Add centralized text routing policy and premium HF defaults

backend/services/llm_providers/huggingface_provider.py

@@ -70,6 +70,7 @@ else:
 
 from loguru import logger
 from utils.logger_utils import get_service_logger
+from .routing_policy import PREMIUM_DEFAULT_MODEL, SIF_LOW_COST_MODEL_DEFAULTS
 
 # Use service-specific logger to avoid conflicts
 logger = get_service_logger("huggingface_provider")
@@ -90,10 +91,10 @@ except ImportError:
     logger.warn("OpenAI library not available. Install with: pip install openai")
 
 HF_FALLBACK_MODELS = [
-    "openai/gpt-oss-120b:groq",
+    PREMIUM_DEFAULT_MODEL,
     "moonshotai/Kimi-K2-Instruct-0905:groq",
     "meta-llama/Llama-3.1-8B-Instruct:groq",
-    "mistralai/Mistral-7B-Instruct-v0.3:groq",
+    SIF_LOW_COST_MODEL_DEFAULTS[0],
 ]
 
 
@@ -140,7 +141,7 @@ def get_huggingface_api_key() -> str:
 @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
 def huggingface_text_response(
     prompt: str,
-    model: str = "openai/gpt-oss-120b:groq",
+    model: str = PREMIUM_DEFAULT_MODEL,
     temperature: float = 0.7,
     max_tokens: int = 2048,
     top_p: float = 0.9,
@@ -175,7 +176,7 @@ def huggingface_text_response(
     Example:
         result = huggingface_text_response(
             prompt="Write a blog post about AI",
-            model="openai/gpt-oss-120b:groq",
+            model=PREMIUM_DEFAULT_MODEL,
             temperature=0.7,
             max_tokens=2048,
             system_prompt="You are a professional content writer."
@@ -274,7 +275,7 @@ def huggingface_text_response(
 def huggingface_structured_json_response(
     prompt: str,
     schema: Dict[str, Any],
-    model: str = "openai/gpt-oss-120b:groq",
+    model: str = PREMIUM_DEFAULT_MODEL,
     temperature: float = 0.7,
     max_tokens: int = 8192,
     system_prompt: Optional[str] = None
@@ -491,12 +492,12 @@ def get_available_models() -> list:
         list: List of available model identifiers
     """
     return [
-        "openai/gpt-oss-120b:groq",
+        PREMIUM_DEFAULT_MODEL,
         "moonshotai/Kimi-K2-Instruct-0905:groq",
         "Qwen/Qwen2.5-VL-7B-Instruct",
         "meta-llama/Llama-3.1-8B-Instruct:groq",
         "microsoft/Phi-3-medium-4k-instruct:groq",
-        "mistralai/Mistral-7B-Instruct-v0.3:groq"
+        SIF_LOW_COST_MODEL_DEFAULTS[0]
     ]
 
 def validate_model(model: str) -> bool: