"""Offline smoke-check for the advisor module wiring (no network required)."""
import math
import sys
import tempfile
from pathlib import Path

sys.path.insert(0, str(Path(__file__).parent.parent))

from rag_eval.advisor.rules import diagnose
from rag_eval.advisor.writer import write_advice, _format_log_summary

# Simulate score_rows with low faithfulness and high noise_sensitivity
rows = [
    {
        "sample_id": f"s{i}",
        "question": f"问题{i}：西门子CT扫描的Flash技术原理是什么？",
        "answer": f"答案{i}：Flash技术采用双源CT扫描",
        "ground_truth": f"标准答案{i}：Flash扫描利用双源CT和大螺距实现超低辐射剂量扫描",
        "faithfulness": 0.3 + i * 0.05,
        "noise_sensitivity": 0.4 + i * 0.02,
        "context_recall": 0.75,
        "semantic_similarity": 0.65,
    }
    for i in range(5)
]

diags = diagnose(rows, metrics=["faithfulness", "noise_sensitivity", "context_recall", "semantic_similarity"])
print(f"Diagnosed {len(diags)} metric(s):")
for d in diags:
    print(f"  {d.metric}: mean={d.mean_score}, severity={d.severity}, low_samples={len(d.low_samples)}")

assert len(diags) >= 2, f"Expected at least 2 diagnoses, got {len(diags)}"
metrics_hit = {d.metric for d in diags}
assert "faithfulness" in metrics_hit, "faithfulness should be triggered"
assert "noise_sensitivity" in metrics_hit, "noise_sensitivity should be triggered"

with tempfile.TemporaryDirectory() as tmp:
    path = Path(tmp) / "optimization_advice.md"
    write_advice(
        diagnoses=diags,
        llm_markdown="",  # fallback mode (no LLM)
        advice_path=path,
        scenario_name="smoke-test-siemens",
        run_id="2026-06-16T00-00-00",
        judge_model="deepseek-v4-flash",
    )
    content = path.read_text(encoding="utf-8")
    assert "smoke-test-siemens" in content, "scenario name missing from report"
    assert "faithfulness" in content, "faithfulness missing from report"
    assert "noise_sensitivity" in content, "noise_sensitivity missing from report"
    print(f"\nAdvice file ({len(content)} chars) — assertions OK")

# Verify log summary format
summary = _format_log_summary(diags, Path("optimization_advice.md"))
print(f"\nLog summary length: {len(summary)} chars, faithfulness present: {'faithfulness' in summary}")
assert "触发诊断" in summary
assert "faithfulness" in summary

print("\nSmoke check PASSED")