config/settings.py

import os
from dataclasses import dataclass
from typing import Optional
from pathlib import Path
from dotenv import load_dotenv
load_dotenv()

@dataclass
class Settings:
    """Application-wide configuration settings."""

    # LLM Provider settings
    llm_provider: str = os.getenv("LLM_PROVIDER", "auto")

    # Hugging Face settings
    hf_token: str = os.getenv("HF_TOKEN", "")
    hf_chat_model: str = os.getenv("HF_CHAT_MODEL", "Qwen/Qwen2.5-7B-Instruct")
    hf_temperature: float = 0.001
    hf_max_new_tokens: int = 512

    # Model settings
    model_name: str = os.getenv("MODEL_NAME", "Qwen/Qwen2.5-7B-Instruct")

    # Audio provider settings
    audio_provider: str = os.getenv("AUDIO_PROVIDER", "auto")
    tts_model: str = os.getenv("TTS_MODEL", "canopylabs/orpheus-3b-0.1-ft")
    stt_model: str = os.getenv("STT_MODEL", "openai/whisper-large-v3")


    # Screen sharing settings
    screen_capture_interval: float = float(os.getenv("SCREEN_CAPTURE_INTERVAL", "1.0"))
    screen_compression_quality: int = int(os.getenv("SCREEN_COMPRESSION_QUALITY", "50"))
    max_width: int = int(os.getenv("SCREEN_MAX_WIDTH", "3440"))
    max_height: int = int(os.getenv("SCREEN_MAX_HEIGHT", "1440"))
    NEBIUS_MODEL: str = os.getenv("NEBIUS_MODEL", "google/gemma-3-27b-it")
    NEBIUS_API_KEY: str = os.getenv("NEBIUS_API_KEY", "Not found")
    NEBIUS_BASE_URL: str = os.getenv("NEBIUS_BASE_URL", "https://api.studio.nebius.com/v1/")

    # Hyper-V settings
    hyperv_enabled: bool = os.getenv("HYPERV_ENABLED", "false").lower() == "true"
    hyperv_host: str = os.getenv("HYPERV_HOST", "localhost")
    hyperv_username: Optional[str] = os.getenv("HYPERV_USERNAME")
    hyperv_password: Optional[str] = os.getenv("HYPERV_PASSWORD")

    # Application settings
    max_conversation_history: int = int(os.getenv("MAX_CONVERSATION_HISTORY", "50"))
    temp_dir: str = os.getenv("TEMP_DIR", "./temp")
    log_level: str = os.getenv("LOG_LEVEL", "INFO")

    def __post_init__(self):
        # Ensure necessary directories exist
        Path(self.temp_dir).mkdir(exist_ok=True, parents=True)
        Path("./config").mkdir(exist_ok=True, parents=True)
        Path("./logs").mkdir(exist_ok=True, parents=True)

    def is_hf_token_valid(self) -> bool:
        return bool(self.hf_token and len(self.hf_token) > 10)

    @property
    def effective_llm_provider(self) -> str:
        if self.llm_provider == "auto":
            return "huggingface" if self.is_hf_token_valid() else "openai"
        return self.llm_provider

    @property
    def effective_audio_provider(self) -> str:
        if self.audio_provider == "auto":
            return "huggingface" if self.is_hf_token_valid() else "openai"
        return self.audio_provider

    @property
    def llm_endpoint(self) -> str:
        if self.effective_llm_provider == "huggingface":
            return f"https://api-inference.huggingface.co/models/{self.hf_chat_model}"
        return self.openai_endpoint

    @property
    def llm_api_key(self) -> str:
        return self.hf_token if self.effective_llm_provider == "huggingface" else self.openai_api_key

    @property
    def effective_model_name(self) -> str:
        return self.hf_chat_model if self.effective_llm_provider == "huggingface" else self.model_name