# SPDX-License-Identifier: GPL-3.0-only
"""Summarisation providers.

- OpenAICompatibleSummariser: any endpoint speaking the OpenAI /chat/completions API -
  OpenAI, Groq, Together, OpenRouter, LocalAI, vLLM, LM Studio, and Ollama's /v1 endpoint.
- AnthropicSummariser: Claude via the official Anthropic SDK (Messages API). Anthropic does
  not expose an OpenAI-compatible endpoint, so it needs its own provider.
"""
from __future__ import annotations

import httpx

from ..models import Summary
from .base import SUMMARY_SYSTEM, parse_summary, summary_user_prompt


class OpenAICompatibleSummariser:
    """Talks the OpenAI Chat Completions API. Works with any compatible base_url."""

    def __init__(self, base_url: str, api_key: str, model: str):
        self.base_url = base_url.rstrip("/")
        self.api_key = api_key
        self.model = model
        self.name = f"openai_compatible:{model}"

    def summarise(self, recording_id: str, transcript_text: str) -> Summary:
        headers = {"Authorization": f"Bearer {self.api_key}"} if self.api_key else {}
        payload = {
            "model": self.model,
            "messages": [
                {"role": "system", "content": SUMMARY_SYSTEM},
                {"role": "user", "content": summary_user_prompt(transcript_text)},
            ],
            "temperature": 0.2,
            # Honoured by OpenAI/Groq/vLLM/etc.; ignored by servers that don't support it.
            "response_format": {"type": "json_object"},
        }
        resp = httpx.post(
            f"{self.base_url}/chat/completions", headers=headers, json=payload, timeout=120
        )
        resp.raise_for_status()
        content = resp.json()["choices"][0]["message"]["content"]
        return parse_summary(recording_id, self.name, content)


class AnthropicSummariser:
    """Claude via the official Anthropic SDK. Default model: claude-opus-4-8."""

    def __init__(self, api_key: str, model: str):
        import anthropic  # imported lazily so the dep is only needed for this provider

        self._client = (
            anthropic.Anthropic(api_key=api_key) if api_key else anthropic.Anthropic()
        )
        self.model = model
        self.name = f"anthropic:{model}"

    def summarise(self, recording_id: str, transcript_text: str) -> Summary:
        resp = self._client.messages.create(
            model=self.model,
            max_tokens=2000,
            system=SUMMARY_SYSTEM,
            messages=[{"role": "user", "content": summary_user_prompt(transcript_text)}],
        )
        text = next((b.text for b in resp.content if b.type == "text"), "")
        return parse_summary(recording_id, self.name, text)