"""Multi-LLM Explainability Pipeline.

Orchestrates GPT-4o (primary analyzer) + Claude & Gemini (validators)
to produce a hierarchical feature tree explaining why an object was
classified as mission-relevant.
"""

import asyncio
import json
import logging
import os
from typing import Optional

from models.isr.utils import crop_and_encode, encode_frame, parse_llm_json

logger = logging.getLogger(__name__)

# Category color map (synced with frontend)
_CATEGORY_COLORS = {
    "Structure": "#3b82f6",
    "Function": "#06b6d4",
    "Material": "#f59e0b",
    "Color": "#ef4444",
    "Size": "#10b981",
    "Type": "#8b5cf6",
    "Motion": "#ec4899",
    "Context": "#64748b",
    "Shape": "#f97316",
    "Markings": "#a855f7",
}

_PRIMARY_SYSTEM_PROMPT = """You are an ISR (Intelligence, Surveillance, Reconnaissance) analyst explaining WHY a detected object matches or does not match a mission objective.

You will receive:
- A cropped image of the detected object
- The full frame showing spatial context
- Detection metadata (label, confidence, speed, depth, direction)
- The mission objective

Analyze the object and produce a HIERARCHICAL FEATURE TREE explaining the key visual and functional features that led to the classification.

Return ONLY a JSON object (no markdown, no explanation) with this exact structure:
{
  "object": "<detected class label>",
  "satisfies": true/false/null,
  "confidence": 0.0-1.0,
  "reasoning_summary": "<1-2 sentence summary>",
  "categories": [
    {
      "name": "<category name>",
      "features": [
        {
          "name": "<feature name>",
          "value": true/false,
          "reasoning": "<1 sentence explaining this observation>"
        }
      ]
    }
  ]
}

Rules:
- Pick 3-6 categories most relevant to THIS SPECIFIC object from: Structure, Function, Material, Color, Size, Type, Motion, Context, Shape, Markings
- Each category should have 1-4 features (total 5-20 features across all categories)
- Features must be VISUAL OBSERVATIONS from the image, not assumptions
- Be specific and expert-level (a program manager should find this insightful)
- confidence reflects how certain you are about the overall assessment"""

_VALIDATOR_SYSTEM_PROMPT = """You are an ISR analyst reviewing another analyst's feature assessment of a detected object.

You will receive:
- The same cropped image and full frame
- Detection metadata
- The primary analyst's hierarchical feature tree

Your job: independently validate each feature by examining the images yourself.

Return ONLY a JSON object (no markdown) with this structure:
{
  "agreement": true/false,
  "confidence": 0.0-1.0,
  "feature_validations": {
    "CategoryName/FeatureName": {
      "agree": true/false,
      "note": "<brief observation>"
    }
  }
}

Rules:
- Validate EVERY feature in the tree
- Use the key format "CategoryName/FeatureName" exactly
- Be honest — disagree when the image doesn't support the claim
- Keep notes to 1 sentence"""


class ISRExplainer:
    """Orchestrates multi-LLM explanation pipeline for a single track."""

    def __init__(self):
        self._openai_client = None
        self._anthropic_client = None

    def _get_openai(self):
        if self._openai_client is None:
            import openai
            key = os.environ.get("OPENAI_API_KEY")
            if not key:
                raise ValueError("OPENAI_API_KEY not set")
            self._openai_client = openai.OpenAI(api_key=key)
        return self._openai_client

    def _get_anthropic(self):
        if self._anthropic_client is None:
            import anthropic
            key = os.environ.get("ANTHROPIC_API_KEY")
            if not key:
                return None
            self._anthropic_client = anthropic.Anthropic(api_key=key)
        return self._anthropic_client

    async def explain(
        self,
        crop_b64: str,
        frame_b64: str,
        metadata: dict,
        mission: str,
    ) -> dict:
        """Run the full 3-LLM explanation pipeline.

        Args:
            crop_b64: Base64-encoded JPEG of the cropped ROI.
            frame_b64: Base64-encoded JPEG of the full frame.
            metadata: Detection metadata dict (label, score, speed_kph, etc.).
            mission: Mission objective string.

