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"""Inference engine using Hugging Face API or local transformers."""
import logging
from typing import Any
from huggingface_hub import InferenceClient
from .config import Settings
logger = logging.getLogger(__name__)
class InferenceEngine:
"""Handles model loading and inference."""
def __init__(self, settings: Settings) -> None:
"""Initialize the inference engine."""
self.settings = settings
self.client: InferenceClient | None = None
self.pipeline = None
self.model_loaded = False
self.use_api = settings.use_api
def load_model(self) -> None:
"""Load the model (API client or local pipeline)."""
if self.use_api:
self._init_api_client()
else:
self._init_local_pipeline()
def _init_api_client(self) -> None:
"""Initialize the HF Inference API client."""
logger.info(
"Initializing HF Inference API client for model: %s",
self.settings.model_name,
)
self.client = InferenceClient(
model=self.settings.model_name,
token=self.settings.api_token,
)
self.model_loaded = True
logger.info("HF Inference API client ready")
def _init_local_pipeline(self) -> None:
"""Load the model locally using transformers."""
try:
from transformers import pipeline
except ImportError:
raise ImportError(
"transformers and torch are required for local inference. "
"Install them with: pip install transformers torch"
)
logger.info(
"Loading local model: %s for task: %s",
self.settings.model_name,
self.settings.task,
)
self.pipeline = pipeline(
task=self.settings.task,
model=self.settings.model_name,
device=self.settings.device if self.settings.device != "cpu" else -1,
)
self.model_loaded = True
logger.info("Local model loaded successfully")
def predict(
self, inputs: str | list[str], parameters: dict[str, Any] | None = None
) -> list[Any]:
"""Run inference on the input(s)."""
if not self.model_loaded:
raise RuntimeError("Model not loaded")
if self.use_api:
return self._predict_api(inputs, parameters)
else:
return self._predict_local(inputs, parameters)
def _predict_api(
self, inputs: str | list[str], parameters: dict[str, Any] | None = None
) -> list[Any]:
"""Run inference using HF Inference API."""
params = parameters or {}
task = self.settings.task
if isinstance(inputs, str):
inputs_list = [inputs]
else:
inputs_list = inputs
results = []
for text in inputs_list:
result = self._call_api(task, text, params)
results.append(result)
return results
def _call_api(self, task: str, text: str, params: dict[str, Any]) -> Any:
"""Call the appropriate API method based on task."""
if task in ("text-classification", "sentiment-analysis"):
return self.client.text_classification(text, **params)
elif task == "text-generation":
return self.client.text_generation(text, **params)
elif task == "summarization":
return self.client.summarization(text, **params)
elif task == "translation":
return self.client.translation(text, **params)
elif task == "fill-mask":
return self.client.fill_mask(text, **params)
elif task == "question-answering":
context = params.pop("context", "")
return self.client.question_answering(question=text, context=context)
elif task == "feature-extraction":
return self.client.feature_extraction(text, **params)
else:
# Generic post for unsupported tasks
return self.client.post(json={"inputs": text, **params})
def _predict_local(
self, inputs: str | list[str], parameters: dict[str, Any] | None = None
) -> list[Any]:
"""Run inference using local transformers pipeline."""
params = parameters or {}
results = self.pipeline(inputs, **params)
if isinstance(inputs, str):
return [results]
return results
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