")
self.assertEqual(len(tg2.edits), 1)
self.assertEqual(tg2.edits[0].added_lines, ["new line"])
def test_code_block_text_group_id(self):
from skill_seekers.cli.video_models import CodeBlock, CodeContext
cb = CodeBlock(
code="print('hi')",
language="python",
context=CodeContext.EDITOR,
confidence=0.9,
text_group_id="TG-001",
)
d = cb.to_dict()
self.assertEqual(d["text_group_id"], "TG-001")
cb2 = CodeBlock.from_dict(d)
self.assertEqual(cb2.text_group_id, "TG-001")
def test_video_info_timeline_serialization(self):
from skill_seekers.cli.video_models import (
VideoInfo,
VideoSourceType,
TextGroupTimeline,
TextGroup,
FrameType,
)
tg = TextGroup(
group_id="TG-001",
appearances=[(1.0, 5.0)],
consensus_lines=[{"y_center": 100.0, "text": "code", "confidence": 0.9}],
edits=[],
frame_type=FrameType.CODE_EDITOR,
)
timeline = TextGroupTimeline(text_groups=[tg], total_groups=1)
info = VideoInfo(
video_id="test",
source_type=VideoSourceType.YOUTUBE,
text_group_timeline=timeline,
)
d = info.to_dict()
self.assertIsNotNone(d["text_group_timeline"])
self.assertEqual(len(d["text_group_timeline"]["text_groups"]), 1)
info2 = VideoInfo.from_dict(d)
self.assertIsNotNone(info2.text_group_timeline)
self.assertEqual(len(info2.text_group_timeline.text_groups), 1)
def test_video_info_no_timeline_serialization(self):
from skill_seekers.cli.video_models import VideoInfo, VideoSourceType
info = VideoInfo(video_id="test", source_type=VideoSourceType.YOUTUBE)
d = info.to_dict()
self.assertIsNone(d["text_group_timeline"])
info2 = VideoInfo.from_dict(d)
self.assertIsNone(info2.text_group_timeline)
def test_extract_visual_data_returns_3_tuple(self):
"""Verify extract_visual_data returns (keyframes, code_blocks, timeline) tuple."""
from skill_seekers.cli.video_visual import extract_visual_data, HAS_OPENCV
if not HAS_OPENCV:
with self.assertRaises(RuntimeError):
extract_visual_data("test.mp4", [], "/tmp/test")
else:
import inspect
sig = inspect.signature(extract_visual_data)
self.assertIn("tuple", str(sig.return_annotation).lower())
self.assertIn("TextGroupTimeline", str(sig.return_annotation))
# =============================================================================
# Test: Audio-Visual Alignment (Phase D)
# =============================================================================
class TestAudioVisualAlignment(unittest.TestCase):
"""Test audio-visual alignment building and rendering."""
def test_alignment_serialization(self):
from skill_seekers.cli.video_models import AudioVisualAlignment
av = AudioVisualAlignment(
text_group_id="TG-001",
start_time=1.0,
end_time=5.0,
on_screen_code="def hello():\n return 'world'",
transcript_during="Now let's define a hello function",
language="python",
)
d = av.to_dict()
self.assertEqual(d["text_group_id"], "TG-001")
self.assertEqual(d["language"], "python")
av2 = AudioVisualAlignment.from_dict(d)
self.assertEqual(av2.text_group_id, "TG-001")
self.assertEqual(av2.language, "python")
self.assertIn("hello function", av2.transcript_during)
def test_build_audio_visual_alignments(self):
from skill_seekers.cli.video_scraper import _build_audio_visual_alignments
from skill_seekers.cli.video_models import (
TextGroup,
TextGroupTimeline,
TranscriptSegment,
TranscriptSource,
FrameType,
)
tg = TextGroup(
group_id="TG-001",
appearances=[(10.0, 20.0)],
consensus_lines=[
{"y_center": 100.0, "text": "def hello():", "confidence": 0.9},
],
edits=[],
frame_type=FrameType.CODE_EDITOR,
)
timeline = TextGroupTimeline(text_groups=[tg])
transcript = [
TranscriptSegment(
text="Before code", start=5.0, end=9.0, source=TranscriptSource.YOUTUBE_MANUAL
),
TranscriptSegment(
text="Now we define hello",
start=10.0,
end=15.0,
source=TranscriptSource.YOUTUBE_MANUAL,
),
TranscriptSegment(
text="and it returns world",
start=15.0,
end=20.0,
source=TranscriptSource.YOUTUBE_MANUAL,
),
TranscriptSegment(
text="After code", start=21.0, end=25.0, source=TranscriptSource.YOUTUBE_MANUAL
),
]
alignments = _build_audio_visual_alignments(timeline, transcript)
self.assertEqual(len(alignments), 1)
self.assertEqual(alignments[0].text_group_id, "TG-001")
self.assertIn("define hello", alignments[0].transcript_during)
self.assertIn("returns world", alignments[0].transcript_during)
# Before and after should not be included
self.assertNotIn("Before code", alignments[0].transcript_during)
self.assertNotIn("After code", alignments[0].transcript_during)
def test_build_alignments_no_overlap(self):
from skill_seekers.cli.video_scraper import _build_audio_visual_alignments
from skill_seekers.cli.video_models import (
TextGroup,
TextGroupTimeline,
TranscriptSegment,
TranscriptSource,
FrameType,
)
tg = TextGroup(
group_id="TG-001",
appearances=[(100.0, 110.0)],
consensus_lines=[{"y_center": 100.0, "text": "code", "confidence": 0.9}],
edits=[],
frame_type=FrameType.CODE_EDITOR,
)
timeline = TextGroupTimeline(text_groups=[tg])
transcript = [
TranscriptSegment(
text="Unrelated", start=0.0, end=5.0, source=TranscriptSource.YOUTUBE_MANUAL
),
]
alignments = _build_audio_visual_alignments(timeline, transcript)
self.assertEqual(len(alignments), 0)
def test_reference_md_code_timeline_section(self):
"""Test that Code Timeline section renders correctly."""