        Returns:
            Merged explanation tree with consensus data.
        """
        # Step 1: GPT-4o primary analysis
        primary_tree = await self._call_gpt(crop_b64, frame_b64, metadata, mission)
        if primary_tree is None:
            raise ValueError("Primary GPT-4o analysis failed")

        # Step 2: Claude + Gemini validation in parallel
        claude_result, gemini_result = await asyncio.gather(
            self._call_claude(crop_b64, frame_b64, metadata, mission, primary_tree),
            self._call_gemini(crop_b64, frame_b64, metadata, mission, primary_tree),
            return_exceptions=True,
        )

        # Handle exceptions from validators
        if isinstance(claude_result, Exception):
            logger.warning("Claude validation failed: %s", claude_result)
            claude_result = None
        if isinstance(gemini_result, Exception):
            logger.warning("Gemini validation failed: %s", gemini_result)
            gemini_result = None

        # Step 3: Merge into consensus tree
        return self._merge(primary_tree, claude_result, gemini_result)

    async def _call_gpt(self, crop_b64: str, frame_b64: str, metadata: dict, mission: str) -> Optional[dict]:
        """Call GPT-4o to generate the primary feature tree."""
        try:
            client = self._get_openai()
            user_text = self._build_metadata_text(metadata, mission)

            response = await asyncio.to_thread(
                client.chat.completions.create,
                model="gpt-4o",
                messages=[
                    {"role": "system", "content": _PRIMARY_SYSTEM_PROMPT},
                    {"role": "user", "content": [
                        {"type": "text", "text": user_text},
                        {"type": "text", "text": "\n[Cropped object]:"},
                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{crop_b64}", "detail": "high"}},
                        {"type": "text", "text": "\n[Full frame context]:"},
                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{frame_b64}", "detail": "low"}},
                    ]},
                ],
                max_tokens=2048,
                temperature=0.3,
            )

            raw = response.choices[0].message.content
            return parse_llm_json(raw)
        except Exception:
            logger.exception("GPT-4o primary analysis failed")
            return None

    async def _call_claude(self, crop_b64: str, frame_b64: str, metadata: dict, mission: str, tree: dict) -> Optional[dict]:
        """Call Claude to validate the primary tree."""
        client = self._get_anthropic()
        if client is None:
            logger.info("Skipping Claude validation — ANTHROPIC_API_KEY not set")
            return None

        try:
            user_text = self._build_metadata_text(metadata, mission)
            user_text += f"\n\nPrimary analyst's feature tree:\n```json\n{json.dumps(tree, indent=2)}\n```"

            response = await asyncio.to_thread(
                client.messages.create,
                model="claude-sonnet-4-20250514",
                max_tokens=1024,
                system=_VALIDATOR_SYSTEM_PROMPT,
                messages=[{
                    "role": "user",
                    "content": [
                        {"type": "text", "text": user_text},
                        {"type": "text", "text": "\n[Cropped object]:"},
                        {"type": "image", "source": {"type": "base64", "media_type": "image/jpeg", "data": crop_b64}},
                        {"type": "text", "text": "\n[Full frame context]:"},
                        {"type": "image", "source": {"type": "base64", "media_type": "image/jpeg", "data": frame_b64}},
                    ],
                }],
            )

            raw = response.content[0].text
            logger.info("Claude raw response: %s", raw[:200] if raw else "empty")
            return parse_llm_json(raw)
        except Exception:
            logger.exception("Claude validation failed")
            return None

    async def _call_gemini(self, crop_b64: str, frame_b64: str, metadata: dict, mission: str, tree: dict) -> Optional[dict]:
        """Call Gemini to validate the primary tree."""
        api_key = os.environ.get("GEMINI_API_KEY")
        if not api_key:
            logger.info("Skipping Gemini validation — GEMINI_API_KEY not set")
            return None

        try:
            import base64
            from google import genai
            from google.genai import types

            client = genai.Client(api_key=api_key)

            user_text = self._build_metadata_text(metadata, mission)
            user_text += f"\n\nPrimary analyst's feature tree:\n```json\n{json.dumps(tree, indent=2)}\n```"

