"""
agents.py — Prefect-orchestrated multi-agent evaluation layer.

Two concurrent agents evaluate each claim:

  1. misinformation_task  → Groq mixtral-8x7b-32768
     Given: claim + top-3 RAG evidence chunks + trust score
     Output: color (red|yellow|green), confidence, explanation, sources

  2. hallucination_task   → Claude Haiku (runs ONLY on AI chat platforms)
     Given: claim text
     Output: color (purple|green), confidence, explanation
     Checks for: fabricated citations, statistical impossibilities,
                 internal contradictions, LLM-specific failure patterns

Both tasks run concurrently via asyncio.gather. Prefect merges results,
picks higher-severity color, returns the final AnalysisResult.

Why Prefect over Celery:
  - Dynamic DAG-based orchestration (no pre-declared task graph)
  - Native async support — no gevent hacks needed
  - Built-in retry with exponential backoff per task
  - Far better observability: every flow run gets a full execution trace
  - Deployable without a separate worker process (embedded server mode)
"""

import asyncio
import time
from typing import Literal

import structlog
from litellm import acompletion
from prefect import flow, task
from prefect.tasks import task_input_hash

from core.config import HighlightColor, Platform, Settings, get_settings
from core.models import AnalysisResult, EvidenceChunk, GrokSensorResult, RAGResult, SourceRef, TrustScore

log = structlog.get_logger(__name__)

# ---------------------------------------------------------------------------
# Color severity ordering (higher index = more severe)
# ---------------------------------------------------------------------------
SEVERITY: dict[HighlightColor, int] = {
    HighlightColor.GREEN: 0,
    HighlightColor.YELLOW: 1,
    HighlightColor.RED: 2,
    HighlightColor.PURPLE: 3,
}

# ---------------------------------------------------------------------------
# LiteLLM prompts
# ---------------------------------------------------------------------------

MISINFO_SYSTEM = """You are a professional fact-checker with access to recent evidence.
Analyze the claim against the evidence chunks and trust score. Output ONLY valid JSON.

Output schema (no markdown, no preamble):
{
  "color": "red" | "yellow" | "green",
  "confidence": <integer 0-100>,
  "explanation": "<2-3 sentence explanation for the hover card>",
  "verdict_label": "<8 words max, e.g. 'Debunked by Reuters and AP'>",
  "sources": ["<url1>", "<url2>", "<url3>"]
}

Color logic:
- "green": Claim is factually accurate, corroborated by ≥2 independent sources, trust score ≥ 0.65
- "yellow": Claim is unverified, breaking news, or evidence is weak/contradictory
- "red": Claim is demonstrably false, debunked by ≥2 sources, OR trust score < 0.25, OR community note active"""

MISINFO_USER_TMPL = """Claim: {claim}

Trust score: {trust_score:.2f} (0=untrustworthy, 1=highly trusted)
Author verified: {verified}
Active Community Note: {has_note}{note_text_part}
Corroborating sources in database: {source_count}

Evidence chunks (cosine similarity descending):
{evidence_text}

Analyze and output JSON."""

HALLUCINATION_SYSTEM = """You are an LLM output auditor specializing in detecting AI hallucinations.
Analyze the following text that was generated by an AI system. Output ONLY valid JSON.

Output schema:
{
  "color": "purple" | "green",
  "confidence": <integer 0-100>,
  "explanation": "<specific explanation of what's wrong, or confirmation it's accurate>"
}

Check for:
1. Fabricated citations: URLs, paper titles, author names that don't exist
2. Statistical impossibilities: numbers that exceed known bounds (e.g., "500% of people")
3. Internal contradictions: statements that contradict each other within the text
4. Temporal paradoxes: referencing future events as past, or anachronistic details
5. Entity confusion: mixing attributes of different real-world entities

Color "purple" only if you find a clear, specific hallucination pattern.
Color "green" if the text appears factually coherent (you cannot verify external facts)."""


