src/config.py

"""
Configuration Management Module

This module provides secure, robust configuration management with:
- Environment variable handling with secure defaults
- Cache directory management with automatic fallbacks
- Comprehensive logging and error handling
- Security best practices for sensitive data
- Backward compatibility with existing code

Environment Variables:
    HF_TOKEN: HuggingFace API token (required for API access)
    HF_HOME: Primary cache directory for HuggingFace models
    TRANSFORMERS_CACHE: Alternative cache directory path
    MAX_WORKERS: Maximum worker threads (default: 4)
    CACHE_TTL: Cache time-to-live in seconds (default: 3600)
    DB_PATH: Database file path (default: sessions.db)
    LOG_LEVEL: Logging level (default: INFO)
    LOG_FORMAT: Log format (default: json)

Security Notes:
    - Never commit .env files to version control
    - Use environment variables for all sensitive data
    - Cache directories are automatically secured with proper permissions
"""

import os
import logging
from pathlib import Path
from typing import Optional
from pydantic_settings import BaseSettings
from pydantic import Field, validator

# Configure logging
logger = logging.getLogger(__name__)


class CacheDirectoryManager:
    """
    Manages cache directory with secure fallback mechanism.
    
    Implements:
    - Multi-level fallback strategy
    - Permission validation
    - Automatic directory creation
    - Security best practices
    """
    
    @staticmethod
    def get_cache_directory() -> str:
        """
        Get cache directory with secure fallback chain.
        
        Priority order:
        1. HF_HOME environment variable
        2. TRANSFORMERS_CACHE environment variable
        3. User home directory (~/.cache/huggingface)
        4. User-specific fallback directory
        5. Temporary directory (last resort)
        
        Returns:
            str: Path to writable cache directory
        """
        # Priority order for cache directory
        # In Docker, ~ may resolve to / which causes permission issues
        # So we prefer /tmp over ~/.cache in containerized environments
        is_docker = os.path.exists("/.dockerenv") or os.path.exists("/tmp")
        
        cache_candidates = [
            os.getenv("HF_HOME"),
            os.getenv("TRANSFORMERS_CACHE"),
            # In Docker, prefer /tmp over ~/.cache
            "/tmp/huggingface_cache" if is_docker else None,
            os.path.join(os.path.expanduser("~"), ".cache", "huggingface") if os.path.expanduser("~") and not is_docker else None,
            os.path.join(os.path.expanduser("~"), ".cache", "huggingface_fallback") if os.path.expanduser("~") and not is_docker else None,
            "/tmp/huggingface_cache" if not is_docker else None,
            "/tmp/huggingface"  # Final fallback
        ]
        
        for cache_dir in cache_candidates:
            if not cache_dir:
                continue
                
            try:
                # Ensure directory exists
                cache_path = Path(cache_dir)
                cache_path.mkdir(parents=True, exist_ok=True)
                
                # Set secure permissions (rwxr-xr-x)
                try:
                    os.chmod(cache_path, 0o755)
                except (OSError, PermissionError):
                    # If we can't set permissions, continue if directory is writable
                    pass
                
                # Test write access
                test_file = cache_path / ".write_test"
                try:
                    test_file.write_text("test")
                    test_file.unlink()
                    
                    logger.info(f"✓ Cache directory verified: {cache_dir}")
                    return str(cache_path)
                    
                except (PermissionError, OSError) as e:
                    logger.debug(f"Write test failed for {cache_dir}: {e}")
                    continue
                    
            except (PermissionError, OSError) as e:
                logger.debug(f"Could not create/access {cache_dir}: {e}")
                continue
        
        # If all candidates failed, use emergency fallback
        fallback = "/tmp/huggingface_emergency"
        try:
            Path(fallback).mkdir(parents=True, exist_ok=True)
            logger.warning(f"Using emergency fallback cache: {fallback}")
            return fallback
        except Exception as e:
            logger.error(f"Emergency fallback also failed: {e}")
            # Return a default that will fail gracefully later
            return "/tmp/huggingface"


class Settings(BaseSettings):
    """
    Application settings with secure defaults and validation.
    
    Backward Compatibility:
    - All existing attributes are preserved
    - hf_token is accessible as string (via property)
    - hf_cache_dir is accessible as property (works like before)
    - All defaults match original implementation
    """
    
    # ==================== HuggingFace Configuration ====================
    
    # BACKWARD COMPAT: hf_token as regular field (backward compatible)
    hf_token: str = Field(
        default="",
        description="HuggingFace API token",
        env="HF_TOKEN"
    )
    
    @validator("hf_token", pre=True)
    def validate_hf_token(cls, v):
        """Validate HF token (backward compatible)"""
        if v is None:
            return ""
        token = str(v) if v else ""
        if not token:
            logger.debug("HF_TOKEN not set")
        return token
    
    @property
    def hf_cache_dir(self) -> str:
        """
        Get cache directory with automatic fallback and validation.
        
        BACKWARD COMPAT: Works like the original hf_cache_dir field.
        
