import json
import re
import anthropic
from ai.base import AIProvider, ClassificationResult

MAX_AI_CHARS = 8_000


class AnthropicProvider(AIProvider):
    def __init__(self, api_key: str, model: str = "claude-sonnet-4-6"):
        self._api_key = api_key
        self._model = model

    def _client(self):
        return anthropic.AsyncAnthropic(api_key=self._api_key)

    async def classify(
        self,
        document_text: str,
        existing_topics: list[str],
        system_prompt: str,
    ) -> ClassificationResult:
        topics_str = ", ".join(existing_topics) if existing_topics else "(none yet)"
        user_msg = (
            f"Existing topics: [{topics_str}]\n\n"
            f"Document text:\n{document_text[:MAX_AI_CHARS]}"
        )
        client = self._client()
        response = await client.messages.create(
            model=self._model,
            max_tokens=1024,
            system=system_prompt,
            messages=[{"role": "user", "content": user_msg}],
        )
        raw = response.content[0].text
        return _parse_classification(raw)

    async def suggest_topics(
        self,
        document_text: str,
        system_prompt: str,
    ) -> list[str]:
        user_msg = (
            "Suggest 3-5 topic names for this document. "
            "Return ONLY valid JSON: {\"suggested_topics\": [\"topic1\", \"topic2\"]}\n\n"
            f"Document text:\n{document_text[:MAX_AI_CHARS]}"
        )
        client = self._client()
        response = await client.messages.create(
            model=self._model,
            max_tokens=256,
            system=system_prompt,
            messages=[{"role": "user", "content": user_msg}],
        )
        raw = response.content[0].text
        return _parse_suggestions(raw)

    async def health_check(self) -> bool:
        try:
            client = self._client()
            await client.messages.create(
                model=self._model,
                max_tokens=5,
                messages=[{"role": "user", "content": "ping"}],
            )
            return True
        except Exception:
            return False


def _strip_code_fences(text: str) -> str:
    text = re.sub(r"```(?:json)?\s*", "", text)
    text = re.sub(r"```", "", text)
    return text.strip()


def _parse_classification(raw: str) -> ClassificationResult:
    raw = _strip_code_fences(raw)
    # Try to find JSON object
    match = re.search(r"\{.*\}", raw, re.DOTALL)
    if match:
        try:
            data = json.loads(match.group())
            return ClassificationResult(
                topics=data.get("assigned_topics", []),
                suggested_new_topics=data.get("new_topic_suggestions", []),
                reasoning=data.get("reasoning", ""),
            )
        except json.JSONDecodeError:
            pass
    return ClassificationResult()


def _parse_suggestions(raw: str) -> list[str]:
    raw = _strip_code_fences(raw)
    match = re.search(r"\{.*\}", raw, re.DOTALL)
    if match:
        try:
            data = json.loads(match.group())
            return data.get("suggested_topics", [])
        except json.JSONDecodeError:
            pass
    return []