# ---------------------------------------------------------------------------
# Prefect tasks — each is independently retried with exponential backoff
# ---------------------------------------------------------------------------

@task(
    name="misinformation-agent",
    retries=2,
    retry_delay_seconds=[1, 3],
    cache_key_fn=task_input_hash,
    cache_expiration=None,
    log_prints=False,
)
async def misinformation_task(
    claim: str,
    evidence: list[EvidenceChunk],
    trust: TrustScore,
    grok: GrokSensorResult,
    settings: Settings,
) -> dict:
    """
    Groq mixtral-8x7b-32768 evaluates the claim against RAG evidence.
    32k context window accommodates all 8 evidence chunks comfortably.
    """
    # Build evidence text block (top-3 by cosine score for the prompt)
    top_evidence = sorted(evidence, key=lambda e: e.score, reverse=True)[:3]
    evidence_text = "\n\n".join(
        f"[{i+1}] Source: {e.domain} (similarity: {e.score:.3f})\n{e.text[:400]}"
        for i, e in enumerate(top_evidence)
    ) or "No evidence chunks retrieved (claim may be too recent or niche)."

    note_part = f"\nCommunity Note: {trust.community_note_text}" if trust.community_note_text else ""

    user_prompt = MISINFO_USER_TMPL.format(
        claim=claim[:500],
        trust_score=trust.score,
        verified=trust.author_verified,
        has_note=trust.has_community_note,
        note_text_part=note_part,
        source_count=trust.corroborating_sources,
        evidence_text=evidence_text,
    )

    # LiteLLM routes to Groq — swap to "openai/gpt-4o" or "groq/llama3-70b-8192"
    # by changing a single string, zero code changes elsewhere
    response = await acompletion(
        model=settings.misinformation_model,
        messages=[
            {"role": "system", "content": MISINFO_SYSTEM},
            {"role": "user", "content": user_prompt},
        ],
        response_format={"type": "json_object"},
        temperature=0.1,
        max_tokens=400,
        api_key=settings.groq_api_key or None,
    )

    import json
    raw = response.choices[0].message.content or "{}"
    return json.loads(raw)


@task(
    name="hallucination-agent",
    retries=2,
    retry_delay_seconds=[1, 3],
    log_prints=False,
)
async def hallucination_task(claim: str, settings: Settings) -> dict:
    """
    Groq llama3-70b-8192 audits AI-generated text for hallucination patterns.
    Previously Claude Haiku — now fully free via Groq, same prompt, same output schema.
    Only invoked when the source platform is an AI chat interface.
    """
    response = await acompletion(
        model=settings.hallucination_model,  # groq/llama3-70b-8192
        messages=[
            {"role": "system", "content": HALLUCINATION_SYSTEM},
            {"role": "user", "content": f"Audit this AI-generated text:\n\n{claim[:1000]}"},
        ],
        response_format={"type": "json_object"},
        temperature=0.0,
        max_tokens=300,
        api_key=settings.groq_api_key or None,
    )

    import json
    raw = response.choices[0].message.content or "{}"
    return json.loads(raw)


def _demo_misinfo_result(trust_score: float, has_note: bool) -> dict:
    """Deterministic demo result when LLM keys are absent."""
    if has_note or trust_score < 0.25:
        return {
            "color": "red", "confidence": 82,
            "explanation": "Demo mode: trust score below threshold and/or active community note detected.",
            "verdict_label": "Low trust signal detected",
            "sources": [],
        }
    elif trust_score < 0.55:
        return {
            "color": "yellow", "confidence": 61,
            "explanation": "Demo mode: insufficient corroboration to confirm or deny this claim.",
            "verdict_label": "Unverified — insufficient evidence",
            "sources": [],
        }
    return {
        "color": "green", "confidence": 78,
        "explanation": "Demo mode: claim appears well-corroborated based on trust graph signals.",
        "verdict_label": "Appears credible",
        "sources": [],
    }


def _demo_hallucination_result() -> dict:
    return {
        "color": "purple", "confidence": 71,
        "explanation": "Demo mode: AI-generated content detected. Unable to verify external citations without live API.",
    }


# ---------------------------------------------------------------------------
# Main Prefect flow
# ---------------------------------------------------------------------------

@flow(name="fact-intelligence-pipeline", log_prints=False)
async def evaluate_claim(
    claim: str,
    claim_hash: str,
    element_id: str,
    platform: Platform,
    rag_result: RAGResult,
    grok_result: GrokSensorResult,
    settings: Settings | None = None,
) -> AnalysisResult:
    """
    Orchestrates the full multi-agent evaluation as a Prefect flow.

