import os
import json
import time
from typing import Optional, Dict, Any

try:
    import openai
except Exception:
    openai = None


OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
DEFAULT_MODEL = os.environ.get('CARDS_LLM_MODEL', 'gpt-4o-mini')


PROMPT_TEMPLATE = (
    """
You are a JSON generator. Given a dinosaur name, return EXACTLY one JSON object matching the schema below.
Only return JSON (no explanatory text).

Schema:
{
  "name": string,
  "speed_kmh": number|null,    // numeric speed in km/h
  "weight_kg": number|null,    // numeric weight in kg
  "height_m": number|null,     // numeric height in meters
  "intelligence_score": integer|null, // 1-5 scale or null
  "facts": [string,...],
  "sources": [{"title":string,"url":string},...]
}

Rules:
- Use units: km/h, kg, m.
- If you cannot find a reliable number, use null.
- Provide up to 5 short facts.
- Include at least one source object with a URL when available.
Name: {name}
"""
)


def _ensure_openai():
    if openai is None:
        raise RuntimeError('openai package not installed; add openai to requirements')
    if not OPENAI_API_KEY:
        raise RuntimeError('OPENAI_API_KEY not set in environment')
    openai.api_key = OPENAI_API_KEY


def _extract_json(text: str) -> Optional[Dict[str, Any]]:
    text = text.strip()
    # try direct parse
    try:
        return json.loads(text)
    except Exception:
        pass
    # heuristic: find first { ... } block
    start = text.find('{')
    end = text.rfind('}')
    if start != -1 and end != -1 and end > start:
        try:
            return json.loads(text[start:end+1])
        except Exception:
            return None
    return None


def fetch_dinosaur_structured(name: str, model: Optional[str] = None, max_retries: int = 2) -> Dict[str, Any]:
    """Call the configured LLM and return parsed JSON per schema.

    Raises RuntimeError on misconfiguration or ValueError if parsing fails.
    """
    model = model or DEFAULT_MODEL
    if openai is None:
        raise RuntimeError('openai package not available')
    _ensure_openai()

    prompt = PROMPT_TEMPLATE.format(name=name)
    for attempt in range(max_retries + 1):
        resp = openai.ChatCompletion.create(
            model=model,
            messages=[{"role": "user", "content": prompt}],
            temperature=0.0,
            max_tokens=600,
        )
        content = resp['choices'][0]['message']['content'].strip()
        data = _extract_json(content)
        if data:
            return data
        time.sleep(1 + attempt)
    raise ValueError('Failed to parse LLM response as JSON')