from skill_seekers.cli.video_scraper import VideoToSkillConverter
from skill_seekers.cli.video_models import (
FrameType,
TextGroup,
TextGroupTimeline,
TranscriptSource,
VideoInfo,
VideoScraperResult,
VideoSegment,
SegmentContentType,
VideoSourceType,
)
config = {"name": "test_video", "output": os.path.join(tempfile.mkdtemp(), "test_video")}
converter = VideoToSkillConverter(config)
tg = TextGroup(
group_id="TG-001",
appearances=[(1.0, 5.0)],
consensus_lines=[
{"y_center": 100.0, "text": "def hello():", "confidence": 0.9},
{"y_center": 140.0, "text": " return 'world'", "confidence": 0.8},
],
edits=[],
frame_type=FrameType.CODE_EDITOR,
)
timeline = TextGroupTimeline(
text_groups=[tg], total_code_time=4.0, total_groups=1, total_edits=0
)
converter.result = VideoScraperResult(
videos=[
VideoInfo(
video_id="test",
source_type=VideoSourceType.YOUTUBE,
title="Test",
duration=60.0,
transcript_source=TranscriptSource.YOUTUBE_MANUAL,
text_group_timeline=timeline,
segments=[
VideoSegment(
index=0,
start_time=0.0,
end_time=60.0,
duration=60.0,
content="### Intro\n\nContent.",
content_type=SegmentContentType.LIVE_CODING,
),
],
),
],
total_duration_seconds=60.0,
total_segments=1,
)
ref_md = converter._generate_reference_md(converter.result.videos[0])
self.assertIn("## Code Timeline", ref_md)
self.assertIn("TG-001", ref_md)
self.assertIn("def hello():", ref_md)
self.assertIn("return 'world'", ref_md)
def test_reference_md_audio_visual_section(self):
"""Test that Audio-Visual Alignment section renders correctly."""
from skill_seekers.cli.video_scraper import VideoToSkillConverter
from skill_seekers.cli.video_models import (
AudioVisualAlignment,
TranscriptSource,
VideoInfo,
VideoScraperResult,
VideoSegment,
SegmentContentType,
VideoSourceType,
)
config = {"name": "test_video", "output": os.path.join(tempfile.mkdtemp(), "test_video")}
converter = VideoToSkillConverter(config)
converter.result = VideoScraperResult(
videos=[
VideoInfo(
video_id="test",
source_type=VideoSourceType.YOUTUBE,
title="Test",
duration=60.0,
transcript_source=TranscriptSource.YOUTUBE_MANUAL,
audio_visual_alignments=[
AudioVisualAlignment(
text_group_id="TG-001",
start_time=1.0,
end_time=5.0,
on_screen_code="def hello():\n return 'world'",
transcript_during="Now we write a hello function",
language="python",
),
],
segments=[
VideoSegment(
index=0,
start_time=0.0,
end_time=60.0,
duration=60.0,
content="### Intro\n\nContent.",
content_type=SegmentContentType.LIVE_CODING,
),
],
),
],
total_duration_seconds=60.0,
total_segments=1,
)
ref_md = converter._generate_reference_md(converter.result.videos[0])
self.assertIn("## Audio-Visual Alignment", ref_md)
self.assertIn("TG-001", ref_md)
self.assertIn("def hello():", ref_md)
self.assertIn("hello function", ref_md)
self.assertIn("**Narrator:**", ref_md)
# =============================================================================
# Phase E-G Tests: Dark Theme, Multi-Engine OCR, Claude Vision
# =============================================================================
class TestDarkThemePreprocessing(unittest.TestCase):
"""Tests for dark theme detection and frame preprocessing."""
