hf_backend.py

# hf_backend.py (patched)
import time, logging, os
from typing import Any, Dict, AsyncIterable

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from backends_base import ChatBackend, ImagesBackend
from config import settings

try:
    import spaces
    from spaces.zero.client import SpaceZeroClient
except ImportError:
    spaces, SpaceZeroClient = None, None

logger = logging.getLogger(__name__)

MODEL_ID = settings.LlmHFModelID or "Qwen/Qwen2.5-1.5B-Instruct"
logger.info(f"Loading {MODEL_ID} on CPU at startup (ZeroGPU safe)...")

tokenizer, model, load_error = None, None, None
try:
    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True, use_fast=False)
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_ID,
        torch_dtype=torch.float32,
        trust_remote_code=True,
    )
    model.eval()
except Exception as e:
    load_error = f"Failed to load model/tokenizer: {e}"
    logger.exception(load_error)


def pick_device() -> str:
    if torch.cuda.is_available():
        return "cuda"
    if getattr(torch.backends, "mps", None) and torch.backends.mps.is_available():
        return "mps"
    return "cpu"

def pick_dtype(device: str) -> torch.dtype:
    if device == "cuda":
        major, _ = torch.cuda.get_device_capability()
        return torch.bfloat16 if major >= 8 else torch.float16
    if device == "mps":
        return torch.float16
    return torch.float32


class HFChatBackend(ChatBackend):
    async def stream(self, request: Dict[str, Any]) -> AsyncIterable[Dict[str, Any]]:
        if load_error:
            raise RuntimeError(load_error)

        messages = request.get("messages", [])
        prompt = messages[-1]["content"] if messages else "(empty)"
        temperature = float(request.get("temperature", settings.LlmTemp or 0.7))
        max_tokens = int(request.get("max_tokens", settings.LlmOpenAICtxSize or 512))

        rid = f"chatcmpl-hf-{int(time.time())}"
        now = int(time.time())

        # --- ✅ Extract X-IP-Token from RabbitMQ message
        x_ip_token = request.get("x_ip_token")
        headers = {}
        if x_ip_token:
            headers["X-IP-Token"] = x_ip_token
            logger.info("Using X-IP-Token from request for ZeroGPU attribution")

        def _gpu_inference_fn(prompt: str) -> str:
            device = pick_device()
            dtype = pick_dtype(device)
            model.to(device=device, dtype=dtype).eval()

            inputs = tokenizer(prompt, return_tensors="pt").to(device)
            with torch.inference_mode(), torch.autocast(device_type=device, dtype=dtype):
                outputs = model.generate(
                    **inputs,
                    max_new_tokens=max_tokens,
                    temperature=temperature,
                    do_sample=True,
                )
            return tokenizer.decode(outputs[0], skip_special_tokens=True)

        if spaces and SpaceZeroClient:
            # Use a custom SpaceZeroClient with headers
            client = SpaceZeroClient(headers=headers or None)
            try:
                text = await client.run(_gpu_inference_fn, args=[prompt], duration=120)
            except Exception:
                logger.exception("HF inference (ZeroGPU) failed")
                raise
        else:
            # CPU fallback
            inputs = tokenizer(prompt, return_tensors="pt")
            with torch.inference_mode():
                outputs = model.generate(
                    **inputs,
                    max_new_tokens=max_tokens,
                    temperature=temperature,
                    do_sample=True,
                )
            text = tokenizer.decode(outputs[0], skip_special_tokens=True)

        yield {
            "id": rid,
            "object": "chat.completion.chunk",
            "created": now,
            "model": MODEL_ID,
            "choices": [
                {"index": 0, "delta": {"content": text}, "finish_reason": "stop"}
            ],
        }