#!/usr/bin/env python3

"""
secretchat-api-example.py

• Prints model IDs at start
• Shows which model handles each sentence, 5 sentences

Requires:
  pip install requests python-dotenv
"""

import os
import random
import time
from typing import List, Union, Tuple, Optional

import requests
from dotenv import load_dotenv


# ── API-key helper ───────────────────────────────────────────────────────────
def load_api_key() -> str:
    load_dotenv(os.path.join(os.path.expanduser("~"), ".secretlab.env"))
    key = os.getenv("SECRETLAB_API_KEY")
    if not key:
        raise ValueError("SECRETLAB_API_KEY not found")
    return key


# ── /api/models helper ───────────────────────────────────────────────────────
def fetch_model_ids(api_key: str, base_url: str) -> List[str]:
    resp = requests.get(
        f"{base_url}/api/models",
        headers={"Authorization": f"Bearer {api_key}"},
        timeout=300,
    )
    resp.raise_for_status()
    raw: Union[list, dict] = resp.json()

    if isinstance(raw, dict):  # unwrap {"models":[...]} or similar
        for key in ("models", "data", "items", "result"):
            if key in raw and isinstance(raw[key], list):
                raw = raw[key]
                break

    if not isinstance(raw, list):
        raise RuntimeError("Unexpected /api/models response")

    ids: List[str] = []
    for item in raw:
        if isinstance(item, str):
            ids.append(item)
        elif isinstance(item, dict):
            if isinstance(item.get("model"), str):
                ids.append(item["model"])
            elif isinstance(item.get("ollama"), dict) and isinstance(
                item["ollama"].get("model"), str
            ):
                ids.append(item["ollama"]["model"])
    if not ids:
        raise RuntimeError("No model IDs found")
    return ids


# ── classify helper ──────────────────────────────────────────────────────────
def classify(
    sentence: str,
    api_key: str,
    base_url: str,
    models: List[str],
    retries: int = 6,
    backoff: int = 2,
) -> Tuple[str, Optional[str]]:
    """Return (model_used, classification or None)."""
    url = f"{base_url}/api/chat/completions"

    for attempt in range(1, retries + 1):
        model = random.choice(models)
        payload = {
            "model": model,
            "messages": [
                {
                    "role": "system",
                    "content": (
                        "Respond ONLY with 'Positive' or 'Negative'. "
                        "No other words or punctuation."
                    ),
                },
                {"role": "user", "content": f"Classify this:\n{sentence}"},
            ],
            "temperature": 0.0,
        }

        try:
            r = requests.post(
                url,
                headers={
                    "Authorization": f"Bearer {api_key}",
                    "Content-Type": "application/json",
                },
                json=payload,
                timeout=300,
            )
            if r.status_code == 200:
                result = r.json()["choices"][0]["message"]["content"].strip()
                return model, result
            print(f"API error {r.status_code}: {r.text}")
            return model, None
        except requests.exceptions.RequestException as e:
            print(f"Network error ({attempt}/{retries}): {e}")
            if attempt < retries:
                time.sleep(backoff)
                backoff *= 2
            else:
                return model, None


# ── main loop ────────────────────────────────────────────────────────────────
def main() -> None:
    api_key = load_api_key()
    BASE = "https://chat.secretlab.page"
    models = fetch_model_ids(api_key, BASE)

    print("Models:")
    for m in models:
        print(m)
    print("-" * 40, "\n")

    sentences = [
        "I love sunny days!",
        "This is a bland sandwich.",
        "I really hate waiting in long lines.",
        "The meeting was okay, nothing special.",
        "I am so excited about the upcoming holidays!",
    ]

    for run in range(1):  # repeat 1 times
        print(f"\n--- Run {run+1} ---\n")

        for idx, s in enumerate(sentences, 1):
            model_used, res = classify(s, api_key, BASE, models)

            print(f"Sentence {idx}: {s}")
            print(f"Model      : {model_used}")
            print(f"Result     : {res or 'Failed'}\n")

        time.sleep(1)  # optional pause between runs

    print("\nAll runs complete!\n")


if __name__ == "__main__":
    main()