"""
Per-entity evaluation pipeline.

Builds prompts from entity metadata and delegates LLM evaluation to
the :class:`BatchEvaluator`.  Writes structured results to the
evaluations directory.
"""

from __future__ import annotations

import hashlib
from datetime import datetime
from pathlib import Path
from typing import Callable, Dict, List, Optional

from markitect.infospace.config import InfospaceConfig
from markitect.infospace.evaluation import EntityEvaluation, ScoreEntry
from markitect.infospace.evaluation_io import write_entity_evaluation
from markitect.infospace.models import EntityMeta
from markitect.prompts.execution.batch import BatchEvaluator, BatchItem, BatchSummary
from markitect.prompts.execution.llm_adapter import LLMAdapter
from markitect.prompts.execution.models import RunConfig


_DEFAULT_DIMENSIONS = [
    "definition_precision",
    "source_grounding",
    "domain_relevance",
    "discipline_alignment",
    "conceptual_clarity",
]

_PROMPT_TEMPLATE = """\
You are evaluating an entity from an infospace about "{topic}".

## Entity: {title}

**Slug:** {slug}
**Domain:** {domain}
**Source chapter:** {source_chapter}

### Definition
{definition}

### Context
{context}

## Instructions

Rate this entity on each dimension below using a scale of 1-5 \
(1 = poor, 5 = excellent). For each dimension, provide:
1. A numeric score (1-5)
2. A brief rationale (1-2 sentences)

### Dimensions to evaluate:
{dimensions_list}

## Output format

Return your evaluation as a structured list:

DIMENSION: <name>
SCORE: <1-5>
RATIONALE: <explanation>

Repeat for each dimension.
"""


def build_evaluation_prompt(
    entity: EntityMeta,
    topic: str,
    dimensions: Optional[List[str]] = None,
) -> str:
    """Build an evaluation prompt for a single entity."""
    dims = dimensions or _DEFAULT_DIMENSIONS
    dims_list = "\n".join(f"- {d}" for d in dims)
    return _PROMPT_TEMPLATE.format(
        topic=topic,
        title=entity.title,
        slug=entity.slug,
        domain=entity.domain or "(unspecified)",
        source_chapter=entity.source_chapter or "(unspecified)",
        definition=entity.definition or "(no definition)",
        context=entity.context or "(no context)",
        dimensions_list=dims_list,
    )


def content_digest(entity: EntityMeta) -> str:
    """Compute a content digest for incremental evaluation."""
    content = f"{entity.slug}:{entity.definition}:{entity.context}:{entity.domain}"
    return hashlib.sha256(content.encode()).hexdigest()[:16]


def parse_evaluation_response(
    response_text: str,
    dimensions: Optional[List[str]] = None,
) -> List[ScoreEntry]:
    """Parse structured dimension scores from LLM response text.

    Expects blocks of::

        DIMENSION: <name>
        SCORE: <1-5>
        RATIONALE: <text>
    """
    dims = dimensions or _DEFAULT_DIMENSIONS
    scores: List[ScoreEntry] = []
    current_dim = None
    current_score = None
    current_rationale = ""

    for line in response_text.splitlines():
        stripped = line.strip()
        if stripped.upper().startswith("DIMENSION:"):
            # Flush previous
            if current_dim is not None and current_score is not None:
                scores.append(ScoreEntry(
                    name=current_dim,
                    value=current_score,
                    max_value=5.0,
                    rationale=current_rationale.strip(),
                ))
            current_dim = stripped.split(":", 1)[1].strip()
            current_score = None
            current_rationale = ""
        elif stripped.upper().startswith("SCORE:"):
            try:
                current_score = float(stripped.split(":", 1)[1].strip())
            except ValueError:
                current_score = None
        elif stripped.upper().startswith("RATIONALE:"):
            current_rationale = stripped.split(":", 1)[1].strip()
        elif current_dim is not None and current_score is not None:
            # Continuation of rationale
            if stripped:
                current_rationale += " " + stripped

    # Flush last
    if current_dim is not None and current_score is not None:
        scores.append(ScoreEntry(
            name=current_dim,
            value=current_score,
            max_value=5.0,
            rationale=current_rationale.strip(),
        ))

    return scores


def run_entity_evaluation(
    config: InfospaceConfig,
    entities: List[EntityMeta],
    adapter: LLMAdapter,
    run_config: Optional[RunConfig] = None,
    output_dir: Optional[Path] = None,
    previous_digests: Optional[Dict[str, str]] = None,
    progress_callback: Optional[Callable] = None,
    dimensions: Optional[List[str]] = None,
) -> BatchSummary:
    """Run per-entity evaluation using the batch evaluator.

    Args:
        config: The infospace configuration.
        entities: Entities to evaluate.
        adapter: LLM adapter for evaluation.
        run_config: LLM execution configuration.
        output_dir: Where to write evaluation results.  Defaults to
            ``config.evaluations_dir`` relative to CWD.
        previous_digests: ``{slug: digest}`` for incremental skip.
        progress_callback: Called after each item.
        dimensions: Custom evaluation dimensions.

    Returns:
        A :class:`BatchSummary` with per-entity results.
    """
    topic = config.topic.name
    items = [
        BatchItem(
            key=entity.slug,
            prompt=build_evaluation_prompt(entity, topic, dimensions),
            content_digest=content_digest(entity),
            metadata={"source_path": entity.source_path},
        )
        for entity in entities
    ]

    evaluator = BatchEvaluator(
        adapter=adapter,
        config=run_config,
        progress_callback=progress_callback,
        previous_digests=previous_digests,
    )
    summary = evaluator.evaluate(items)

    # Write successful results
    evaluations_path = output_dir or Path(config.evaluations_dir)
    evaluator_name = (run_config.model_name if run_config else "unknown")

    for result in summary.results:
        if result.status != "success" or result.response is None:
            continue

        scores = parse_evaluation_response(result.response.content, dimensions)
        evaluation = EntityEvaluation(
            entity_slug=result.key,
            evaluator=evaluator_name,
            scores=scores,
            evaluated_at=datetime.utcnow(),
        )
        eval_path = evaluations_path / f"{result.key}.md"
        write_entity_evaluation(evaluation, eval_path)

    return summary