"""
Populate telemetry with simulated traffic, then run drift detection.

Two batches:
  clean  — golden-dataset expected_answers (should match reference distribution)
  dirty  — same questions, hallucinated responses (should show faithfulness drift)

Bypasses the API entirely: runs graders + telemetry.record() directly.

Usage:
    cd /Users/praca/ai-response-validator && .venv/bin/python eval/simulate_traffic.py
"""

import sys
import time
from pathlib import Path

import yaml

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

import telemetry
from config import CLIENT_DOMAIN
from grader import GradeReport, grade

DATASET_PATH = Path(__file__).parent / "golden-dataset.yaml"
KNOWLEDGE_ROOT = Path(__file__).parent.parent / "knowledge"

# Hallucinated responses — plausible-sounding but contradicts KB facts
HALLUCINATED: dict[str, str] = {
    # retail — NovaMart
    "retail-nm-001": (
        "When a product runs out of stock, the system automatically places a reorder after 72 hours "
        "with no alerts sent to any manager. The supplier is notified only at month-end review."
    ),
    "retail-nm-002": (
        "To add a new supplier, send an email to the procurement team with the company name. "
        "No tax ID or payment terms are required at this stage. "
        "Purchase orders can be created immediately without waiting for validation."
    ),
    "retail-nm-003": (
        "Feature flags are permanent once enabled and cannot be disabled without a code deployment. "
        "There is no expiry date or activation scope. Any employee can enable a flag in production."
    ),
    "retail-nm-004": (
        "The authoritative source for product information is the pricing portal. "
        "SKU records are updated manually once per week by the merchandising team. "
        "Archived products can be reactivated instantly by any store manager."
    ),
    "retail-nm-005": (
        "Price changes take effect immediately upon submission with no approval required. "
        "There is no sync window; prices update in real time. "
        "Emergency corrections are handled automatically without escalation."
    ),
    # retail — ShelfWise
    "retail-sw-001": (
        "An out-of-stock alert fires only after a manual stock check is initiated by a store manager. "
        "The alert is sent exclusively to the regional director. "
        "No escalation occurs if the alert is unacknowledged."
    ),
    "retail-sw-002": (
        "Feature toggles are permanent once enabled. "
        "There is no activation scope and no expiry date requirement. "
        "Any user can enable toggles in production without sign-off."
    ),
    "retail-sw-004": (
        "Compliance reports are editable for up to 30 days after creation and are stored for 2 years. "
        "Any user can access compliance reports from the standard dashboard. "
        "Reports are generated on demand only."
    ),
    "retail-sw-005": (
        "Product catalog updates require manual approval for each SKU and can take up to 48 hours. "
        "Deactivated products are permanently deleted and cannot be recovered."
    ),
    # pharma — ClinixOne
    "pharma-cx-001": (
        "Prior authorization is optional and payers respond within 7 business days. "
        "Denied requests cannot be appealed and the prescriber must choose an alternative drug."
    ),
    "pharma-cx-003": (
        "Adverse events must be reported to regulators within 30 days for all event types. "
        "A safety signal is raised automatically by the system when 3 or more events occur. "
        "Expected events do not require regulatory reporting."
    ),
    "pharma-cx-004": (
        "Clinical trials have two phases: Phase I for safety and Phase II for market approval. "
        "Enrollment eligibility is determined by the treating physician with no formal criteria."
    ),
    # pharma — PharmaLink
    "pharma-pl-001": (
        "Formulary pre-approval is automatically granted for all branded drugs. "
        "The payer responds within 30 days and denied requests cannot be appealed."
    ),
    "pharma-pl-003": (
        "The formulary has two tiers: generic and branded. "
        "Moving a drug to a higher tier requires a 7-day notice to prescribers. "
        "Tier assignment is reviewed every 5 years."
    ),
    "pharma-pl-004": (
        "A prescribing pathway is a marketing document produced by pharmaceutical companies. "
        "Pathways are reviewed every 5 years and payers do not use them in coverage decisions. "
        "Deviation from a pathway requires no documentation."
    ),
    "pharma-pl-005": (
        "Enrollment authorization is a formality — patients sign a standard waiver. "
        "Consent is obtained after the first study procedure, not before. "
        "Protocol changes do not require re-consent from existing participants."
    ),
}


def _load_kb_context(domain: str) -> str:
    path = KNOWLEDGE_ROOT / domain / "features.yaml"
    data = yaml.safe_load(path.read_text())
    chunks = [f"[{doc['title']}]\n{doc['content'].strip()}" for doc in data["documents"]]
    return "\n\n".join(chunks)


def _record(pair: dict, response: str, context: str, tag: str) -> GradeReport:
    client = pair["client"]
    report = grade(
        query=pair["question"],
        response=response,
        context=context,
        client=client,
    )
    telemetry.record(
        client=client,
        domain=pair["domain"],
        query_len=len(pair["question"].split()),
        latency_ms={"retrieve": 12.0, "generate": 180.0, "grade": 45.0},
        report=report,
        docs_retrieved=3,
        min_retrieval_score=0.72,
    )
    status = "PASS" if report.overall else "FAIL"
    faith = next(r for r in report.results if r.metric == "faithfulness")
    print(f"  [{tag}] {pair['id']:<20} {status}  faith={faith.score:.3f}  {faith.detail}")
    return report


def run() -> None:
    pairs = yaml.safe_load(DATASET_PATH.read_text())["pairs"]
    kb: dict[str, str] = {}

    # ── Batch 1: clean traffic ──────────────────────────────────────────────
    print("\n── Batch 1: clean traffic (expected answers) ──\n")
    for pair in pairs:
        domain = pair["domain"]
        if domain not in kb:
            kb[domain] = _load_kb_context(domain)
        response = pair["expected_answer"].strip()
        _record(pair, response, kb[domain], "clean")
        time.sleep(0.05)

    # ── Batch 2: dirty traffic (hallucinated responses) ─────────────────────
    print("\n── Batch 2: dirty traffic (hallucinated responses) ──\n")
    dirty_pairs = [p for p in pairs if p["id"] in HALLUCINATED]
    for pair in dirty_pairs:
        domain = pair["domain"]
        response = HALLUCINATED[pair["id"]]
        _record(pair, response, kb[domain], "dirty")
        time.sleep(0.05)

    total = telemetry.live_stats()["total_queries"]
    print(f"\nTelemetry buffer: {total} events ({len(pairs)} clean + {len(dirty_pairs)} dirty)\n")

    # ── Drift detection ─────────────────────────────────────────────────────
    print("=" * 60)
    print("Running drift detection vs golden-dataset baseline...")
    print("=" * 60)

    sys.path.insert(0, str(Path(__file__).parent))
    from drift import build_current, build_reference, detect_drift, report_drift

    print("\nBuilding reference distribution...")
    reference = build_reference()

    current = build_current()
    cur_n = len(next(iter(current.values()), []))
    print(f"Reference: {len(next(iter(reference.values())))} pairs")
    print(f"Current:   {cur_n} events\n")

    results = detect_drift(current, reference)
    report_drift(results)
    print()


if __name__ == "__main__":
    run()