import torch
import requests
from .config import TAG_NAMES, SPACE_URL
from .globals import global_model, global_tokenizer

def predict_single(text, hf_repo, backend="local", hf_token=None):
    if backend == "local":
        return _predict_local(text, hf_repo)
    elif backend == "hf":
        return _predict_hf_api(text, hf_token)
    else:
        raise ValueError(f"Unknown backend: {backend}")

def _predict_local(text, hf_repo):
    global global_model, global_tokenizer
    
    # Lazy-loading to avoid slow startup
    if global_model is None:
        from .model import QwenClassifier
        from transformers import AutoTokenizer
        
        global_model = QwenClassifier.from_pretrained(hf_repo).eval()
        global_tokenizer = AutoTokenizer.from_pretrained(hf_repo)
    
    inputs = global_tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        logits = global_model(**inputs)
    return _process_output(logits)

def _predict_hf_api(text, hf_token=None):
    try:
        response = requests.post(
            f"{SPACE_URL}/predict",
            json={"text": text},  # This matches the Pydantic model
            headers={
                "Authorization": f"Bearer {hf_token}",
                "Content-Type": "application/json"
            } if hf_token else {"Content-Type": "application/json"},
            timeout=60
        )
        response.raise_for_status()  # Raise HTTP errors
        return response.json()
    except requests.exceptions.RequestException as e:
        raise ValueError(f"API Error: {str(e)}\nResponse: {e.response.text if hasattr(e, 'response') else ''}")

def _process_output(logits):
    probs = torch.sigmoid(logits)
    s = ''
    for tag, prob in zip(TAG_NAMES, probs[0]):
        if prob>0.5:
            s += f"{tag}({prob:.2f}), "
    return s[:-2]