from __future__ import annotations

import json
import os
import sqlite3
import urllib.error
import urllib.request
from collections.abc import Callable
from dataclasses import dataclass

from .taxonomy import (
    ALLOWED_TAGS,
    FLAVORS,
    MAX_TAGS,
    TOPICS,
    coerce_flavor,
    coerce_tags,
    coerce_topic,
    flavors_prompt_block,
    tags_prompt_block,
    topics_prompt_block,
)


DEFAULT_BASE_URL = "http://127.0.0.1:1234/v1"
DEFAULT_MODEL = "gpt-oss"
DEFAULT_EMBED_MODEL = "text-embedding-nomic-embed-text-v1.5"
DEFAULT_TIMEOUT = 180


# Structured-output schema. Newer LM Studio / OpenAI-compatible servers want a
# json_schema response_format (older ones took json_object); we try schema first
# and fall back gracefully so the client works across server versions.
_SCORE_FIELD = {"type": "integer", "minimum": 0, "maximum": 10}
CLASSIFICATION_SCHEMA = {
    "type": "object",
    "additionalProperties": False,
    "required": [
        "constructive_score",
        "cortisol_score",
        "ragebait_score",
        "agency_score",
        "human_benefit_score",
        "novelty_score",
        "pr_risk_score",
        "accepted",
        "topic",
        "flavor",
        "tags",
        "reason_code",
        "reason_text",
    ],
    "properties": {
        "constructive_score": _SCORE_FIELD,
        "cortisol_score": _SCORE_FIELD,
        "ragebait_score": _SCORE_FIELD,
        "agency_score": _SCORE_FIELD,
        "human_benefit_score": _SCORE_FIELD,
        "novelty_score": _SCORE_FIELD,
        "pr_risk_score": _SCORE_FIELD,
        "accepted": {"type": "boolean"},
        "topic": {"type": "string", "enum": list(TOPICS)},
        "flavor": {"type": "string", "enum": list(FLAVORS)},
        "tags": {"type": "array", "items": {"type": "string", "enum": list(ALLOWED_TAGS)}, "maxItems": MAX_TAGS},
        "reason_code": {"type": "string"},
        "reason_text": {"type": "string"},
    },
}

# Response-format variants tried in order. Once one succeeds for a client, it is
# pinned so we stop paying failed round-trips on every subsequent call.
_RESPONSE_FORMATS = (
    {"type": "json_schema", "json_schema": {"name": "classification", "strict": True, "schema": CLASSIFICATION_SCHEMA}},
    {"type": "json_object"},
    None,
)


SYSTEM_PROMPT = """You classify article metadata for Upbeat Bytes, a calm news digest.

The bar is NOT "is this happy?" — it is "will a reader finish this calm or a little better, never worse?" ACCEPT stories that are calm, neutral, insightful, or uplifting: they inform, teach, delight, or show progress or benefit. Neutral-but-absorbing is welcome — a discovery, a clear explainer, a clever build or gadget, a fascinating bit of science, space, nature, design, or culture, a genuinely useful insight — even when it isn't "feel-good."

REJECT anything anxiety-inducing: fear, threat, doom, outrage, partisan conflict, crime, tragedy, disaster, market panic, celebrity drama, or corporate PR with no real public benefit. ESPECIALLY reject the comparison traps — anything that would make a reader feel inferior, behind, inadequate, envious, or pressured (status flexing, FOMO, hustle-grind, "you're falling behind"). When unsure, judge the emotional aftertaste, not the topic.

On AI specifically: this is NOT "no AI" — it is "no AI dread." ACCEPT AI stories about practical tools, accessibility, medical/scientific/educational benefit, creative or maker use, environmental or resource gains, open research, humane design, or a specific bounded innovation. REJECT AI stories whose main frame is loss of human control, cognitive decline or "brain rot," job-displacement panic, surveillance panic, existential doom, harm-to-children or social-fabric panic, "you're falling behind" productivity anxiety, or adversarial arms-race framing.

Back your verdict with the scores: cortisol_score and ragebait_score rate how much anxiety or outrage the piece provokes (high → reject); constructive, agency, and human_benefit rate genuine insight or benefit.

Also assign one primary topic and one flavor (the single best fit), plus 1-4 grouping tags.