def test_detect_theme_dark(self):
"""Dark image (median < 128) returns 'dark'."""
import numpy as np
from skill_seekers.cli.video_visual import _detect_theme
# Simulate a dark IDE background (median ~30)
dark_img = np.full((100, 200), 30, dtype=np.uint8)
self.assertEqual(_detect_theme(dark_img), "dark")
def test_detect_theme_light(self):
"""Light image (median >= 128) returns 'light'."""
import numpy as np
from skill_seekers.cli.video_visual import _detect_theme
# Simulate a light background (median ~220)
light_img = np.full((100, 200), 220, dtype=np.uint8)
self.assertEqual(_detect_theme(light_img), "light")
def test_preprocess_inverts_dark_frame(self):
"""Verify dark code frame gets inverted to produce lighter output."""
try:
import cv2
import numpy as np
except ImportError:
self.skipTest("OpenCV not available")
from skill_seekers.cli.video_models import FrameType
from skill_seekers.cli.video_visual import _preprocess_frame_for_ocr
# Create a dark frame (simulating dark-theme IDE)
dark_frame = np.full((100, 200, 3), 30, dtype=np.uint8)
# Add some "text" pixels (bright on dark)
dark_frame[40:60, 20:180] = 200
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
tmp_path = tmp.name
cv2.imwrite(tmp_path, dark_frame)
try:
result_path = _preprocess_frame_for_ocr(tmp_path, FrameType.CODE_EDITOR)
self.assertNotEqual(result_path, tmp_path)
result_img = cv2.imread(result_path, cv2.IMREAD_GRAYSCALE)
self.assertIsNotNone(result_img)
# After inversion + binarization, the output should have higher
# median brightness (white background with dark text)
original_gray = cv2.imread(tmp_path, cv2.IMREAD_GRAYSCALE)
self.assertGreater(float(np.median(result_img)), float(np.median(original_gray)))
os.unlink(result_path)
finally:
os.unlink(tmp_path)
def test_preprocess_keeps_light_frame_orientation(self):
"""Verify light code frame is binarized but not double-inverted."""
try:
import cv2
import numpy as np
except ImportError:
self.skipTest("OpenCV not available")
from skill_seekers.cli.video_models import FrameType
from skill_seekers.cli.video_visual import _preprocess_frame_for_ocr
# Create a light frame (white background, dark text)
light_frame = np.full((100, 200, 3), 240, dtype=np.uint8)
light_frame[40:60, 20:180] = 30 # dark text
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
tmp_path = tmp.name
cv2.imwrite(tmp_path, light_frame)
try:
result_path = _preprocess_frame_for_ocr(tmp_path, FrameType.CODE_EDITOR)
self.assertNotEqual(result_path, tmp_path)
result_img = cv2.imread(result_path, cv2.IMREAD_GRAYSCALE)
self.assertIsNotNone(result_img)
# Light frame should still have high median (white background preserved)
self.assertGreater(float(np.median(result_img)), 128)
os.unlink(result_path)
finally:
os.unlink(tmp_path)
class TestMultiEngineOCR(unittest.TestCase):
"""Tests for multi-engine OCR ensemble voting."""
def test_tesseract_ocr_returns_correct_format(self):
"""Verify _run_tesseract_ocr returns (bbox, text, confidence) tuples."""
try:
import pytesseract # noqa: F401
import cv2
import numpy as np
except ImportError:
self.skipTest("pytesseract or OpenCV not available")
from skill_seekers.cli.video_models import FrameType
from skill_seekers.cli.video_visual import _run_tesseract_ocr
# Create a simple white image with black text
img = np.full((100, 400), 255, dtype=np.uint8)
cv2.putText(img, "def hello():", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, 0, 2)
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
tmp_path = tmp.name
cv2.imwrite(tmp_path, img)
try:
results = _run_tesseract_ocr(tmp_path, FrameType.CODE_EDITOR)
# Results should be a list of tuples
self.assertIsInstance(results, list)
for item in results:
self.assertEqual(len(item), 3)
bbox, text, conf = item
self.assertIsInstance(bbox, list)
self.assertIsInstance(text, str)
self.assertIsInstance(conf, float)
self.assertGreaterEqual(conf, 0.0)
self.assertLessEqual(conf, 1.0)
finally:
os.unlink(tmp_path)
def test_multi_engine_picks_higher_confidence(self):
"""Mock both engines: higher confidence result wins."""
from skill_seekers.cli.video_visual import _pick_better_ocr_result
result_high = ([[0, 0], [100, 0], [100, 20], [0, 20]], "def foo():", 0.9)
result_low = ([[0, 0], [100, 0], [100, 20], [0, 20]], "deff fo()", 0.4)
winner = _pick_better_ocr_result(result_high, result_low)
self.assertEqual(winner[1], "def foo():")
self.assertEqual(winner[2], 0.9)
def test_multi_engine_code_token_preference(self):
"""Result with code tokens preferred over garbage."""
