Delete inference.py

inference.py

@@ -1,97 +0,0 @@
-# inference.py
-# -------------------------------------------------------------
-# Unified wrapper around hf_client.get_inference_client
-# with automatic provider‑routing based on model registry
-# (see models.py) and graceful fall‑back to Groq.
-# -------------------------------------------------------------
-from __future__ import annotations
-
-from typing import Dict, Generator, List, Optional
-
-from hf_client import get_inference_client
-from models import find_model
-
-
-# ------------------------------------------------------------------
-# Helpers
-# ------------------------------------------------------------------
-def _resolve_provider(model_id: str, override: str | None) -> str:
-    """
-    Decide which provider to use.
-
-    Priority:
-    1. Explicit *override* arg supplied by caller.
-    2. Model registry default_provider (see models.py).
-    3. "auto" – lets HF route to the first available provider.
-    """
-    if override:
-        return override
-
-    meta = find_model(model_id)
-    return getattr(meta, "default_provider", "auto") if meta else "auto"
-
-
-# ------------------------------------------------------------------
-# Public API
-# ------------------------------------------------------------------
-def chat_completion(
-    model_id: str,
-    messages: List[Dict[str, str]],
-    provider: Optional[str] = None,
-    max_tokens: int = 4096,
-    **kwargs,
-) -> str:
-    """
-    Blocking convenience wrapper – returns the full assistant reply.
-
-    Parameters
-    ----------
-    model_id      : HF or provider‑qualified model path (e.g. "openai/gpt-4").
-    messages      : OpenAI‑style [{'role': ..., 'content': ...}, …].
-    provider      : Optional provider override; otherwise auto‑resolved.
-    max_tokens    : Token budget for generation.
-    kwargs        : Forward‑compatible extra arguments (temperature, etc.).
-
-    Returns
-    -------
-    str – assistant message content.
-    """
-    client = get_inference_client(model_id, _resolve_provider(model_id, provider))
-    resp = client.chat.completions.create(
-        model=model_id,
-        messages=messages,
-        max_tokens=max_tokens,
-        **kwargs,
-    )
-    return resp.choices[0].message.content
-
-
-def stream_chat_completion(
-    model_id: str,
-    messages: List[Dict[str, str]],
-    provider: Optional[str] = None,
-    max_tokens: int = 4096,
-    **kwargs,
-) -> Generator[str, None, None]:
-    """
-    Yield the assistant response *incrementally*.
-
-    Example
-    -------
-    >>> for chunk in stream_chat_completion(model, msgs):
-    ...     print(chunk, end='', flush=True)
-    """
-    client = get_inference_client(model_id, _resolve_provider(model_id, provider))
-    stream = client.chat.completions.create(
-        model=model_id,
-        messages=messages,
-        max_tokens=max_tokens,
-        stream=True,
-        **kwargs,
-    )
-
-    # HF Inference returns chunks with .choices[0].delta.content
-    for chunk in stream:
-        delta: str | None = getattr(chunk.choices[0].delta, "content", None)
-        if delta:
-            yield delta