import json from autoshorts.claims.fact_gate import Claim, ClaimType, validate_claims_for_candidate from autoshorts.cli.demo_review_batch_v2 import build_demo_review_batch_v2, main from autoshorts.compliance.ai_disclosure import ( AIDisclosureReview, DisclosureValue, SyntheticMediaType, validate_ai_disclosure_review, ) from autoshorts.content.to_candidate_v2 import ScriptInputV2, convert_to_candidate_v2 from autoshorts.review.approvals_v2 import parse_approval_command_v2 from autoshorts.review.workflow_v2 import build_review_batch_v2, classify_candidate_v2 from autoshorts.strategy.spine_v2 import ( AIDisclosureDecision, AudienceNeed, ClaimRisk, ContentBriefV2, ExperimentHypothesis, HumanTexture, SaturationRisk, StrategyScoreV2, VideoCandidateV2, ViewerEmotion, build_demo_candidates_v2, build_review_package_v2, render_review_package_v2, validate_candidate_v2, ) def hypothesis() -> ExperimentHypothesis: return ExperimentHypothesis("Tests whether visible fail examples drive saves.", "saves_per_view") def brief(**overrides) -> ContentBriefV2: data = dict( brief_id="meeting-summary-brief", series_id="ai_output_autopsy", topic="Meeting summary misses decision target", audience_need=AudienceNeed.CLARITY, job_to_be_done="When I summarize a meeting, I want decision evidence, so I can act.", viewer_identity="office_worker", emotion_before=ViewerEmotion.CONFUSED, emotion_after=ViewerEmotion.IN_CONTROL, audience_problem="Generic AI summary misses the decision.", payoff="Ask for decision evidence.", search_intent_phrase="better meeting summary prompt", saturation_risk=SaturationRisk.MEDIUM, human_texture=HumanTexture.MINI_CASE, claim_risk=ClaimRisk.LOW, ai_disclosure_required=AIDisclosureDecision.NO, experiment_hypothesis=hypothesis(), platforms=("youtube_shorts", "tiktok", "instagram_reels", "linkedin_manual"), target_duration_seconds=38, ) data.update(overrides) return ContentBriefV2(**data) def script(**overrides) -> ScriptInputV2: data = dict( title="Your AI summary has no decision target", hook="Your AI summary is useless because it has no decision target.", first_frame="Split-screen: transcript vs. useless summary stamped USELESS.", visible_fail="The AI summary says great discussion but misses the launch decision.", anti_example="A useless AI summary that says great discussion and lists no decision, owner, or blocker.", mechanism="AI compresses everything equally when no decision target exists.", fix="Name the decision and ask for blockers, owners, risks, and next steps.", before_after="Before: generic summary. After: decision evidence.", takeaway="Don't ask for a summary. Ask for decision evidence.", save_reason="Viewer can copy the decision-evidence sentence.", share_reason="Every office worker knows bad meeting summaries.", follow_reason="Follow for small systems that make digital work clearer.", human_texture_note="messy meeting transcript with launch decision", claims=(Claim("Decision-targeted summaries preserve relevance better than generic summaries.", ClaimType.OPINION, risk=ClaimRisk.LOW),), platform_fit={"youtube_shorts": "strong", "tiktok": "strong", "instagram_reels": "medium", "linkedin_manual": "strong silent-autoplay fit"}, linkedin_title="A better way to brief AI meeting summaries", linkedin_caption="Most AI meeting summaries fail because they protect no decision. Name the decision first; then ask for blockers, owners, risks, and next steps.", risks=("medium saturation",), visual_structure="motion-led compression funnel with visible transcript conflict", render_backend_candidate="python_motion", ) data.update(overrides) return ScriptInputV2(**data) def candidate(**overrides) -> VideoCandidateV2: return convert_to_candidate_v2(brief(), script(**overrides)) def test_to_candidate_v2_creates_valid_candidate_with_linkedin_manual_fit(): cand = candidate() assert cand.candidate_id == "ai-output-autopsy-your-ai-summary-has-no-decision-target-38s" assert "linkedin_manual" in cand.platforms assert cand.linkedin_caption.startswith("Most AI meeting summaries") assert validate_candidate_v2(cand).is_valid def test_workflow_v2_classifies_ready_blocked_and_invalid_candidates(): ready = candidate() blocked = candidate(human_texture_note="") invalid = candidate(series_id_override="not-real") assert classify_candidate_v2(ready).status == "ready_for_review" blocked_item = classify_candidate_v2(blocked) assert blocked_item.status == "blocked" assert "human_texture_note is required" in blocked_item.reasons invalid_item = classify_candidate_v2(invalid) assert invalid_item.status == "invalid" assert any("unknown series_id" in reason for reason in invalid_item.reasons) batch = build_review_batch_v2((ready, blocked, invalid)) assert len(batch.ready_for_review) == 1 assert len(batch.blocked) == 1 assert len(batch.invalid) == 1 def test_review_package_v2_json_is_deterministic_and_roundtrips(): package = build_review_package_v2(candidate()) first = package.to_json() second = package.to_json() loaded = json.loads(first) assert