from skill_seekers.cli.video_visual import _pick_better_ocr_result
# Garbage has higher confidence but no code tokens
garbage = ([[0, 0], [100, 0], [100, 20], [0, 20]], "chitd Icrate", 0.8)
code = ([[0, 0], [100, 0], [100, 20], [0, 20]], "def create():", 0.6)
winner = _pick_better_ocr_result(garbage, code)
self.assertEqual(winner[1], "def create():")
def test_multi_engine_single_engine_fallback(self):
"""When one engine returns nothing, use the other."""
from skill_seekers.cli.video_visual import _merge_by_y_bucket
easy_results = [
([[0, 0], [100, 0], [100, 20], [0, 20]], "line one", 0.8),
([[0, 30], [100, 30], [100, 50], [0, 50]], "line two", 0.7),
]
merged = _merge_by_y_bucket(easy_results, [])
# Should return easy_results when tess is empty
# (the function won't be called with both empty — that's handled upstream)
self.assertEqual(len(merged), 2)
class TestClaudeVisionOCR(unittest.TestCase):
"""Tests for Claude Vision API OCR fallback."""
def test_vision_ocr_no_api_key(self):
"""Returns empty when ANTHROPIC_API_KEY is not set."""
from unittest.mock import patch
from skill_seekers.cli.video_models import FrameType
from skill_seekers.cli.video_visual import _ocr_with_claude_vision
with patch.dict(os.environ, {}, clear=True):
# Ensure no ANTHROPIC_API_KEY
os.environ.pop("ANTHROPIC_API_KEY", None)
text, conf = _ocr_with_claude_vision("/fake/path.png", FrameType.CODE_EDITOR)
self.assertEqual(text, "")
self.assertEqual(conf, 0.0)
def test_vision_ocr_success(self):
"""Mock anthropic client returns extracted code."""
import sys
from unittest.mock import MagicMock, patch
from skill_seekers.cli.video_models import FrameType
from skill_seekers.cli.video_visual import _ocr_with_claude_vision
# Create a minimal image file
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
tmp.write(b"\x89PNG\r\n\x1a\n" + b"\x00" * 100)
tmp_path = tmp.name
try:
mock_response = MagicMock()
mock_content = MagicMock()
mock_content.text = "def hello():\n return 'world'"
mock_response.content = [mock_content]
mock_client = MagicMock()
mock_client.messages.create.return_value = mock_response
mock_anthropic = MagicMock()
mock_anthropic.Anthropic.return_value = mock_client
with (
patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}),
patch.dict(sys.modules, {"anthropic": mock_anthropic}),
):
text, conf = _ocr_with_claude_vision(tmp_path, FrameType.CODE_EDITOR)
self.assertIn("def hello():", text)
self.assertEqual(conf, 0.95)
finally:
os.unlink(tmp_path)
def test_vision_fallback_on_low_confidence(self):
"""Vision API is only called when multi-engine conf < 0.5."""
from skill_seekers.cli.video_models import FrameType
from skill_seekers.cli.video_visual import _ocr_with_claude_vision
# Without API key, vision always returns empty — simulating no-fallback
os.environ.pop("ANTHROPIC_API_KEY", None)
text, conf = _ocr_with_claude_vision("/fake.png", FrameType.CODE_EDITOR)
self.assertEqual(text, "")
self.assertEqual(conf, 0.0)
class TestRegionDetection(unittest.TestCase):
"""Tests for IDE panel detection and region-based classification."""
def test_single_panel_no_dividers(self):
"""A uniform frame produces a single full-frame region."""
try:
import cv2
import numpy as np
except ImportError:
self.skipTest("OpenCV not available")
from skill_seekers.cli.video_visual import classify_frame_regions
# Uniform dark frame — no dividers
img = np.full((400, 800, 3), 35, dtype=np.uint8)
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
tmp_path = tmp.name
cv2.imwrite(tmp_path, img)
try:
regions = classify_frame_regions(tmp_path)
self.assertEqual(len(regions), 1)
x1, y1, x2, y2, _ft = regions[0]
self.assertEqual((x1, y1), (0, 0))
self.assertEqual((x2, y2), (800, 400))
finally:
os.unlink(tmp_path)
def test_vertical_divider_splits_panels(self):
"""A bright vertical line creates two separate panels."""