Primary topic (what the story is mainly about):
{topics}

Flavor (why it belongs in a calm, uplifting digest):
{flavors}

Grouping tags — choose ONLY from this controlled vocabulary:
{tags}

Tag discipline: assign 1-4 tags; prefer fewer, stronger ones; never tag by weak
association; pick tags a reader would reasonably use to find this story later.

Return only JSON with this exact shape:
{{
  "constructive_score": 0,
  "cortisol_score": 0,
  "ragebait_score": 0,
  "agency_score": 0,
  "human_benefit_score": 0,
  "novelty_score": 0,
  "pr_risk_score": 0,
  "accepted": false,
  "topic": "one_of_the_allowed_topics",
  "flavor": "one_of_the_allowed_flavors",
  "tags": ["one_to_four_allowed_tags"],
  "reason_code": "short_snake_case",
  "reason_text": "one concise sentence"
}}
""".format(topics=topics_prompt_block(), flavors=flavors_prompt_block(), tags=tags_prompt_block())


@dataclass
class LocalModelClient:
    base_url: str
    model: str
    api_key: str | None = None
    timeout: int = DEFAULT_TIMEOUT
    embed_model: str = DEFAULT_EMBED_MODEL
    # Index into _RESPONSE_FORMATS that the server accepts; discovered lazily.
    _response_format_idx: int | None = None

    @classmethod
    def from_env(cls) -> "LocalModelClient":
        return cls(
            base_url=os.environ.get("GOODNEWS_LLM_BASE_URL", DEFAULT_BASE_URL).rstrip("/"),
            model=os.environ.get("GOODNEWS_LLM_MODEL", DEFAULT_MODEL),
            api_key=os.environ.get("GOODNEWS_LLM_API_KEY"),
            timeout=int(os.environ.get("GOODNEWS_LLM_TIMEOUT", DEFAULT_TIMEOUT)),
            embed_model=os.environ.get("GOODNEWS_EMBED_MODEL", DEFAULT_EMBED_MODEL),
        )

    def embed(self, texts: list[str]) -> list[list[float]]:
        """Return embedding vectors for a batch of texts via /embeddings."""
        body = json.dumps({"model": self.embed_model, "input": texts}).encode("utf-8")
        headers = {"Content-Type": "application/json"}
        if self.api_key:
            headers["Authorization"] = f"Bearer {self.api_key}"
        request = urllib.request.Request(
            f"{self.base_url}/embeddings", data=body, headers=headers, method="POST"
        )
        try:
            with urllib.request.urlopen(request, timeout=self.timeout) as response:
                data = json.loads(response.read().decode("utf-8"))
        except urllib.error.HTTPError as exc:
            detail = exc.read().decode("utf-8", errors="replace")
            raise RuntimeError(f"HTTP {exc.code} from embeddings: {detail}") from exc
        except urllib.error.URLError as exc:
            raise RuntimeError(f"could not reach embeddings at {self.base_url}: {exc.reason}") from exc
        try:
            return [item["embedding"] for item in data["data"]]
        except (KeyError, TypeError) as exc:
            raise RuntimeError(f"unexpected embeddings response: {data}") from exc

    def classify(self, article: sqlite3.Row) -> dict:
        messages = [
            {"role": "system", "content": SYSTEM_PROMPT},
            {"role": "user", "content": _article_prompt(article)},
        ]
        # If we already learned which response_format the server accepts, use it.
        if self._response_format_idx is not None:
            return self._chat(self._build_payload(messages, _RESPONSE_FORMATS[self._response_format_idx]))

        # Otherwise escalate through the variants, pinning the first that works.
        last_exc: RuntimeError | None = None
        for idx, fmt in enumerate(_RESPONSE_FORMATS):
            try:
                result = self._chat(self._build_payload(messages, fmt))
                self._response_format_idx = idx
                return result
            except RuntimeError as exc:
                if "HTTP 400" not in str(exc):
                    raise
                last_exc = exc
        raise last_exc if last_exc else RuntimeError("no usable response_format")

    def _build_payload(self, messages: list[dict], response_format: dict | None) -> dict:
        payload = {"model": self.model, "temperature": 0.1, "messages": messages}
        if response_format is not None:
            payload["response_format"] = response_format
        return payload