    Concurrent execution:
      - misinformation_task always runs
      - hallucination_task runs only for AI chat platforms

    Results are merged by taking the higher-severity color.
    The final AnalysisResult is returned directly (no Celery queue needed).
    """
    cfg = settings or get_settings()
    t0 = time.perf_counter()

    is_ai_platform = platform in (Platform.CHATGPT, Platform.CLAUDE, Platform.GEMINI)

    # Determine whether to use demo mode
    use_demo = cfg.demo_mode or not cfg.has_groq

    if use_demo:
        misinfo_raw = _demo_misinfo_result(rag_result.trust.score, grok_result.community_note)
        halluc_raw = _demo_hallucination_result() if is_ai_platform else None
    else:
        # Concurrently run both agents when applicable
        # Both agents now use Groq (free) — no Anthropic key needed
        if is_ai_platform and cfg.has_groq:
            misinfo_raw, halluc_raw = await asyncio.gather(
                misinformation_task(claim, rag_result.evidence, rag_result.trust, grok_result, cfg),
                hallucination_task(claim, cfg),
            )
        else:
            misinfo_raw = await misinformation_task(
                claim, rag_result.evidence, rag_result.trust, grok_result, cfg
            )
            halluc_raw = None

    # --- Merge results: pick higher-severity color ---
    misinfo_color = HighlightColor(misinfo_raw.get("color", "yellow"))
    final_color = misinfo_color
    final_confidence = misinfo_raw.get("confidence", 50)
    final_explanation = misinfo_raw.get("explanation", "")
    final_verdict = misinfo_raw.get("verdict_label", "Under review")

    if halluc_raw:
        halluc_color = HighlightColor(halluc_raw.get("color", "green"))
        if SEVERITY[halluc_color] > SEVERITY[final_color]:
            final_color = halluc_color
            final_confidence = halluc_raw.get("confidence", final_confidence)
            final_explanation = halluc_raw.get("explanation", final_explanation)
            final_verdict = "AI hallucination detected"

    # Build SourceRef list from evidence + misinfo agent sources
    raw_sources: list[str] = misinfo_raw.get("sources", [])
    evidence_sources = [e.source_url for e in rag_result.evidence[:3] if e.source_url]
    combined = list(dict.fromkeys(raw_sources + evidence_sources))[:3]  # deduplicated, max 3

    source_refs = [
        SourceRef(
            url=url,
            domain=_extract_domain(url),
            favicon_url=f"https://www.google.com/s2/favicons?domain={_extract_domain(url)}&sz=16",
            snippet="",
        )
        for url in combined
    ]

    latency_ms = round((time.perf_counter() - t0) * 1000, 2)

    log.info(
        "agents.flow.complete",
        color=final_color,
        confidence=final_confidence,
        platform=platform,
        latency_ms=latency_ms,
        demo=use_demo,
    )

    return AnalysisResult(
        element_id=element_id,
        content_hash=claim_hash,
        platform=platform,
        color=final_color,
        confidence=final_confidence,
        verdict_label=final_verdict,
        explanation=final_explanation,
        sources=source_refs,
        gatekeeper_label="fact",
        trust_score=rag_result.trust.score,
        velocity=grok_result.velocity,
        has_community_note=grok_result.community_note,
        latency_ms=latency_ms,
    )


def _extract_domain(url: str) -> str:
    try:
        from urllib.parse import urlparse
        return urlparse(url).netloc.lstrip("www.")
    except Exception:
        return url