try:
import cv2
import numpy as np
except ImportError:
self.skipTest("OpenCV not available")
from skill_seekers.cli.video_visual import classify_frame_regions
# Dark frame with a bright vertical divider at x=400
img = np.full((600, 800, 3), 35, dtype=np.uint8)
img[:, 398:402] = 200 # 4px bright vertical line
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
tmp_path = tmp.name
cv2.imwrite(tmp_path, img)
try:
regions = classify_frame_regions(tmp_path)
# Should detect at least 2 panels (left and right of divider)
self.assertGreaterEqual(len(regions), 2)
finally:
os.unlink(tmp_path)
def test_find_code_bbox_merges_regions(self):
"""_find_code_bbox merges multiple code panels into one box."""
from skill_seekers.cli.video_models import FrameType
from skill_seekers.cli.video_visual import _find_code_bbox
regions = [
(0, 0, 200, 600, FrameType.CODE_EDITOR),
(200, 0, 800, 600, FrameType.WEBCAM),
(800, 0, 1000, 600, FrameType.CODE_EDITOR),
]
bbox = _find_code_bbox(regions)
self.assertIsNotNone(bbox)
self.assertEqual(bbox, (0, 0, 1000, 600))
def test_find_code_bbox_returns_none_for_no_code(self):
"""_find_code_bbox returns None when no code regions exist."""
from skill_seekers.cli.video_models import FrameType
from skill_seekers.cli.video_visual import _find_code_bbox
regions = [
(0, 0, 800, 600, FrameType.WEBCAM),
(800, 0, 1200, 600, FrameType.DIAGRAM),
]
self.assertIsNone(_find_code_bbox(regions))
def test_small_panels_filtered_out(self):
"""Panels smaller than minimum size thresholds are excluded."""
try:
import cv2
import numpy as np
except ImportError:
self.skipTest("OpenCV not available")
from skill_seekers.cli.video_visual import classify_frame_regions
# Create frame with many thin vertical dividers creating tiny panels
img = np.full((400, 800, 3), 35, dtype=np.uint8)
# Add dividers at x=50, x=100 — creates panels < 200px wide
img[:, 48:52] = 200
img[:, 98:102] = 200
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
tmp_path = tmp.name
cv2.imwrite(tmp_path, img)
try:
regions = classify_frame_regions(tmp_path)
# Tiny panels (< 200px wide) should be filtered out
for x1, _y1, x2, _y2, _ft in regions:
self.assertGreaterEqual(x2 - x1, 200)
finally:
os.unlink(tmp_path)
def test_crop_code_region(self):
"""_crop_code_region saves a cropped version of the frame."""
try:
import cv2
import numpy as np
except ImportError:
self.skipTest("OpenCV not available")
from skill_seekers.cli.video_visual import _crop_code_region
img = np.full((600, 1000, 3), 100, dtype=np.uint8)
# Mark code region with distinct color
img[100:500, 200:800] = 50
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
tmp_path = tmp.name
cv2.imwrite(tmp_path, img)
try:
cropped = _crop_code_region(tmp_path, (200, 100, 800, 500))
self.assertTrue(os.path.exists(cropped))
cropped_img = cv2.imread(cropped)
self.assertEqual(cropped_img.shape[:2], (400, 600))
os.unlink(cropped)
finally:
os.unlink(tmp_path)
class TestPerPanelOCR(unittest.TestCase):
"""Tests for per-panel sub-section OCR tracking."""
def test_get_code_panels_returns_individual_panels(self):
"""_get_code_panels returns separate bboxes instead of merging."""
from skill_seekers.cli.video_models import FrameType
from skill_seekers.cli.video_visual import _get_code_panels
regions = [
(0, 0, 500, 1080, FrameType.CODE_EDITOR),
(500, 0, 1000, 1080, FrameType.CODE_EDITOR),
(1000, 0, 1920, 1080, FrameType.OTHER),
]
panels = _get_code_panels(regions)
self.assertEqual(len(panels), 2)
self.assertEqual(panels[0], (0, 0, 500, 1080))
self.assertEqual(panels[1], (500, 0, 1000, 1080))
def test_get_code_panels_includes_terminals(self):
"""_get_code_panels returns terminal panels too."""
from skill_seekers.cli.video_models import FrameType
from skill_seekers.cli.video_visual import _get_code_panels
regions = [
(0, 0, 960, 540, FrameType.CODE_EDITOR),
(0, 540, 960, 1080, FrameType.TERMINAL),
(960, 0, 1920, 1080, FrameType.OTHER),
]
panels = _get_code_panels(regions)
self.assertEqual(len(panels), 2)
def test_get_code_panels_filters_narrow_panels(self):
"""_get_code_panels drops panels narrower than min_width."""
