"""Content Intelligence Service: classify brief content into typed blocks for slide mapping."""
import re
from typing import Any, Dict, List, Optional

from models.content_models import (
    ClassifiedContent,
    ContentBlock,
    ContentBlockType,
)
from models.llm_message import LLMSystemMessage, LLMUserMessage
from services.attachment_parser_service import ImageInfo, TableData
from services.llm_client import LLMClient
from services.score_based_chunker import ScoreBasedChunker
from utils.llm_provider import get_model

# --- Regex patterns for rule-based classification ---

_METRIC_RE = re.compile(
    r"""
    (?:                             # value-first: $2.3M, 45%, 1,200 units
        [\$€£¥]\s?\d[\d,.]*[KMBTkmbt%]? |
        \d[\d,.]*\s?%               |
        \d[\d,.]*\s?[KMBTkmbt]\b
    )
    |
    (?:                             # "grew 45%", "increased by $2M"
        (?:grew|growth|increased?|decreased?|rose|fell|dropped|declined|revenue|profit|margin|roi|cagr|arpu)
        .{0,30}?
        [\$€£¥]?\d[\d,.]*[KMBTkmbt%]?
    )
    """,
    re.IGNORECASE | re.VERBOSE,
)

_QUOTE_RE = re.compile(
    r'["\u201c\u201d].{15,300}?["\u201c\u201d]'  # 15-300 chars inside quotes
    r"(?:\s*[-\u2014\u2013]\s*.{2,60})?",  # optional attribution
    re.DOTALL,
)

_TABLE_RE = re.compile(r"^\|.+\|$", re.MULTILINE)

_TIMELINE_RE = re.compile(
    r"(?:(?:19|20)\d{2}|Q[1-4]|(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\w*\s+\d{4})",
    re.IGNORECASE,
)

_COMPARISON_RE = re.compile(
    r"\b(?:vs\.?|versus|compared?\s+to|in\s+contrast|on\s+the\s+other\s+hand|whereas|alternatively)\b",
    re.IGNORECASE,
)

_LIST_RE = re.compile(r"^[\s]*[-*•]\s+.+", re.MULTILINE)
_NUMBERED_LIST_RE = re.compile(r"^[\s]*\d+[.)]\s+.+", re.MULTILINE)

_IMAGE_REF_RE = re.compile(
    r"(?:!\[|see\s+(?:figure|image|diagram|chart|photo)|attached\s+image|\.(?:png|jpg|jpeg|gif|webp|svg)\b)",
    re.IGNORECASE,
)

_CTA_RE = re.compile(
    r"\b(?:contact\s+us|get\s+started|sign\s+up|learn\s+more|next\s+steps|action\s+items|call\s+to\s+action|let's\s+(?:discuss|connect|talk))\b",
    re.IGNORECASE,
)

# Priority map: higher = more important for presentation
_PRIORITY_MAP = {
    ContentBlockType.metric: 8,
    ContentBlockType.quote: 7,
    ContentBlockType.table: 6,
    ContentBlockType.timeline: 6,
    ContentBlockType.comparison: 6,
    ContentBlockType.call_to_action: 7,
    ContentBlockType.list_items: 5,
    ContentBlockType.image_reference: 5,
    ContentBlockType.narrative: 4,
}


class ContentIntelligenceService:

    def __init__(self):
        self._chunker = ScoreBasedChunker()

    async def classify(
        self,
        markdown: str,
        tables: Optional[List[TableData]] = None,
        images: Optional[List[ImageInfo]] = None,
    ) -> ClassifiedContent:
        """Classify markdown content into typed content blocks."""
        tables = tables or []
        images = images or []

        # 1. Extract a title (first heading, if any)
        title = self._extract_title(markdown)

        # 2. Chunk the content
        chunks = await self._chunk_content(markdown)

        # 3. Rule-based classification per chunk
        blocks: List[ContentBlock] = []
        ambiguous_chunks: List[tuple] = []  # (index, text) for LLM classification

        for chunk in chunks:
            text = f"{chunk.heading}\n{chunk.content}".strip()
            block_type = self._classify_by_rules(text)

            if block_type:
                extracted = self._extract_data(block_type, text)
                blocks.append(
                    ContentBlock(
                        type=block_type,
                        raw_text=text,
                        extracted_data=extracted,
                        source_section=chunk.heading.lstrip("# ").strip(),
                        priority=_PRIORITY_MAP.get(block_type, 4),
                    )
                )
            else:
                ambiguous_chunks.append((len(blocks), text))
                # Placeholder — will be replaced after LLM classification
                blocks.append(
                    ContentBlock(
                        type=ContentBlockType.narrative,
                        raw_text=text,
                        source_section=chunk.heading.lstrip("# ").strip(),
                        priority=4,
                    )
                )