            # Decode images for Gemini
            crop_bytes = base64.b64decode(crop_b64)
            frame_bytes = base64.b64decode(frame_b64)

            response = await asyncio.to_thread(
                client.models.generate_content,
                model="gemini-2.0-flash",
                contents=[
                    types.Content(role="user", parts=[
                        types.Part.from_text(_VALIDATOR_SYSTEM_PROMPT + "\n\n" + user_text),
                        types.Part.from_bytes(data=crop_bytes, mime_type="image/jpeg"),
                        types.Part.from_text("\n[Full frame context]:"),
                        types.Part.from_bytes(data=frame_bytes, mime_type="image/jpeg"),
                    ]),
                ],
                config=types.GenerateContentConfig(
                    max_output_tokens=1024,
                    temperature=0.3,
                ),
            )

            raw = response.text
            logger.info("Gemini raw response: %s", raw[:200] if raw else "empty")
            return parse_llm_json(raw)
        except Exception:
            logger.exception("Gemini validation failed")
            return None

    def _build_metadata_text(self, metadata: dict, mission: str) -> str:
        """Build the text portion describing the detection."""
        lines = [
            f'Mission: "{mission}"',
            "",
            "Detection metadata:",
            f"- Label: {metadata.get('label', 'unknown')}",
            f"- Confidence: {metadata.get('score', 0):.2f}",
            f"- Speed: {metadata.get('speed_kph', 0):.1f} kph",
            f"- Direction: {metadata.get('direction_clock', 'unknown')}",
            f"- Angle: {metadata.get('angle_deg', 'N/A')}°",
        ]
        bbox = metadata.get("bbox")
        if bbox:
            bw = bbox[2] - bbox[0]
            bh = bbox[3] - bbox[1]
            lines.append(f"- Bounding box size: {bw}x{bh} px")
        return "\n".join(lines)

    def _merge(self, tree: dict, claude: Optional[dict], gemini: Optional[dict]) -> dict:
        """Merge primary tree with validator results into consensus output."""
        validators_available = sum(1 for v in [claude, gemini] if v is not None)
        total_features = 0
        agreed = 0

        for cat in tree.get("categories", []):
            cat_name = cat.get("name", "")
            cat["color"] = _CATEGORY_COLORS.get(cat_name, "#64748b")

            for feat in cat.get("features", []):
                total_features += 1
                feat_key = f"{cat_name}/{feat['name']}"
                validators = {}
                feat_agreed = 0

                if claude and "feature_validations" in claude:
                    cv = self._find_validation(claude["feature_validations"], feat_key, feat["name"])
                    if cv:
                        validators["claude"] = cv
                        if cv.get("agree"):
                            feat_agreed += 1

                if gemini and "feature_validations" in gemini:
                    gv = self._find_validation(gemini["feature_validations"], feat_key, feat["name"])
                    if gv:
                        validators["gemini"] = gv
                        if gv.get("agree"):
                            feat_agreed += 1

                feat["validators"] = validators
                feat["consensus"] = feat_agreed

                if validators_available > 0 and feat_agreed == validators_available:
                    agreed += 1

        tree["consensus_bar"] = {
            "total_features": total_features,
            "agreed": agreed,
            "disagreed": total_features - agreed,
            "validators_available": validators_available,
        }

        return tree

    @staticmethod
    def _find_validation(validations: dict, exact_key: str, feat_name: str) -> Optional[dict]:
        """Find validation by exact key first, then fuzzy match on feature name."""
        # Exact match
        val = validations.get(exact_key)
        if val:
            return val
        # Fuzzy: try case-insensitive exact key
        lower_key = exact_key.lower()
        for k, v in validations.items():
            if k.lower() == lower_key:
                return v
        # Fuzzy: match by feature name alone (validator may omit category)
        lower_name = feat_name.lower()
        for k, v in validations.items():
            parts = k.split("/")
            candidate = parts[-1].lower() if parts else k.lower()
            if candidate == lower_name:
                return v
        return None