from skill_seekers.cli.video_models import FrameType
from skill_seekers.cli.video_visual import _get_code_panels
regions = [
(0, 0, 500, 1080, FrameType.CODE_EDITOR), # 500px wide — kept
(500, 0, 1400, 1080, FrameType.CODE_EDITOR), # 900px wide — kept
(1400, 0, 1650, 1080, FrameType.CODE_EDITOR), # 250px wide — dropped
(1650, 0, 1920, 1080, FrameType.CODE_EDITOR), # 270px wide — dropped
]
panels = _get_code_panels(regions)
self.assertEqual(len(panels), 2)
self.assertEqual(panels[0], (0, 0, 500, 1080))
self.assertEqual(panels[1], (500, 0, 1400, 1080))
def test_get_code_panels_custom_min_width(self):
"""_get_code_panels respects custom min_width."""
from skill_seekers.cli.video_models import FrameType
from skill_seekers.cli.video_visual import _get_code_panels
regions = [
(0, 0, 200, 1080, FrameType.CODE_EDITOR), # 200px
(200, 0, 500, 1080, FrameType.CODE_EDITOR), # 300px
]
# Default min_width=300 drops the 200px panel
self.assertEqual(len(_get_code_panels(regions)), 1)
# Custom min_width=100 keeps both
self.assertEqual(len(_get_code_panels(regions, min_width=100)), 2)
def test_frame_subsection_serialization(self):
"""FrameSubSection to_dict/from_dict round-trips correctly."""
from skill_seekers.cli.video_models import (
FrameSubSection,
FrameType,
OCRRegion,
)
ss = FrameSubSection(
bbox=(100, 200, 500, 600),
frame_type=FrameType.CODE_EDITOR,
ocr_text="def hello():\n pass",
ocr_regions=[OCRRegion(text="def hello():", confidence=0.9, bbox=(100, 200, 400, 220))],
ocr_confidence=0.9,
panel_id="panel_0_0",
)
data = ss.to_dict()
restored = FrameSubSection.from_dict(data)
self.assertEqual(restored.bbox, (100, 200, 500, 600))
self.assertEqual(restored.frame_type, FrameType.CODE_EDITOR)
self.assertEqual(restored.ocr_text, "def hello():\n pass")
self.assertEqual(len(restored.ocr_regions), 1)
self.assertAlmostEqual(restored.ocr_confidence, 0.9)
self.assertEqual(restored.panel_id, "panel_0_0")
def test_keyframe_with_sub_sections(self):
"""KeyFrame serialization preserves sub_sections."""
from skill_seekers.cli.video_models import (
FrameSubSection,
FrameType,
KeyFrame,
)
kf = KeyFrame(
timestamp=10.0,
image_path="/tmp/frame.jpg",
frame_type=FrameType.CODE_EDITOR,
sub_sections=[
FrameSubSection(
bbox=(0, 0, 500, 1080),
frame_type=FrameType.CODE_EDITOR,
ocr_text="panel 1 code",
panel_id="panel_0_0",
),
FrameSubSection(
bbox=(500, 0, 1000, 1080),
frame_type=FrameType.CODE_EDITOR,
ocr_text="panel 2 code",
panel_id="panel_0_1",
),
],
)
data = kf.to_dict()
self.assertEqual(len(data["sub_sections"]), 2)
restored = KeyFrame.from_dict(data)
self.assertEqual(len(restored.sub_sections), 2)
self.assertEqual(restored.sub_sections[0].ocr_text, "panel 1 code")
self.assertEqual(restored.sub_sections[1].panel_id, "panel_0_1")
def test_tracker_panel_position_matching(self):
"""Two calls with overlapping x-range bbox match the same block."""
from skill_seekers.cli.video_models import FrameType
from skill_seekers.cli.video_visual import TextBlockTracker
tracker = TextBlockTracker()
code = "def hello():\n return 'world'\n# some code here"
# First frame — left panel
tracker.update(
frame_index=0,
timestamp=1.0,
ocr_text=code,
confidence=0.8,
frame_type=FrameType.CODE_EDITOR,
panel_bbox=(0, 0, 500, 1080),
)
# Second frame — same left panel (slightly shifted)
tracker.update(
frame_index=1,
timestamp=2.0,
ocr_text=code + "\n# added line",
confidence=0.85,
frame_type=FrameType.CODE_EDITOR,
panel_bbox=(0, 0, 510, 1080),
)
blocks = tracker.finalize()
# Should match as one block due to x-range overlap
self.assertEqual(len(blocks), 1)
self.assertEqual(len(blocks[0].frame_indices), 2)
def test_tracker_separate_panels_tracked_separately(self):
"""Two calls with non-overlapping bboxes create separate blocks."""
