import asyncio
import httpx
from typing import List, Dict, Any

BATCH_SIZE = 1000
TIMEOUT_S = 600
MAX_RETRIES = 3
RETRY_DELAY = 1


def chunks(xs: List[str], n: int):
    for i in range(0, len(xs), n):
        yield xs[i:i+n]


async def fetch_metadata(client: httpx.AsyncClient, base_url: str) -> Dict[str, Any]:
    for attempt in range(MAX_RETRIES):
        r = await client.get(f"{base_url}/metadata", timeout=30)
        r.raise_for_status()
        return r.json()


async def call_predict(client: httpx.AsyncClient, base_url: str, smiles_batch: List[str]) -> Dict[str, Any]:
    for attempt in range(MAX_RETRIES):
        r = await client.post(
            f"{base_url}/predict",
            json={"smiles": smiles_batch},
            timeout=TIMEOUT_S,
        )
        r.raise_for_status()
        return r.json()


async def evaluate_model(hf_space_tag: str, smiles_list: List[str]) -> Dict[str, Any]:
    # Convert username/space-name to username-space-name.hf.space
    base_url = f"https://{hf_space_tag.replace('/', '-').replace('_', '-').lower()}.hf.space"
    results = []
    
    async with httpx.AsyncClient() as client:
        meta = await fetch_metadata(client, base_url)
        max_bs = min(meta.get("max_batch_size", BATCH_SIZE), BATCH_SIZE)

        for batch in chunks(smiles_list, max_bs):
            resp = await call_predict(client, base_url, batch)
            predictions_dict = resp["predictions"]
            for smiles in batch:
                if smiles in predictions_dict:
                    results.append({"smiles": smiles, "raw_predictions": predictions_dict[smiles]})
                else:
                    results.append({"smiles": smiles, "raw_predictions": {}, "error": "No prediction found"})
    
    return {"results": results, "metadata": meta}