    def list_models(self) -> list[str]:
        headers = {}
        if self.api_key:
            headers["Authorization"] = f"Bearer {self.api_key}"
        request = urllib.request.Request(f"{self.base_url}/models", headers=headers)
        try:
            with urllib.request.urlopen(request, timeout=10) as response:
                data = json.loads(response.read().decode("utf-8"))
        except urllib.error.HTTPError as exc:
            detail = exc.read().decode("utf-8", errors="replace")
            raise RuntimeError(f"HTTP {exc.code} from local model: {detail}") from exc
        except urllib.error.URLError as exc:
            raise RuntimeError(f"could not reach local model at {self.base_url}: {exc.reason}") from exc

        models = data.get("data", [])
        names = []
        for model in models:
            if isinstance(model, dict) and model.get("id"):
                names.append(str(model["id"]))
        return names

    def chat_text(self, messages: list[dict]) -> str:
        """Plain chat completion → the raw message text (no JSON parsing).

        Used for free-form output like summaries; classification uses _chat,
        which JSON-parses the same content.
        """
        return self._raw_content(self._build_payload(messages, None))

    def _raw_content(self, payload: dict) -> str:
        body = json.dumps(payload).encode("utf-8")
        headers = {"Content-Type": "application/json"}
        if self.api_key:
            headers["Authorization"] = f"Bearer {self.api_key}"
        request = urllib.request.Request(
            f"{self.base_url}/chat/completions",
            data=body,
            headers=headers,
            method="POST",
        )
        try:
            with urllib.request.urlopen(request, timeout=self.timeout) as response:
                data = json.loads(response.read().decode("utf-8"))
        except urllib.error.HTTPError as exc:
            detail = exc.read().decode("utf-8", errors="replace")
            raise RuntimeError(f"HTTP {exc.code} from local model: {detail}") from exc
        except urllib.error.URLError as exc:
            raise RuntimeError(f"could not reach local model at {self.base_url}: {exc.reason}") from exc

        try:
            return data["choices"][0]["message"]["content"]
        except (KeyError, IndexError, TypeError) as exc:
            raise RuntimeError(f"unexpected local model response: {data}") from exc

    def _chat(self, payload: dict) -> dict:
        return parse_classifier_json(self._raw_content(payload))


@dataclass
class ClassifyReport:
    results: list[tuple[int, dict]]
    attempted: int
    succeeded: int
    skipped: int


def classify_articles(
    conn: sqlite3.Connection,
    client: LocalModelClient,
    limit: int,
    include_rejected: bool = False,
    dry_run: bool = False,
    only_unclassified: bool = False,
    progress: "Callable[[int, int, int], None] | None" = None,
) -> ClassifyReport:
    rows = _classification_candidates(
        conn, limit=limit, include_rejected=include_rejected, only_unclassified=only_unclassified
    )
    results = []
    skipped = 0
    for index, row in enumerate(rows, start=1):
        try:
            scores = client.classify(row)
        except RuntimeError as exc:
            # One slow/failed article (timeout, bad response) shouldn't sink the
            # whole batch or discard work already committed. Skip and continue.
            skipped += 1
            print(f"[{row['id']}] skipped: {exc}")
            continue
        scores = normalize_scores(scores, model_name=client.model)
        results.append((row["id"], scores))
        if not dry_run:
            upsert_article_score(conn, row["id"], scores)
            conn.commit()
        if progress is not None:
            progress(index, len(rows), row["id"])
    return ClassifyReport(results=results, attempted=len(rows), succeeded=len(results), skipped=skipped)


def parse_classifier_json(content: str) -> dict:
    content = content.strip()
    try:
        return json.loads(content)
    except json.JSONDecodeError:
        start = content.find("{")
        end = content.rfind("}")
        if start == -1 or end == -1 or end <= start:
            raise RuntimeError(f"model did not return JSON: {content}")
        return json.loads(content[start : end + 1])


def normalize_scores(data: dict, model_name: str) -> dict:
    return {
        "constructive_score": _bounded_int(data.get("constructive_score")),
        "cortisol_score": _bounded_int(data.get("cortisol_score")),
        "ragebait_score": _bounded_int(data.get("ragebait_score")),
        "agency_score": _bounded_int(data.get("agency_score")),
        "human_benefit_score": _bounded_int(data.get("human_benefit_score")),
        "novelty_score": _bounded_int(data.get("novelty_score")),
        "pr_risk_score": _bounded_int(data.get("pr_risk_score")),
        "accepted": 1 if bool(data.get("accepted")) else 0,
        "topic": coerce_topic(data.get("topic")),
        "flavor": coerce_flavor(data.get("flavor")),
        "tags": coerce_tags(data.get("tags")),
        "reason_code": str(data.get("reason_code") or "model_no_reason")[:120],
        "reason_text": str(data.get("reason_text") or "")[:1000],
        "model_name": model_name,
    }