from skill_seekers.cli.video_models import FrameType
from skill_seekers.cli.video_visual import TextBlockTracker
tracker = TextBlockTracker()
left_code = "def left_func():\n return 'left'\n# left panel code"
right_code = "def right_func():\n return 'right'\n# right panel code"
# Frame 0: left panel
tracker.update(
frame_index=0,
timestamp=1.0,
ocr_text=left_code,
confidence=0.8,
frame_type=FrameType.CODE_EDITOR,
panel_bbox=(0, 0, 500, 1080),
)
# Frame 0: right panel (same frame, different panel)
tracker.update(
frame_index=0,
timestamp=1.0,
ocr_text=right_code,
confidence=0.8,
frame_type=FrameType.CODE_EDITOR,
panel_bbox=(520, 0, 1020, 1080),
)
blocks = tracker.finalize()
self.assertEqual(len(blocks), 2)
# Verify they tracked different content
texts = {b.best_text for b in blocks}
self.assertIn(left_code, texts)
self.assertIn(right_code, texts)
class TestTextGroupPanelId(unittest.TestCase):
"""Tests for panel_id propagation to TextGroup."""
def test_text_group_inherits_panel_id(self):
"""Panel ID propagates from TrackedTextBlock to TextGroup."""
from skill_seekers.cli.video_models import FrameType
from skill_seekers.cli.video_visual import TextBlockTracker
tracker = TextBlockTracker()
code = "class MyClass:\n def method(self):\n pass"
tracker.update(
frame_index=0,
timestamp=1.0,
ocr_text=code,
confidence=0.8,
frame_type=FrameType.CODE_EDITOR,
panel_bbox=(0, 0, 500, 1080),
)
# Complete blocks and assign text groups
tracker.finalize()
groups = tracker.get_text_groups()
# TrackedTextBlock should have panel_bbox set
blocks = tracker._completed_blocks
self.assertEqual(len(blocks), 1)
self.assertEqual(blocks[0].panel_bbox, (0, 0, 500, 1080))
# The text group should exist (but panel_id propagation depends
# on panel_id being set on the block, which requires the extraction
# loop to set it — here we verify the mechanism works)
self.assertTrue(len(groups) >= 1)
def test_text_group_panel_id_serialization(self):
"""TextGroup panel_id survives to_dict/from_dict."""
from skill_seekers.cli.video_models import FrameType, TextGroup
group = TextGroup(
group_id="TG-001",
appearances=[(1.0, 5.0)],
consensus_lines=[{"y_center": 100.0, "text": "hello", "confidence": 0.9}],
frame_type=FrameType.CODE_EDITOR,
panel_id="panel_0_1",
)
data = group.to_dict()
self.assertEqual(data["panel_id"], "panel_0_1")
restored = TextGroup.from_dict(data)
self.assertEqual(restored.panel_id, "panel_0_1")
# =============================================================================
# Video Enhancement Tests
# =============================================================================
class TestVideoEnhanceSourceDetection(unittest.TestCase):
"""Test video source detection in utils and enhance_skill."""
def test_utils_detect_video_source(self):
"""_determine_source_metadata classifies video_ files as video_tutorial."""
from skill_seekers.cli.utils import read_reference_files
# Create a temp skill dir with a video reference file
with tempfile.TemporaryDirectory() as tmpdir:
refs_dir = os.path.join(tmpdir, "references")
os.makedirs(refs_dir)
video_ref = os.path.join(refs_dir, "video_my_tutorial.md")
with open(video_ref, "w") as f:
f.write("# Test Video\n\nSome content")
references = read_reference_files(tmpdir)
self.assertIn("video_my_tutorial.md", references)
self.assertEqual(references["video_my_tutorial.md"]["source"], "video_tutorial")
self.assertEqual(references["video_my_tutorial.md"]["confidence"], "high")
def test_utils_non_video_not_detected(self):
"""Regular reference files are not classified as video_tutorial."""
from skill_seekers.cli.utils import read_reference_files
with tempfile.TemporaryDirectory() as tmpdir:
refs_dir = os.path.join(tmpdir, "references")
os.makedirs(refs_dir)
ref = os.path.join(refs_dir, "api_reference.md")
with open(ref, "w") as f:
f.write("# API Reference\n\nSome content")
references = read_reference_files(tmpdir)
self.assertIn("api_reference.md", references)
self.assertNotEqual(references["api_reference.md"]["source"], "video_tutorial")
class TestVideoEnhancementPrompt(unittest.TestCase):
"""Test video-specific enhancement prompt building."""
def test_is_video_source_true(self):
"""_is_video_source returns True for video_tutorial references."""
from unittest.mock import MagicMock
from skill_seekers.cli.enhance_skill import SkillEnhancer
# Mock the enhancer (skip API key requirement)
enhancer = MagicMock(spec=SkillEnhancer)
enhancer._is_video_source = SkillEnhancer._is_video_source.__get__(enhancer)
refs = {
"video_tutorial.md": {"source": "video_tutorial", "confidence": "high"},
}
self.assertTrue(enhancer._is_video_source(refs))
def test_is_video_source_false(self):
"""_is_video_source returns False for non-video references."""
