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from __future__ import annotations
from typing import Iterable, Literal, Optional
import os
import time
import traceback
from huggingface_hub import InferenceClient, login, logout as hf_logout
from llm.llm_login import login_huggingface, is_loggedin_huggingface #,is_login_huggingface
from utils.logger import get_logger
## Get logger instance
logger = get_logger(__name__)
class HFChatClient:
"""
Provider‐agnostic LLM client interface.
Encapsulate `huggingface_hub.InferenceClient` setup and chat calls.
Backends:
- model: plain HF model id (e.g., "HuggingFaceH4/zephyr-7b-beta")
- provider: provider-routed id (e.g., "openai/gpt-oss-120b:fireworks-ai")
- endpoint: full inference endpoint URL (e.g., "http://localhost:1234").
"""
def __init__(self,
#api_token: str,
#model_id: str = "gpt2",
provider: str = "huggingface", ## "huggingface2", "openai"
model_id: str = "openai/gpt-oss-120b", ##default_model
hf_provider: str = "huggingface",
endpoint_url: Optional[str] = None,
#backend: Literal["model", "provider", "endpoint"] = [],
backend_choice: Optional[str] = None, #choices=["model-id", "provider", "endpoint"]
system_message: str = "",
max_tokens: int = 4096,
temperature: float = 0.0,
top_p: float = 0.1,
stream: bool = False,
api_token: Optional[str] = None
) -> None:
try:
self.model_id = model_id
self.provider = provider.lower()
self.hf_provider = hf_provider.lower()
self.endpoint_url = endpoint_url
#self.backend = backend
#self.backend_literal: Literal["model", "provider", "endpoint"] = (
'''
self.backend: Literal["model", "provider", "endpoint"] = (
"model" if backend_choice == "Hugging Face Model ID" else (
"provider" if backend_choice == "HF Provider Route" else "endpoint")
),
'''
self.backend: Literal["model", "provider", "endpoint"] = (
"model" if backend_choice == "model-id" else (
"provider" if backend_choice == "provider" else "endpoint")
) ## see Gradio backend_choice dropdown
self.system_message = system_message
self.max_tokens = max_tokens
self.temperature = temperature
self.top_p = top_p
self.stream = stream
self.token = api_token if api_token else None #"" # invalid; preserved
#self.token = token or os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACEHUB_API_TOKEN") ## not preferred
self.base_url = "https://router.huggingface.co/v1" #%22" #HF API proxy
except Exception as exc:
#logger.error(f"client_init_failed", extra={"error": str(exc)}")
tb = traceback.format_exc()
logger.exception(f'✗ client_init_failed", extra={"error": str(exc)}\n{tb}', exc_info=True)
raise RuntimeError(f"✗ Failed to initialise client: {exc}\n{tb}")
##SMY: //TOBE: Deprecated : Moved to llm.llm_login
'''
# # Disable implicit token propagation for determinism
# Explicitly disable implicit token propagation; we rely on explicit auth or env var
os.environ["HF_HUB_DISABLE_IMPLICIT_TOKEN"] = "1"
# Privacy-first login: try interactive CLI first; fallback to provided/env token only if needed
try:
login()
time.sleep(15) ##SMY pause for login. Helpful: pool async opex
logger.info("hf_login", extra={"mode": "cli"})
except Exception as exc:
# Respect common env var names; prefer explicit token arg when provided
fallback_token = self.token or os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACEHUB_API_TOKEN")
if fallback_token:
try:
login(token=fallback_token)
self.token = fallback_token
logger.info("hf_login", extra={"mode": "token"})
except Exception as exc_token:
logger.warning("hf_login_failed", extra={"error": str(exc_token)})
else:
logger.warning("hf_login_failed", extra={"error": str(exc)})
# Silent fallback; client will still work if token is passed directly
#pass
'''
login_huggingface(self.token) if not is_loggedin_huggingface() else logger.log(level=20, msg=f"You are logged in to HF Hub already") ## attempt login if not already logged in. NB: HF CLI login prompt would not display in Process Worker.
##SMY: TODO: Mapped with openai_client.py
#self.islogged_in = is_loggedin_huggingface()
@staticmethod
def _normalise_history(history: list, system_message: str, latest_user_message: str) -> list[dict]:
"""
`prompt` prefixed by system_message if set
Normalise chat history to list of {"role": role, "content": content} dicts.
Supports both dict and tuple formats for history items.