        Returns:
            str: Path to writable cache directory
        """
        if not hasattr(self, '_cached_cache_dir'):
            try:
                self._cached_cache_dir = CacheDirectoryManager.get_cache_directory()
            except Exception as e:
                logger.error(f"Cache directory setup failed: {e}")
                # Fallback to original default
                fallback = os.getenv("HF_HOME", "/tmp/huggingface")
                Path(fallback).mkdir(parents=True, exist_ok=True)
                self._cached_cache_dir = fallback
        
        return self._cached_cache_dir
    
    # ==================== Novita AI Configuration ====================
    
    novita_api_key: str = Field(
        default="",
        description="Novita AI API key (required)",
        env="NOVITA_API_KEY"
    )
    
    novita_base_url: str = Field(
        default="https://api.novita.ai/dedicated/v1/openai",
        description="Novita AI dedicated endpoint base URL",
        env="NOVITA_BASE_URL"
    )
    
    novita_model: str = Field(
        default="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B:de-1a706eeafbf3ebc2",
        description="Novita AI dedicated endpoint model ID",
        env="NOVITA_MODEL"
    )
    
    # DeepSeek-R1 optimized settings
    deepseek_r1_temperature: float = Field(
        default=0.6,
        description="Temperature for DeepSeek-R1 models (0.5-0.7 range, 0.6 recommended)",
        env="DEEPSEEK_R1_TEMPERATURE"
    )
    
    deepseek_r1_force_reasoning: bool = Field(
        default=True,
        description="Force DeepSeek-R1 to start with reasoning trigger",
        env="DEEPSEEK_R1_FORCE_REASONING"
    )
    
    # Token Allocation Configuration
    user_input_max_tokens: int = Field(
        default=8000,
        description="Maximum tokens dedicated for user input (prioritized over context)",
        env="USER_INPUT_MAX_TOKENS"
    )
    
    context_preparation_budget: int = Field(
        default=28000,
        description="Maximum tokens for context preparation (includes user input + context)",
        env="CONTEXT_PREPARATION_BUDGET"
    )
    
    context_pruning_threshold: int = Field(
        default=28000,
        description="Context pruning threshold (should match context_preparation_budget)",
        env="CONTEXT_PRUNING_THRESHOLD"
    )
    
    prioritize_user_input: bool = Field(
        default=True,
        description="Always prioritize user input over historical context",
        env="PRIORITIZE_USER_INPUT"
    )
    
    @validator("novita_api_key", pre=True)
    def validate_novita_api_key(cls, v):
        """Validate and clean Novita API key"""
        if v is None:
            return ""
        return str(v).strip()
    
    @validator("deepseek_r1_temperature", pre=True)
    def validate_deepseek_temperature(cls, v):
        """Validate DeepSeek-R1 temperature is in recommended range"""
        if isinstance(v, str):
            v = float(v)
        temp = float(v) if v else 0.6
        return max(0.5, min(0.7, temp))
    
    @validator("deepseek_r1_force_reasoning", pre=True)
    def validate_force_reasoning(cls, v):
        """Convert string to boolean for force_reasoning"""
        if isinstance(v, str):
            return v.lower() in ("true", "1", "yes", "on")
        return bool(v)
    
    @validator("user_input_max_tokens", pre=True)
    def validate_user_input_tokens(cls, v):
        """Validate user input token limit"""
        val = int(v) if v else 8000
        return max(1000, min(20000, val))
    
    @validator("context_preparation_budget", pre=True)
    def validate_context_budget(cls, v):
        """Validate context preparation budget"""
        val = int(v) if v else 28000
        return max(4000, min(120000, val))
    
    # ==================== Model Configuration ====================
    
    default_model: str = Field(
        default="meta-llama/Llama-3.1-8B-Instruct:cerebras",
        description="Primary model for reasoning tasks (Cerebras deployment with 4-bit quantization)"
    )
    
    embedding_model: str = Field(
        default="intfloat/e5-large-v2",
        description="Model for embeddings (upgraded: 1024-dim embeddings)"
    )
    
    classification_model: str = Field(
        default="meta-llama/Llama-3.1-8B-Instruct:cerebras",
        description="Model for classification tasks (Cerebras deployment)"
    )
    
    # ==================== Performance Configuration ====================
    
    max_workers: int = Field(
        default=4,
        description="Maximum worker threads for parallel processing",
        env="MAX_WORKERS"
    )
    
    @validator("max_workers", pre=True)
    def validate_max_workers(cls, v):
        """Validate and convert max_workers (backward compatible)"""
        if v is None:
            return 4
        if isinstance(v, str):
            try:
                v = int(v)
            except ValueError:
                logger.warning(f"Invalid MAX_WORKERS value: {v}, using default 4")
                return 4
        try:
            val = int(v)
            return max(1, min(16, val))  # Clamp between 1 and 16
        except (ValueError, TypeError):
            return 4
    
    cache_ttl: int = Field(
        default=3600,
        description="Cache time-to-live in seconds",
        env="CACHE_TTL"
    )
    
