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from __future__ import annotations
import hmac
import json
import os
import threading
from contextlib import asynccontextmanager
from pathlib import Path
from typing import Annotated
import numpy as np
import torch
from fastapi import Depends, FastAPI, HTTPException
from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
from huggingface_hub import hf_hub_download
from pydantic import BaseModel, Field
from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer
DEFAULT_MODEL_ID = "NajahUniv/AraUni-MARBERTv2-Intent-Classifier"
MODEL_ID = os.getenv("MODEL_ID", DEFAULT_MODEL_ID)
MODEL_REVISION = os.getenv("MODEL_REVISION")
MODEL_DEVICE = os.getenv("MODEL_DEVICE", "auto")
MODEL_BACKEND = os.getenv("MODEL_BACKEND", "auto")
MODEL_PRECISION = os.getenv("MODEL_PRECISION", "auto")
MODEL_API_KEY = os.getenv("MODEL_API_KEY")
MAX_BATCH_SIZE = int(os.getenv("MAX_BATCH_SIZE", "64"))
def choose_device() -> str:
if MODEL_BACKEND == "onnx" and MODEL_DEVICE == "auto":
return "cpu"
if MODEL_DEVICE != "auto":
return MODEL_DEVICE
if torch.cuda.is_available():
return "cuda"
if torch.backends.mps.is_available():
return "mps"
return "cpu"
def resolve_runtime(device: str) -> tuple[str, str]:
if MODEL_BACKEND not in {"auto", "pytorch", "onnx"}:
raise ValueError("MODEL_BACKEND must be auto, pytorch, or onnx")
if MODEL_PRECISION not in {"auto", "fp32", "bf16", "fp16", "int8"}:
raise ValueError("MODEL_PRECISION must be auto, fp32, bf16, fp16, or int8")
backend = "pytorch" if MODEL_BACKEND == "auto" else MODEL_BACKEND
precision = MODEL_PRECISION
if precision == "auto":
if backend == "onnx":
precision = "int8"
elif device == "cuda":
precision = "bf16" if torch.cuda.is_bf16_supported() else "fp16"
else:
precision = "fp32"
if backend == "onnx" and (device != "cpu" or precision != "int8"):
raise ValueError("the published ONNX artifact supports CPU INT8 only")
if backend == "pytorch" and precision == "int8":
raise ValueError("MODEL_PRECISION=int8 requires MODEL_BACKEND=onnx")
if backend == "pytorch" and precision in {"bf16", "fp16"} and device != "cuda":
raise ValueError("this example enables bf16/fp16 only on CUDA")
return backend, precision
def hub_or_local_file(filename: str) -> str:
local = Path(MODEL_ID) / filename
if local.is_file():
return str(local)
return hf_hub_download(MODEL_ID, filename, revision=MODEL_REVISION)
class ClassifyRequest(BaseModel):
texts: list[str] = Field(min_length=1)
top_k: int = Field(default=5, ge=1)
threshold: float | None = Field(default=None, gt=0, lt=1)
class ModelRuntime:
def __init__(self) -> None:
load_kwargs = {"revision": MODEL_REVISION} if MODEL_REVISION else {}
self.tokenizer = AutoTokenizer.from_pretrained(
MODEL_ID,
trust_remote_code=True,
**load_kwargs,
)
self.config = AutoConfig.from_pretrained(
MODEL_ID,
trust_remote_code=True,
**load_kwargs,
)
self.device = choose_device()
self.backend, self.precision = resolve_runtime(self.device)
self.lock = threading.Lock()
self.thresholds = self.config.thresholds
self.model = None
self.session = None
if self.backend == "onnx":
import onnxruntime as ort
onnx_config = json.loads(
Path(hub_or_local_file("onnx/onnx_config.json")).read_text(encoding="utf-8")
)
self.thresholds = onnx_config["thresholds"]
self.session = ort.InferenceSession(
hub_or_local_file("onnx/model_int8.onnx"),
providers=["CPUExecutionProvider"],
)
else:
dtype = {
"fp32": torch.float32,
"bf16": torch.bfloat16,
"fp16": torch.float16,
}[self.precision]
self.model = AutoModelForSequenceClassification.from_pretrained(
MODEL_ID,
trust_remote_code=True,
torch_dtype=dtype,
**load_kwargs,
).to(self.device).eval()
def probabilities(self, texts: list[str]) -> np.ndarray:
if self.backend == "onnx":
encoded = self.tokenizer(
texts,
return_tensors="np",
padding=True,
truncation=True,
max_length=self.config.max_length,
)
with self.lock:
logits = self.session.run(
["logits"],
{
"input_ids": encoded["input_ids"].astype(np.int64),
"attention_mask": encoded["attention_mask"].astype(np.int64),
},
)[0]
return 1.0 / (1.0 + np.exp(-logits))
encoded = self.tokenizer(
texts,
return_tensors="pt",
padding=True,
truncation=True,
max_length=self.config.max_length,
).to(self.device)
with self.lock, torch.inference_mode():
return torch.sigmoid(self.model(**encoded).logits).cpu().float().numpy()
def classify(self, request: ClassifyRequest) -> list[dict[str, object]]:
if len(request.texts) > MAX_BATCH_SIZE:
raise HTTPException(413, f"at most {MAX_BATCH_SIZE} texts are allowed per request")
probabilities = self.probabilities(request.texts)
labels = [self.config.id2label[index] for index in range(self.config.num_labels)]
results = []
for text, row in zip(request.texts, probabilities, strict=True):
scores = []
for index, label in enumerate(labels):
threshold = (
request.threshold
if request.threshold is not None
else float(self.thresholds[label])
)
scores.append(
{
"label": label,
"probability": float(row[index]),
"threshold": threshold,
"selected": float(row[index]) >= threshold,
}
)
scores.sort(key=lambda item: item["probability"], reverse=True)
results.append(
{
"text": text,
"selected_labels": [item["label"] for item in scores if item["selected"]],
"scores": scores[: min(request.top_k, len(scores))],
}
)
return results
runtime: ModelRuntime | None = None
@asynccontextmanager
async def lifespan(_: FastAPI):
global runtime
runtime = ModelRuntime()
yield
runtime = None
app = FastAPI(title="AraUni Multi-label Intent Classifier", lifespan=lifespan)
bearer_scheme = HTTPBearer(
auto_error=False,
scheme_name="BearerAuth",
description="Enter the MODEL_API_KEY value. Swagger adds the 'Bearer' prefix.",
)
def authorize(
credentials: Annotated[
HTTPAuthorizationCredentials | None,
Depends(bearer_scheme),
],
) -> None:
if MODEL_API_KEY is None:
return
if (
credentials is None
or credentials.scheme.lower() != "bearer"
or not hmac.compare_digest(credentials.credentials, MODEL_API_KEY)
):
raise HTTPException(
401,
"invalid bearer token",
headers={"WWW-Authenticate": "Bearer"},
)
@app.get("/health")
def health() -> dict[str, object]:
return {
"status": "ok",
"model_id": MODEL_ID,
"device": runtime.device if runtime else None,
"backend": runtime.backend if runtime else None,
"precision": runtime.precision if runtime else None,
}
@app.get("/labels", dependencies=[Depends(authorize)])
def labels() -> dict[int, str]:
if runtime is None:
raise HTTPException(503, "model is not ready")
return dict(runtime.config.id2label)
@app.post("/classify", dependencies=[Depends(authorize)])
def classify(request: ClassifyRequest) -> list[dict[str, object]]:
if runtime is None:
raise HTTPException(503, "model is not ready")
return runtime.classify(request)
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