        # 4. LLM batch classification for ambiguous blocks
        if ambiguous_chunks:
            llm_types = await self._llm_classify_batch(
                [text for _, text in ambiguous_chunks]
            )
            for (idx, text), btype in zip(ambiguous_chunks, llm_types):
                extracted = self._extract_data(btype, text)
                blocks[idx] = ContentBlock(
                    type=btype,
                    raw_text=text,
                    extracted_data=extracted,
                    source_section=blocks[idx].source_section,
                    priority=_PRIORITY_MAP.get(btype, 4),
                )

        # 5. Merge attachment data
        for td in tables:
            blocks.append(
                ContentBlock(
                    type=ContentBlockType.table,
                    raw_text=f"Table: {td.title or td.sheet_name or 'Data'}\n"
                    f"Headers: {', '.join(td.headers)}\n"
                    f"Rows: {len(td.rows)}",
                    extracted_data={"headers": td.headers, "row_count": len(td.rows)},
                    source_section=td.title or td.sheet_name,
                    priority=_PRIORITY_MAP[ContentBlockType.table],
                )
            )

        for img in images:
            blocks.append(
                ContentBlock(
                    type=ContentBlockType.image_reference,
                    raw_text=f"Image: {img.filename}",
                    extracted_data={
                        "file_path": img.file_path,
                        "width": img.width,
                        "height": img.height,
                    },
                    source_section=None,
                    priority=_PRIORITY_MAP[ContentBlockType.image_reference],
                )
            )

        # 6. Sort by priority (descending), preserving order for same priority
        blocks.sort(key=lambda b: -b.priority)

        # 7. Generate summary
        summary = await self._generate_summary(markdown, blocks)

        return ClassifiedContent(
            title=title,
            blocks=blocks,
            tables=tables,
            images=images,
            summary=summary,
        )

    async def ask_followup_questions(
        self, content: ClassifiedContent
    ) -> Optional[List[str]]:
        """Ask follow-up questions if content is too thin."""
        total_words = sum(len(b.raw_text.split()) for b in content.blocks)
        if total_words >= 200 and len(content.blocks) >= 3:
            return None

        client = LLMClient()
        model = get_model()

        block_summary = "\n".join(
            f"- [{b.type.value}] {b.raw_text[:100]}..." for b in content.blocks[:10]
        )

        messages = [
            LLMSystemMessage(
                content="You help identify missing information for a presentation brief. "
                "Return a JSON array of 2-4 short questions that would help create a more complete presentation."
            ),
            LLMUserMessage(
                content=f"The user provided a brief with {total_words} words and {len(content.blocks)} content blocks:\n\n"
                f"{block_summary}\n\n"
                "What additional information would be helpful?"
            ),
        ]

        schema = {
            "type": "object",
            "properties": {
                "questions": {
                    "type": "array",
                    "items": {"type": "string"},
                    "minItems": 2,
                    "maxItems": 4,
                }
            },
            "required": ["questions"],
        }

        try:
            result = await client.generate_structured(
                model=model,
                messages=messages,
                response_format=schema,
            )
            return result.get("questions", [])
        except Exception:
            return None

    # --- Internal methods ---

    def _extract_title(self, markdown: str) -> Optional[str]:
        for line in markdown.split("\n"):
            stripped = line.strip()
            if stripped.startswith("# ") and not stripped.startswith("## "):
                return stripped.lstrip("# ").strip()
        return None

    async def _chunk_content(self, markdown: str):
        """Chunk using ScoreBasedChunker. Fall back to paragraph splitting."""
        try:
            headings = self._chunker.extract_headings(markdown)
            if len(headings) >= 2:
                scores = self._chunker.score_headings(headings)
                chunks = self._chunker.get_chunks_from_headings(
                    markdown, headings, scores, top_k=30
                )
                if chunks:
                    return chunks
        except Exception:
            pass