first == second assert list(loaded.keys())[:4] == ["candidate_id", "version", "script_hash", "media_hash"] assert loaded["linkedin_caption"].startswith("Most AI meeting summaries") assert package == package.from_dict(loaded) def test_approval_parser_v2_requires_exact_candidate_version_and_hash(): package = build_review_package_v2(candidate()) assert parse_approval_command_v2(package.approval_command, package).action == "approve" assert parse_approval_command_v2(package.approval_command.replace(package.script_hash, "badbadbad"), package).action == "blocked" assert parse_approval_command_v2("APPROVE missing v2.0.0 abc", package).action == "blocked" assert parse_approval_command_v2("passt schon", package).action == "needs_clarification" def test_score_v2_is_calibrated_and_not_automatically_100(): solid = candidate() score = StrategyScoreV2.from_candidate(solid) assert 75 <= score.total <= 93 assert score.breakdown["human_texture"].points < score.breakdown["human_texture"].max_points weak_texture = StrategyScoreV2.from_candidate(candidate(human_texture=HumanTexture.SOURCE_BASED_CLAIM, human_texture_note="source-based claim only")) generic = StrategyScoreV2.from_candidate(candidate(hook="This AI tool will change your life.")) saturated = StrategyScoreV2.from_candidate(candidate(saturation_risk=SaturationRisk.HIGH)) weak_frame = StrategyScoreV2.from_candidate(candidate(first_frame="Plain title card with text only")) assert weak_texture.total < score.total assert generic.total < score.total assert saturated.total < score.total assert weak_frame.total < score.total assert "First Frame" in render_review_package_v2(build_review_package_v2(solid)) assert "Score Breakdown:" in render_review_package_v2(build_review_package_v2(solid)) def test_anti_example_required_for_autopsy_teardown_red_flags_and_decision_design(): for series_id in ("ai_output_autopsy", "workflow_teardown", "digital_red_flags", "decision_design"): cand = convert_to_candidate_v2(brief(series_id=series_id), script(anti_example="")) result = validate_candidate_v2(cand) assert not result.is_valid assert "anti_example is required for this series" in result.errors attention = convert_to_candidate_v2( brief(series_id="attention_traps"), script(anti_example="", concrete_scene="phone opened for one task, feed redirects attention"), ) assert validate_candidate_v2(attention).is_valid def test_human_texture_note_is_required_when_human_texture_is_not_none(): result = validate_candidate_v2(candidate(human_texture_note="")) assert not result.is_valid assert "human_texture_note is required" in result.errors def test_claim_gate_blocks_sourced_and_risky_claims_without_source_but_allows_opinion(): assert validate_claims_for_candidate(candidate(claims=(Claim("This is a practical rule.", ClaimType.OPINION, risk=ClaimRisk.LOW),))).is_valid sourced = validate_claims_for_candidate(candidate(claims=(Claim("Platform says X.", ClaimType.SOURCED_FACT, risk=ClaimRisk.MEDIUM),))) risky = validate_claims_for_candidate(candidate(claims=(Claim("This prevents scams.", ClaimType.RISKY_CLAIM, risk=ClaimRisk.HIGH),))) experience = validate_claims_for_candidate(candidate(human_texture_note="", claims=(Claim("We tested this.", ClaimType.EXPERIENCE, risk=ClaimRisk.LOW),))) assert "source is required for sourced_fact" in sourced.errors assert "source is required for risky_claim" in risky.errors assert "high risk claim requires source" in risky.errors assert "experience claim requires human_texture_note" in experience.errors def test_ai_disclosure_unclear_blocks_and_realistic_media_requires_manual_review(): unclear = AIDisclosureReview( youtube=DisclosureValue.UNCLEAR, tiktok=DisclosureValue.NO, instagram=DisclosureValue.NO, linkedin=DisclosureValue.NO, reason="unclear synthetic background", synthetic_media_type=SyntheticMediaType.UNCLEAR, ) realistic = AIDisclosureReview( youtube=DisclosureValue.YES, tiktok=DisclosureValue.YES, instagram=DisclosureValue.YES, linkedin=DisclosureValue.YES, reason="realistic generated person", synthetic_media_type=SyntheticMediaType.REALISTIC_PERSON, manual_review_required=False, ) text_only = AIDisclosureReview.text_only_assistance("script and captions only") assert not validate_ai_disclosure_review(unclear).is_valid assert "unclear disclosure blocks rendering" in validate_ai_disclosure_review(unclear).errors assert not validate_ai_disclosure_review(realistic).is_valid assert "realistic synthetic media requires manual_review_required" in validate_ai_disclosure_review(realistic).errors assert validate_ai_disclosure_review(text_only).is_valid def test_demo_review_batch_v2_limits_to_three_candidates_and_cli_outputs_json(capsys): batch = build_demo_review_batch_v2(limit=3) assert len(batch.ready_for_review) <= 3 exit_code = main(["--json"], stdout=None) captured = capsys.readouterr().out assert exit_code == 0 payload = json.loads(captured) assert len(payload["ready_for_review"]) <= 3 assert payload["side_effects"] == [] assert payload["external_calls"] == []