from unittest.mock import MagicMock
from skill_seekers.cli.enhance_skill import SkillEnhancer
enhancer = MagicMock(spec=SkillEnhancer)
enhancer._is_video_source = SkillEnhancer._is_video_source.__get__(enhancer)
refs = {
"api.md": {"source": "documentation", "confidence": "high"},
}
self.assertFalse(enhancer._is_video_source(refs))
def test_video_prompt_contains_key_instructions(self):
"""Video enhancement prompt contains video-specific instructions."""
from unittest.mock import MagicMock, PropertyMock
from skill_seekers.cli.enhance_skill import SkillEnhancer
enhancer = MagicMock(spec=SkillEnhancer)
enhancer._build_video_enhancement_prompt = (
SkillEnhancer._build_video_enhancement_prompt.__get__(enhancer)
)
type(enhancer).skill_dir = PropertyMock(
return_value=type("P", (), {"name": "test-tutorial"})()
)
refs = {
"video_test.md": {
"source": "video_tutorial",
"confidence": "high",
"content": "# Test\n\n## Segment 1\nTranscript here\n```\nsome code\n```",
"size": 100,
},
}
prompt = enhancer._build_video_enhancement_prompt(refs, "# test\n")
# Check key video-specific sections are present
self.assertIn("OCR Code Reconstruction", prompt)
self.assertIn("Language Detection", prompt)
self.assertIn("Code Timeline", prompt)
self.assertIn("Audio-Visual Alignment", prompt)
self.assertIn("line numbers", prompt.lower())
self.assertIn("UI chrome", prompt)
self.assertIn("GDScript", prompt)
self.assertIn("video_test.md", prompt)
def test_video_prompt_dispatched_automatically(self):
"""_build_enhancement_prompt dispatches to video prompt when video source detected."""
from unittest.mock import MagicMock, PropertyMock
from skill_seekers.cli.enhance_skill import SkillEnhancer
enhancer = MagicMock(spec=SkillEnhancer)
enhancer._is_video_source = SkillEnhancer._is_video_source.__get__(enhancer)
enhancer._build_enhancement_prompt = SkillEnhancer._build_enhancement_prompt.__get__(
enhancer
)
enhancer._build_video_enhancement_prompt = (
SkillEnhancer._build_video_enhancement_prompt.__get__(enhancer)
)
type(enhancer).skill_dir = PropertyMock(return_value=type("P", (), {"name": "my-video"})())
refs = {
"video_tutorial.md": {
"source": "video_tutorial",
"confidence": "high",
"content": "# Video\n\nContent here",
"size": 50,
},
}
prompt = enhancer._build_enhancement_prompt(refs, "# SKILL\n")
# Should use video prompt (has VIDEO TUTORIAL in header)
self.assertIn("VIDEO TUTORIAL", prompt)
self.assertIn("OCR Code Reconstruction", prompt)
class TestVideoWorkflowAutoInjection(unittest.TestCase):
"""Test that video scraper auto-injects video-tutorial workflow."""
def test_workflow_auto_injected(self):
"""When no workflow specified, video-tutorial is injected."""
import argparse
args = argparse.Namespace(
enhance_level=2,
enhance_workflow=None,
enhance_stage=None,
var=None,
workflow_dry_run=False,
api_key=None,
)
# Simulate the auto-injection logic from video_scraper main()
if not getattr(args, "enhance_workflow", None):
args.enhance_workflow = ["video-tutorial"]
self.assertEqual(args.enhance_workflow, ["video-tutorial"])
def test_workflow_not_overridden(self):
"""When user specifies workflow, it is NOT overridden."""
import argparse
args = argparse.Namespace(
enhance_level=2,
enhance_workflow=["custom-workflow"],
enhance_stage=None,
var=None,
workflow_dry_run=False,
api_key=None,
)
# Simulate the auto-injection logic
if not getattr(args, "enhance_workflow", None):
args.enhance_workflow = ["video-tutorial"]
self.assertEqual(args.enhance_workflow, ["custom-workflow"])
def test_video_tutorial_yaml_exists(self):
"""video-tutorial.yaml workflow file is bundled."""
from importlib.resources import files as importlib_files
try:
pkg = importlib_files("skill_seekers.workflows")
yaml_content = pkg.joinpath("video-tutorial.yaml").read_text(encoding="utf-8")
self.assertIn("video-tutorial", yaml_content)
self.assertIn("ocr_code_cleanup", yaml_content)
self.assertIn("video_scraping", yaml_content)
except Exception:
# If package not installed in editable mode, check file directly
import pathlib
yaml_path = (
pathlib.Path(__file__).parent.parent
/ "src"
/ "skill_seekers"
/ "workflows"
/ "video-tutorial.yaml"
)
self.assertTrue(yaml_path.exists(), "video-tutorial.yaml not found")
if __name__ == "__main__":
unittest.main()
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