"""Groq cloud provider — OpenAI-compatible API with vision."""

from __future__ import annotations

import logging
import os

import requests

from .base import ModelInfo, ProviderResponse

logger = logging.getLogger(__name__)

# Groq-specific env vars
GROQ_API_KEY = os.environ.get("GROQ_API_KEY", "")
GROQ_BASE_URL = os.environ.get("GROQ_BASE_URL", "https://api.groq.com/openai/v1")
GROQ_MODEL = os.environ.get("GROQ_MODEL", "meta-llama/llama-4-scout-17b-16e-instruct")

MODELS = {
    "meta-llama/llama-4-scout-17b-16e-instruct": ModelInfo(
        id="meta-llama/llama-4-scout-17b-16e-instruct",
        vision=True,
        cost_per_input_token=0.0,
        cost_per_output_token=0.0,
        notes="Llama 4 Scout, only vision model on Groq free tier",
    ),
}


class GroqProvider:
    name = "groq"
    models = MODELS

    def __init__(self):
        self.api_key = GROQ_API_KEY
        self.base_url = GROQ_BASE_URL
        self.model = GROQ_MODEL
        self.endpoint = f"{self.base_url.rstrip('/')}/chat/completions"
        self.headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json",
        }

    def call(self, image_b64: str, prompt: str) -> ProviderResponse:
        payload = {
            "model": self.model,
            "messages": [{
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt},
                    {"type": "image_url", "image_url": {
                        "url": f"data:image/jpeg;base64,{image_b64}",
                    }},
                ],
            }],
            "max_tokens": 150,
        }

        resp = requests.post(self.endpoint, headers=self.headers, json=payload, timeout=30)
        resp.raise_for_status()
        data = resp.json()

        answer = data["choices"][0]["message"]["content"].strip()
        total_tokens = data.get("usage", {}).get("total_tokens", 0)

        return ProviderResponse(answer=answer, total_tokens=total_tokens)