"""The answering agent: intent routing -> retrieval -> grounded, cited generation.

Pipeline per user message:
1. Classify intent (greeting / logistics / research-or-person) with cheap heuristics.
2. Retrieve passages (+ graph expansion) from the knowledge base.
3. Grounding guard: if nothing in the corpus is relevant, decline rather than
   hallucinate.
4. Otherwise build a numbered, source-attributed context and stream an answer
   that cites with [n]; a clickable Sources list is always appended by us so the
   links are correct even if the model forgets to cite.
"""

from __future__ import annotations

import re
from typing import Iterator, List, Tuple

import config
import llm
from retriever import retrieve, RetrievalResult

# --------------------------------------------------------------------------- #
# Intent classification (cheap, no LLM call)
# --------------------------------------------------------------------------- #
_GREETING_RE = re.compile(r"^\s*(hi|hey|hello|yo|sup|good (morning|afternoon|evening)|thanks|thank you)\b", re.IGNORECASE)
_LOGISTICS_RE = re.compile(
    r"\b(phd|ph\.d|student|apply|application|admission|join|position|opening|"
    r"intern|internship|recruit|advisor|advise|supervis|collaborat|partner|"
    r"contact|email|reach|hire|hiring|work with|opportunit)\w*",
    re.IGNORECASE,
)


def classify_intent(message: str) -> str:
    text = (message or "").strip()
    if _GREETING_RE.search(text) and len(text) < 40:
        return "greeting"
    if _LOGISTICS_RE.search(text):
        return "logistics"
    return "research"


def wants_to_connect(message: str) -> bool:
    """Heuristic: does this message look like a prospective student/collaborator?"""
    return classify_intent(message) == "logistics"


def _content_to_text(content) -> str:
    """Coerce a Gradio message 'content' to plain text.

    Gradio 6 may hand history back with content as a string, a list of rich-text
    parts (e.g. [{'text': '...', 'type': 'text'}]), or a dict — but the LLM
    backends expect a plain string. Normalize all shapes here.
    """
    if content is None:
        return ""
    if isinstance(content, str):
        return content
    if isinstance(content, dict):
        return content.get("text", "")
    if isinstance(content, list):
        parts = []
        for p in content:
            if isinstance(p, str):
                parts.append(p)
            elif isinstance(p, dict) and p.get("text"):
                parts.append(p["text"])
        return " ".join(parts).strip()
    return str(content)


# --------------------------------------------------------------------------- #
# Prompt construction
# --------------------------------------------------------------------------- #
def _persona_system() -> str:
    base_rules = (
        "Rules:\n"
        "- Use ONLY the numbered context provided to state facts about the research.\n"
        "- Cite sources inline as [n] using the numbers in the context.\n"
        "- If the context does not contain the answer, say you don't have that "
        "information rather than guessing.\n"
        "- Be concise, professional, and engaging. Prefer 2-5 short paragraphs or "
        "bullet points.\n"
        "- Do not invent publication titles, links, dates, or numbers."
    )
    if config.PERSONA == "first_person":
        return (
            f"You are an AI assistant speaking as {config.PROFESSOR_NAME} "
            f"({config.PROFESSOR_TITLE}) on the lab website. Stay in character in "
            f"the first person, but you are an automated assistant, not the real "
            f"person. {base_rules}"
        )
    return (
        f"You are the AI research assistant for {config.LAB_NAME}. You help "
        f"prospective students, collaborators, and the public understand the work of "
        f"{config.PROFESSOR_NAME} ({config.PROFESSOR_TITLE}). Refer to him in the "
        f"third person as 'Prof. Zhao'. {base_rules}"
    )


def _build_context(result: RetrievalResult) -> Tuple[str, List[dict]]:
    """Turn hits into numbered context text + a deduped source list for rendering."""
    sources: List[dict] = []
    doc_to_num: dict = {}
    blocks: List[str] = []
    for hit in result.hits:
        doc = hit.chunk["doc_id"]
        if doc not in doc_to_num:
            doc_to_num[doc] = len(sources) + 1
            sources.append(
                {
                    "n": doc_to_num[doc],
                    "title": hit.chunk["title"],
                    "url": hit.chunk["url"],
                    "source": hit.chunk["source"],
                }
            )
        n = doc_to_num[doc]
        tag = " (related work, via paper graph)" if hit.via_graph else ""
        blocks.append(f"[{n}]{tag} {hit.chunk['title']}\n{hit.chunk['text']}")
    return "\n\n".join(blocks), sources


def _render_sources(sources: List[dict]) -> str:
    if not sources:
        return ""
    lines = ["\n\n---", "**Sources**"]
    for s in sources:
        # only publications get external links worth surfacing distinctly
        lines.append(f"{s['n']}. [{s['title']}]({s['url']})")
    return "\n".join(lines)


_GROUNDING_GUARD = (
    "I don't have that in my knowledge base, so I'd rather not guess. "
    "I can speak to Prof. Zhao's research on **graph neural networks**, "
    "**spatio-temporal / geospatial ML**, **retrieval-augmented and agentic LLMs**, "
    "**AI for science**, and **trustworthy & efficient AI**. Try asking about one of "
    "those — for example, *“What is Spatial-RAG?”* or *“Summarize the lab's work on "
    "graph RAG.”*\n\nIf you'd like to reach the lab directly, tell me you're a "
    "prospective student or collaborator and I can take your details."
)


# --------------------------------------------------------------------------- #
# Public: streaming answer
# --------------------------------------------------------------------------- #
def stream_answer(message: str, history: List[dict] | None = None) -> Iterator[str]:
    """Yield the cumulative answer text (suitable for Gradio streaming)."""
    history = history or []
    intent = classify_intent(message)
    result = retrieve(message)

    # Grounding guard: refuse to fabricate when the corpus is irrelevant.
    if not result.is_grounded and intent != "greeting":
        yield _GROUNDING_GUARD
        return

    context, sources = _build_context(result)
    tier = "deep" if intent == "research" else llm.route(message)

    system = _persona_system()
    convo = [
        {"role": m["role"], "content": _content_to_text(m.get("content"))}
        for m in history
        if m.get("role") in ("user", "assistant")
    ]
    convo = [m for m in convo if m["content"]][-6:]  # drop empties, keep last 6
    user_turn = (
        f"Numbered context:\n{context}\n\n"
        f"Question: {message}\n\n"
        f"Answer using only the context above and cite with [n]."
    )
    messages = [{"role": "system", "content": system}, *convo, {"role": "user", "content": user_turn}]

    body = ""
    for piece in llm.stream(messages, tier=tier):
        body += piece
        yield body

    # Always append correct, clickable sources.
    tail = _render_sources(sources)
    if tail:
        yield body + tail