"""
Threat Chat Module - GPT-powered Q&A about detected threats.
"""

import logging
from typing import List, Dict, Any

from utils.openai_client import chat_completion, extract_content, get_api_key, OpenAIAPIError
from utils.gpt_reasoning import _DOMAIN_ROLES

logger = logging.getLogger(__name__)


def chat_about_threats(
    question: str,
    detections: List[Dict[str, Any]],
    mission_spec_dict: Dict[str, Any] = None,
) -> str:
    """
    Answer user questions about detected threats using GPT.

    Args:
        question: User's question about the current threat situation.
        detections: List of detection dicts with gpt_raw threat analysis.
        mission_spec_dict: Optional dict of mission specification fields.

    Returns:
        GPT's response as a string.
    """
    if not get_api_key():
        logger.warning("OPENAI_API_KEY not set. Cannot process threat chat.")
        return "Error: OpenAI API key not configured."

    if not detections:
        return "No threats detected yet. Run detection first to analyze the scene."

    # Build threat context from detections
    threat_context = _build_threat_context(detections)

    # Domain-aware role selection
    domain = "GENERIC"
    if mission_spec_dict:
        domain = mission_spec_dict.get("domain", "GENERIC")
    role_label = _DOMAIN_ROLES.get(domain, _DOMAIN_ROLES["GENERIC"])

    # Build mission context block (INV-8: mission context forwarded to LLM calls)
    mission_block = ""
    if mission_spec_dict:
        mission_block = "\nMISSION CONTEXT:\n"
        if mission_spec_dict.get("mission_intent"):
            mission_block += f"- Intent: {mission_spec_dict['mission_intent']}\n"
        if mission_spec_dict.get("domain"):
            mission_block += f"- Domain: {mission_spec_dict['domain']}\n"
        if mission_spec_dict.get("object_classes"):
            mission_block += f"- Target Classes: {', '.join(mission_spec_dict['object_classes'])}\n"
        if mission_spec_dict.get("context_phrases"):
            mission_block += f"- Situation: {'; '.join(mission_spec_dict['context_phrases'])}\n"
        mission_block += "\n"

    system_prompt = (
        f"You are a {role_label} providing real-time threat analysis support. "
        "You have access to the current threat assessment data from optical surveillance. "
        "Answer questions concisely and tactically. Use military terminology where appropriate. "
        "If asked about engagement recommendations, always note that final decisions rest with the commanding officer.\n\n"
        f"{mission_block}"
        "CURRENT THREAT PICTURE:\n"
        f"{threat_context}\n\n"
        "Respond to the operator's question based on this threat data."
    )

    payload = {
        "model": "gpt-4o",
        "messages": [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": question}
        ],
        "max_tokens": 500,
        "temperature": 0.3,
    }

    try:
        resp_data = chat_completion(payload)
        content, _refusal = extract_content(resp_data)
        return content.strip() if content else "No response generated."

    except OpenAIAPIError as e:
        logger.error("OpenAI API error: %s", e)
        return f"API Error: {e}"
    except Exception as e:
        logger.error("Threat chat failed: %s", e)
        return f"Error processing question: {str(e)}"


def _build_threat_context(detections: List[Dict[str, Any]]) -> str:
    """Build a text summary of all detected threats for GPT context."""
    lines = []

    for det in detections:
        obj_id = det.get("id", "Unknown")
        label = det.get("label", "object")

        # Extract GPT raw data if available
        gpt_raw = det.get("gpt_raw") or det.get("features") or {}

        # Universal schema fields (with fallbacks to legacy names)
        obj_type = gpt_raw.get("object_type") or gpt_raw.get("vessel_category", label)
        size = gpt_raw.get("size", "")
        threat_score = (
            det.get("threat_level_score")
            or gpt_raw.get("threat_level")
            or gpt_raw.get("threat_level_score", "?")
        )
        threat_class = (
            det.get("threat_classification")
            or gpt_raw.get("threat_classification", "Unknown")
        )
        weapons = gpt_raw.get("visible_weapons", [])
        weapon_ready = gpt_raw.get("weapon_readiness") or det.get("weapon_readiness", "Unknown")
        motion = gpt_raw.get("motion_status", "Unknown")
        range_est = gpt_raw.get("range_estimate") or gpt_raw.get("range_estimation_nm", "")
        bearing = gpt_raw.get("bearing") or gpt_raw.get("bearing_clock") or det.get("gpt_direction", "")
        intent = gpt_raw.get("tactical_intent", "")
        dynamic_features = gpt_raw.get("dynamic_features", [])

        # Build entry
        entry = f"[{obj_id}] {obj_type}"
        if size and size != "Unknown":
            entry += f" ({size})"
        entry += f"\n  - Threat: {threat_class} (Score: {threat_score}/10)"

        if range_est:
            entry += f"\n  - Range: {range_est}"
        if bearing and bearing != "Unknown":
            entry += f", Bearing: {bearing}"
        if motion and motion != "Unknown":
            entry += f"\n  - Motion: {motion}"
        if weapons:
            entry += f"\n  - Weapons: {', '.join(weapons) if isinstance(weapons, list) else weapons}"
        if weapon_ready and weapon_ready != "Unknown":
            entry += f" ({weapon_ready})"
        if intent:
            entry += f"\n  - Assessed Intent: {intent}"

        # Append dynamic features
        for feat in dynamic_features:
            if isinstance(feat, dict):
                key = feat.get("key", "")
                value = feat.get("value", "")
                if key and value:
                    entry += f"\n  - {key}: {value}"

        lines.append(entry)

    return "\n\n".join(lines) if lines else "No threat data available."