def upsert_article_score(conn: sqlite3.Connection, article_id: int, scores: dict) -> None:
    conn.execute(
        """
        INSERT INTO article_scores (
            article_id, constructive_score, cortisol_score, ragebait_score,
            agency_score, human_benefit_score, novelty_score, pr_risk_score,
            accepted, topic, flavor, reason_code, reason_text, model_name, scored_at
        )
        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP)
        ON CONFLICT(article_id) DO UPDATE SET
            constructive_score = excluded.constructive_score,
            cortisol_score = excluded.cortisol_score,
            ragebait_score = excluded.ragebait_score,
            agency_score = excluded.agency_score,
            human_benefit_score = excluded.human_benefit_score,
            novelty_score = excluded.novelty_score,
            pr_risk_score = excluded.pr_risk_score,
            accepted = excluded.accepted,
            topic = excluded.topic,
            flavor = excluded.flavor,
            reason_code = excluded.reason_code,
            reason_text = excluded.reason_text,
            model_name = excluded.model_name,
            scored_at = CURRENT_TIMESTAMP
        """,
        (
            article_id,
            scores["constructive_score"],
            scores["cortisol_score"],
            scores["ragebait_score"],
            scores["agency_score"],
            scores["human_benefit_score"],
            scores["novelty_score"],
            scores["pr_risk_score"],
            scores["accepted"],
            scores["topic"],
            scores["flavor"],
            scores["reason_code"],
            scores["reason_text"],
            scores["model_name"],
        ),
    )
    # Replace this article's grouping tags (controlled vocabulary, 0-4).
    conn.execute("DELETE FROM article_tags WHERE article_id = ?", (article_id,))
    for tag in scores.get("tags") or []:
        conn.execute(
            "INSERT OR IGNORE INTO article_tags (article_id, tag) VALUES (?, ?)", (article_id, tag)
        )


def _classification_candidates(
    conn: sqlite3.Connection,
    limit: int,
    include_rejected: bool,
    only_unclassified: bool = False,
) -> list[sqlite3.Row]:
    filters = []
    if not include_rejected:
        filters.append("(s.accepted = 1 OR s.constructive_score >= 4)")
    if only_unclassified:
        # Articles still carrying the fast heuristic score, i.e. not yet judged
        # by the model. Lets a scheduled cycle only spend the LLM on new items.
        filters.append("s.model_name LIKE 'heuristic-%'")
    where = ("WHERE " + " AND ".join(filters)) if filters else ""
    return conn.execute(
        f"""
        SELECT
            a.id,
            a.title,
            a.description,
            a.published_at,
            a.canonical_url,
            src.name AS source_name,
            src.default_category,
            src.trust_score AS source_trust_score,
            src.pr_risk_score AS source_pr_risk_score,
            s.constructive_score,
            s.cortisol_score,
            s.ragebait_score,
            s.agency_score,
            s.human_benefit_score,
            s.pr_risk_score,
            s.accepted,
            s.reason_code
        FROM articles a
        JOIN sources src ON src.id = a.source_id
        LEFT JOIN article_scores s ON s.article_id = a.id
        {where}
        ORDER BY
            CASE WHEN s.model_name LIKE 'heuristic-%' THEN 0 ELSE 1 END,
            COALESCE(a.published_at, a.discovered_at) DESC
        LIMIT ?
        """,
        (limit,),
    ).fetchall()


def _article_prompt(article: sqlite3.Row) -> str:
    return "\n".join(
        [
            f"Source: {article['source_name']}",
            f"Source category: {article['default_category'] or 'unknown'}",
            f"Source trust score: {article['source_trust_score']}/10",
            f"Source PR risk score: {article['source_pr_risk_score']}/10",
            f"Published: {article['published_at'] or 'unknown'}",
            f"Title: {article['title']}",
            f"Snippet: {article['description'] or ''}",
            f"URL: {article['canonical_url']}",
        ]
    )


def _bounded_int(value: object) -> int:
    try:
        parsed = int(value)
    except (TypeError, ValueError):
        parsed = 0
    return max(0, min(10, parsed))