"""
messages: list[dict] = []
if system_message:
messages.append({"role": "system", "content": system_message})
for item in history or []:
if isinstance(item, dict) and "role" in item and "content" in item:
if item["role"] in ("user", "assistant"):
messages.append({"role": item["role"], "content": item["content"]})
elif isinstance(item, (list, tuple)) and len(item) == 2:
usr, asst = item
if usr:
messages.append({"role": "user", "content": usr})
if asst:
messages.append({"role": "assistant", "content": asst})
messages.append({"role": "user", "content": latest_user_message})
return messages
@staticmethod
def _initialise_client(self,
backend: Literal["model", "provider", "endpoint"],
model_id: Optional[str] = None,
hf_provider: Optional[str] = None,
endpoint_url: Optional[str] = None,
token: Optional[str] = None) -> InferenceClient:
try:
match backend:
case "endpoint" | "model":
logger.debug("_initialise_client: initialising with:", extra={"model":model_id}) ## debug
hf_client = InferenceClient(model=model_id or endpoint_url, token=token) #endpoint=target) ##, token=api_token or self.token)
logger.log(20, "client: ", extra={"model":model_id}) ## debug
case "provider":
logger.info("_initialise_client: initialising with:", extra={"provider":hf_provider}) ## debug
hf_client = InferenceClient(provider=hf_provider, model=model_id, token=token) ##, token=api_token or self.token)
#client = client(model = model_id, provider=provider, token=token) ##target
logger.log(20, "client: ", extra={"backend":backend}) ## debug
case _:
raise ValueError("Invalid backend.")
return hf_client
except Exception as exc:
logger.log(40, "_initialise_client: client_init_failed", extra={"error": str(exc)}) ## debug
raise RuntimeError(f"_initialise_client: Failed to initialise client: {exc}")
## wrap HF client for marker
def chat_fn(
self,
message: str,
history: list = [],
) -> Iterable[str]:
"""
messages = self._normalise_history(history, system_message, message)
token = api_token or self.token
"""
## set prompt and token
messages = self._normalise_history(message, history, self.system_message)
#token = api_token or self.token
#token = self.token ## redundant
logger.log(20,"chat: initialising client", extra={
"backend": self.backend, "model": self.model_id, "provider": self.hf_provider, "endpoint": self.endpoint_url,
"stream": self.stream, "max_tokens": self.max_tokens, "temperature": self.temperature, "top_p": self.top_p,
})
## initialised client
try:
client = self._initialise_client(self, self.backend, self.model_id, self.hf_provider, self.endpoint_url, self.token) #api_token)
logger.log(20, "chat: client initialised") ## debug
except Exception as exc:
##logger.error
logger.log(40,"chat client_init_failed", extra={"error": str(exc)})
raise RuntimeError(f"chat: Failed to initialise client: {exc}")
logger.log(20, "chat_start", extra={
"backend": self.backend, "model": self.model_id, "provider": self.hf_provider, "endpoint": self.endpoint_url,
"stream": self.stream, "max_tokens": self.max_tokens, "temperature": self.temperature, "top_p": self.top_p,
})
if self.stream:
acc = ""
for chunk in client.chat_completion(
messages=messages,
#model=client.model, ## moved back to client initialise
max_tokens=self.max_tokens,
stream=True,
temperature=self.temperature,
top_p=self.top_p,
):
delta = getattr(chunk.choices[0].delta, "content", None) or ""
if delta:
acc += delta
yield acc
return
result = client.chat_completion(
messages=messages,
#model=client.model, ## moved back to client initialised
max_tokens=self.max_tokens,
stream=False,
temperature=self.temperature,
top_p=self.top_p,
)
yield result.choices[0].message.content
'''
## future consideration
response = client.text_generation(
#model=model_name,
inputs=prompt,
parameters={
"max_new_tokens": max_new_tokens,
"temperature": temperature,
},
)
return response[0].generated_text
'''
def logout(self) -> bool:
"""Logout from Hugging Face and clear in-process tokens.
Returns True on success, False otherwise.
"""
try:
hf_logout()
except Exception as exc:
logger.error("hf_logout_failed", extra={"error": str(exc)})
return False
# Clear process environment tokens
for key in ("HF_TOKEN", "HUGGINGFACEHUB_API_TOKEN"):
if key in os.environ:
os.environ.pop(key, None)
self.token = None
logger.info("hf_logout_success")
return True
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