        # Fallback: split by double newlines (paragraph-based)
        from models.document_chunk import DocumentChunk

        paragraphs = [p.strip() for p in re.split(r"\n{2,}", markdown) if p.strip()]
        return [
            DocumentChunk(
                heading=f"Section {i + 1}",
                content=p,
                heading_index=i,
                score=5.0,
            )
            for i, p in enumerate(paragraphs)
            if len(p) > 20
        ]

    def _classify_by_rules(self, text: str) -> Optional[ContentBlockType]:
        """Apply rule-based classification. Returns None if ambiguous."""
        # Check from most specific to least
        if _QUOTE_RE.search(text):
            return ContentBlockType.quote

        if _TABLE_RE.search(text):
            return ContentBlockType.table

        if _IMAGE_REF_RE.search(text):
            return ContentBlockType.image_reference

        if _CTA_RE.search(text):
            return ContentBlockType.call_to_action

        metric_matches = _METRIC_RE.findall(text)
        if len(metric_matches) >= 2:
            return ContentBlockType.metric

        timeline_matches = _TIMELINE_RE.findall(text)
        if len(timeline_matches) >= 2:
            return ContentBlockType.timeline

        if _COMPARISON_RE.search(text):
            return ContentBlockType.comparison

        list_matches = _LIST_RE.findall(text)
        numbered_matches = _NUMBERED_LIST_RE.findall(text)
        if len(list_matches) >= 3 or len(numbered_matches) >= 3:
            return ContentBlockType.list_items

        # Single metric mention
        if metric_matches:
            return ContentBlockType.metric

        return None  # Ambiguous — defer to LLM

    def _extract_data(
        self, block_type: ContentBlockType, text: str
    ) -> Optional[Dict[str, Any]]:
        """Extract structured data from a content block based on its type."""
        if block_type == ContentBlockType.metric:
            return self._extract_metric_data(text)
        if block_type == ContentBlockType.quote:
            return self._extract_quote_data(text)
        return None

    def _extract_metric_data(self, text: str) -> Dict[str, Any]:
        """Extract numeric values and labels from metric text."""
        metrics = []
        # Pattern: label ... value
        for match in re.finditer(
            r"([\w\s]+?)\s*(?::|is|was|reached|hit|grew\s+to|of)\s*"
            r"([\$€£¥]?\s?\d[\d,.]*\s?[KMBTkmbt%]*)",
            text,
            re.IGNORECASE,
        ):
            label = match.group(1).strip()
            value = match.group(2).strip()
            if len(label) < 50:
                metrics.append({"label": label, "value": value})

        # Fallback: just extract all numbers with context
        if not metrics:
            for match in _METRIC_RE.finditer(text):
                metrics.append({"value": match.group().strip()})

        return {"metrics": metrics[:10]}

    def _extract_quote_data(self, text: str) -> Dict[str, Any]:
        """Extract quote text and attribution."""
        match = _QUOTE_RE.search(text)
        if match:
            full = match.group()
            # Try to split attribution
            parts = re.split(r"\s*[-\u2014\u2013]\s*", full, maxsplit=1)
            quote_text = parts[0].strip().strip('"\u201c\u201d')
            attribution = parts[1].strip() if len(parts) > 1 else None
            return {"quote": quote_text, "attribution": attribution}
        return {}

    async def _llm_classify_batch(
        self, texts: List[str]
    ) -> List[ContentBlockType]:
        """Use LLM to classify a batch of ambiguous text chunks."""
        if not texts:
            return []

        client = LLMClient()
        model = get_model()

        types_list = ", ".join(t.value for t in ContentBlockType)
        numbered = "\n".join(f"{i + 1}. {t[:300]}" for i, t in enumerate(texts))

        messages = [
            LLMSystemMessage(
                content=f"Classify each numbered text chunk into one of these content types: {types_list}.\n"
                "Return a JSON object with a 'classifications' array of strings, one per chunk, in order."
            ),
            LLMUserMessage(content=numbered),
        ]

        schema = {
            "type": "object",
            "properties": {
                "classifications": {
                    "type": "array",
                    "items": {"type": "string", "enum": [t.value for t in ContentBlockType]},
                }
            },
            "required": ["classifications"],
        }

        try:
            result = await client.generate_structured(
                model=model,
                messages=messages,
                response_format=schema,
            )
            classifications = result.get("classifications", [])
            output = []
            for i, text in enumerate(texts):
                if i < len(classifications):
                    try:
                        output.append(ContentBlockType(classifications[i]))
                    except ValueError:
                        output.append(ContentBlockType.narrative)
                else:
                    output.append(ContentBlockType.narrative)
            return output
        except Exception:
            return [ContentBlockType.narrative] * len(texts)

    async def _generate_summary(
        self, markdown: str, blocks: List[ContentBlock]
    ) -> str:
        """Generate a brief summary of the content."""
        client = LLMClient()
        model = get_model()

        messages = [
            LLMSystemMessage(
                content="Summarize the following content in 1-2 sentences for use as a presentation overview."
            ),
            LLMUserMessage(content=markdown[:3000]),
        ]

        try:
            result = await client.generate(model=model, messages=messages)
            return result.strip()[:500]
        except Exception:
            # Fallback: first 200 chars
            return markdown[:200].strip() + "..."