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"""
Hugging Face service for model inference and API integration.
"""
import os
import logging
from typing import Dict, List, Optional, Any
# Optional imports - handle missing dependencies gracefully
try:
from huggingface_hub import InferenceClient
HUGGINGFACE_HUB_AVAILABLE = True
except ImportError:
HUGGINGFACE_HUB_AVAILABLE = False
InferenceClient = None
try:
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
TRANSFORMERS_AVAILABLE = True
except ImportError:
TRANSFORMERS_AVAILABLE = False
AutoTokenizer = None
AutoModelForCausalLM = None
pipeline = None
try:
import torch
TORCH_AVAILABLE = True
except ImportError:
TORCH_AVAILABLE = False
torch = None
from config import settings
logger = logging.getLogger(__name__)
class HuggingFaceService:
"""Service for Hugging Face model interactions."""
def __init__(self):
self.client = None
self.tokenizer = None
self.model = None
self.pipeline = None
# Check if dependencies are available
if not HUGGINGFACE_HUB_AVAILABLE:
logger.warning("huggingface_hub not available. Hugging Face API features will be disabled.")
if not TRANSFORMERS_AVAILABLE:
logger.warning("transformers not available. Local model features will be disabled.")
if not TORCH_AVAILABLE:
logger.warning("torch not available. Local model features will be disabled.")
# Initialize Hugging Face client if API token is provided and dependencies are available
if settings.hf_api_token and HUGGINGFACE_HUB_AVAILABLE:
try:
self.client = InferenceClient(token=settings.hf_api_token)
logger.info("Hugging Face Inference Client initialized successfully")
except Exception as e:
logger.error(f"Failed to initialize Hugging Face client: {e}")
# Load local model if specified and dependencies are available
if settings.hf_model_name and TRANSFORMERS_AVAILABLE and TORCH_AVAILABLE:
self._load_local_model()
def _load_local_model(self):
"""Load a local Hugging Face model."""
if not TRANSFORMERS_AVAILABLE or not TORCH_AVAILABLE:
logger.error("Cannot load local model: transformers or torch not available")
return
try:
logger.info(f"Loading local model: {settings.hf_model_name}")
# Load tokenizer and model
self.tokenizer = AutoTokenizer.from_pretrained(settings.hf_model_name)
self.model = AutoModelForCausalLM.from_pretrained(
settings.hf_model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# Create pipeline
self.pipeline = pipeline(
"text-generation",
model=self.model,
tokenizer=self.tokenizer,
max_length=2048,
temperature=0.7,
do_sample=True
)
logger.info("Local model loaded successfully")
except Exception as e:
logger.error(f"Failed to load local model: {e}")
async def generate_text(
self,
prompt: str,
model_name: Optional[str] = None,
max_length: int = 2048,
temperature: float = 0.7,
use_local: bool = False
) -> str:
"""Generate text using Hugging Face models."""
try:
if use_local and self.pipeline and TRANSFORMERS_AVAILABLE:
# Use local model
result = self.pipeline(
prompt,
max_length=max_length,
temperature=temperature,
do_sample=True,
pad_token_id=self.tokenizer.eos_token_id
)
return result[0]['generated_text']
elif self.client and model_name and HUGGINGFACE_HUB_AVAILABLE:
# Use Hugging Face Inference API
result = self.client.text_generation(
prompt,
model=model_name,
max_new_tokens=max_length,
temperature=temperature,
do_sample=True
)
return result
else:
raise Exception("No model available. Please configure HF_API_TOKEN or HF_MODEL_NAME, or install required dependencies")
except Exception as e:
logger.error(f"Hugging Face text generation error: {e}")
raise
async def create_embedding(
self,
text: str,
model_name: str = "sentence-transformers/all-MiniLM-L6-v2"
) -> List[float]:
"""Create embedding using Hugging Face models."""
try:
if self.client and HUGGINGFACE_HUB_AVAILABLE:
# Use Hugging Face Inference API
result = self.client.feature_extraction(
text,
model=model_name
)
return result[0] if isinstance(result, list) else result
else:
raise Exception("Hugging Face client not initialized or dependencies not available")
except Exception as e:
logger.error(f"Hugging Face embedding error: {e}")
raise
async def classify_text(
self,
text: str,
model_name: str = "distilbert-base-uncased-finetuned-sst-2-english"
) -> Dict[str, Any]:
"""Classify text using Hugging Face models."""
try:
if self.client and HUGGINGFACE_HUB_AVAILABLE:
result = self.client.text_classification(
text,
model=model_name
)
return result
else:
raise Exception("Hugging Face client not initialized or dependencies not available")
except Exception as e:
logger.error(f"Hugging Face classification error: {e}")
raise
async def translate_text(
self,
text: str,
source_lang: str = "en",
target_lang: str = "es",
model_name: str = "Helsinki-NLP/opus-mt-en-es"
) -> str:
"""Translate text using Hugging Face models."""
try:
if self.client and HUGGINGFACE_HUB_AVAILABLE:
result = self.client.translation(
text,
model=model_name
)
return result
else:
raise Exception("Hugging Face client not initialized or dependencies not available")
except Exception as e:
logger.error(f"Hugging Face translation error: {e}")
raise
def get_available_models(self) -> List[str]:
"""Get list of available models."""
models = []
if self.client and HUGGINGFACE_HUB_AVAILABLE:
models.append("Hugging Face Inference API (remote)")
if self.pipeline and TRANSFORMERS_AVAILABLE:
models.append(f"Local model: {settings.hf_model_name}")
if not models:
models.append("No models available - check dependencies and configuration")
return models
def get_service_status(self) -> Dict[str, Any]:
"""Get service status and configuration."""
return {
"service": "huggingface",
"status": "available" if (self.client or self.pipeline) else "unavailable",
"dependencies": {
"huggingface_hub": HUGGINGFACE_HUB_AVAILABLE,
"transformers": TRANSFORMERS_AVAILABLE,
"torch": TORCH_AVAILABLE
},
"client_initialized": self.client is not None,
"local_model_loaded": self.pipeline is not None,
"api_token_configured": bool(settings.hf_api_token),
"local_model_configured": bool(settings.hf_model_name),
"available_models": self.get_available_models()
} |