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Runtime error
File size: 4,168 Bytes
4dc8e99 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 | from __future__ import annotations
import json
import os
import uuid
from datetime import datetime, timezone
from pathlib import Path
os.environ["DEBUG"] = "false"
from db.models import AnalysisRecord
from services import report_service
def _report_dir() -> Path:
path = Path("backend") / "test_outputs" / f"reports_{uuid.uuid4().hex}"
path.mkdir(parents=True, exist_ok=True)
return path
def _record(payload: dict) -> AnalysisRecord:
return AnalysisRecord(
id=17,
user_id=1,
media_type=payload.get("media_type", "text"),
verdict=payload["verdict"]["label"],
authenticity_score=float(payload["verdict"]["authenticity_score"]),
result_json=json.dumps(payload),
)
def _text_payload() -> dict:
return {
"analysis_id": "analysis-text-17",
"record_id": 17,
"media_type": "text",
"timestamp": datetime(2026, 4, 30, 10, 0, tzinfo=timezone.utc).isoformat(),
"verdict": {
"label": "Very Likely Fake",
"severity": "critical",
"authenticity_score": 14,
"model_confidence": 0.91,
"model_label": "fake",
},
"explainability": {
"original_text": "BREAKING!!! THIS SHOCKING claim is spreading without verified sourcing.",
"fake_probability": 0.86,
"top_label": "fake",
"all_scores": {"fake": 0.86, "real": 0.14},
"keywords": ["claim", "verified sourcing"],
"sensationalism": {
"score": 82,
"level": "High",
"exclamation_count": 3,
"caps_word_count": 2,
"clickbait_matches": 1,
"emotional_word_count": 1,
"superlative_count": 0,
},
"manipulation_indicators": [
{
"pattern_type": "emotional_manipulation",
"matched_text": "SHOCKING",
"start_pos": 17,
"end_pos": 25,
"severity": "high",
"description": "Loaded language is used to heighten emotion before evidence is shown.",
}
],
"detected_language": "en",
},
"llm_summary": {
"paragraph": "The claim uses urgent wording and lacks corroboration from reliable reporting.",
"bullets": ["High sensationalism", "Trusted-source match is weak"],
"model_used": "gemini-test",
},
"trusted_sources": [
{
"source_name": "Reuters",
"title": "Verified reporting about the event",
"url": "https://www.reuters.com/world/example-report",
"description": "Independent report used for comparison.",
"published_at": "2026-04-29",
"relevance_score": 0.92,
}
],
"processing_summary": {
"stages_completed": ["classification", "keyword_extraction", "news_lookup"],
"total_duration_ms": 1842,
"model_used": "test-text-model",
"models_used": ["test-text-model"],
"calibrator_applied": False,
},
"responsible_ai_notice": "DeepShield Responsible-AI Notice",
}
def test_generate_report_creates_pdf_with_active_trusted_source_link(monkeypatch):
monkeypatch.setattr(report_service.settings, "REPORT_DIR", str(_report_dir()))
path = report_service.generate_report(_record(_text_payload()))
data = path.read_bytes()
assert data.startswith(b"%PDF")
assert b"/URI (https://www.reuters.com/world/example-report)" in data
def test_report_pdf_contains_required_text_context_and_pipeline(monkeypatch):
monkeypatch.setattr(report_service.settings, "REPORT_DIR", str(_report_dir()))
path = report_service.generate_report(_record(_text_payload()))
text = path.read_bytes().decode("latin-1", errors="ignore")
assert "Analyzed Media Context" in text
assert "BREAKING!!! THIS SHOCKING claim" in text
assert "classification" in text
assert "news_lookup" in text
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