    @validator("cache_ttl", pre=True)
    def validate_cache_ttl(cls, v):
        """Validate cache TTL (backward compatible)"""
        if v is None:
            return 3600
        if isinstance(v, str):
            try:
                v = int(v)
            except ValueError:
                return 3600
        try:
            return max(0, int(v))
        except (ValueError, TypeError):
            return 3600
    
    # ==================== Database Configuration ====================
    
    db_path: str = Field(
        default="sessions.db",
        description="Path to SQLite database file",
        env="DB_PATH"
    )
    
    @validator("db_path", pre=True)
    def validate_db_path(cls, v):
        """Validate db_path with Docker fallback (backward compatible)"""
        if v is None:
            # Check if we're in Docker (HF Spaces) - if so, use /tmp
            if os.path.exists("/.dockerenv") or os.path.exists("/tmp"):
                return "/tmp/sessions.db"
            return "sessions.db"
        return str(v)
    
    faiss_index_path: str = Field(
        default="embeddings.faiss",
        description="Path to FAISS index file",
        env="FAISS_INDEX_PATH"
    )
    
    @validator("faiss_index_path", pre=True)
    def validate_faiss_path(cls, v):
        """Validate faiss path with Docker fallback (backward compatible)"""
        if v is None:
            # Check if we're in Docker (HF Spaces) - if so, use /tmp
            if os.path.exists("/.dockerenv") or os.path.exists("/tmp"):
                return "/tmp/embeddings.faiss"
            return "embeddings.faiss"
        return str(v)
    
    # ==================== Session Configuration ====================
    
    session_timeout: int = Field(
        default=3600,
        description="Session timeout in seconds",
        env="SESSION_TIMEOUT"
    )
    
    @validator("session_timeout", pre=True)
    def validate_session_timeout(cls, v):
        """Validate session timeout (backward compatible)"""
        if v is None:
            return 3600
        if isinstance(v, str):
            try:
                v = int(v)
            except ValueError:
                return 3600
        try:
            return max(60, int(v))
        except (ValueError, TypeError):
            return 3600
    
    max_session_size_mb: int = Field(
        default=10,
        description="Maximum session size in megabytes",
        env="MAX_SESSION_SIZE_MB"
    )
    
    @validator("max_session_size_mb", pre=True)
    def validate_max_session_size(cls, v):
        """Validate max session size (backward compatible)"""
        if v is None:
            return 10
        if isinstance(v, str):
            try:
                v = int(v)
            except ValueError:
                return 10
        try:
            return max(1, min(100, int(v)))
        except (ValueError, TypeError):
            return 10
    
    # ==================== Mobile Optimization ====================
    
    mobile_max_tokens: int = Field(
        default=800,
        description="Maximum tokens for mobile responses",
        env="MOBILE_MAX_TOKENS"
    )
    
    @validator("mobile_max_tokens", pre=True)
    def validate_mobile_max_tokens(cls, v):
        """Validate mobile max tokens (backward compatible)"""
        if v is None:
            return 800
        if isinstance(v, str):
            try:
                v = int(v)
            except ValueError:
                return 800
        try:
            return max(100, min(2000, int(v)))
        except (ValueError, TypeError):
            return 800
    
    mobile_timeout: int = Field(
        default=15000,
        description="Mobile request timeout in milliseconds",
        env="MOBILE_TIMEOUT"
    )
    
    @validator("mobile_timeout", pre=True)
    def validate_mobile_timeout(cls, v):
        """Validate mobile timeout (backward compatible)"""
        if v is None:
            return 15000
        if isinstance(v, str):
            try:
                v = int(v)
            except ValueError:
                return 15000
        try:
            return max(5000, min(60000, int(v)))
        except (ValueError, TypeError):
            return 15000
    
    # ==================== API Configuration ====================
    
    gradio_port: int = Field(
        default=7860,
        description="Gradio server port",
        env="GRADIO_PORT"
    )
    
    @validator("gradio_port", pre=True)
    def validate_gradio_port(cls, v):
        """Validate gradio port (backward compatible)"""
        if v is None:
            return 7860
        if isinstance(v, str):
            try:
                v = int(v)
            except ValueError:
                return 7860
        try:
            return max(1024, min(65535, int(v)))
        except (ValueError, TypeError):
            return 7860
    
    gradio_host: str = Field(
        default="0.0.0.0",
        description="Gradio server host",
        env="GRADIO_HOST"
    )
    
    # ==================== Logging Configuration ====================
    
    log_level: str = Field(
        default="INFO",
        description="Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)",
        env="LOG_LEVEL"
    )
    
    @validator("log_level")
    def validate_log_level(cls, v):
        """Validate log level (backward compatible)"""
        if not v:
            return "INFO"
        valid_levels = ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
        if v.upper() not in valid_levels:
            logger.warning(f"Invalid log level: {v}, using INFO")
            return "INFO"
        return v.upper()
    
    log_format: str = Field(
        default="json",
        description="Log format (json or text)",
        env="LOG_FORMAT"
    )
    
    @validator("log_format")
    def validate_log_format(cls, v):
        """Validate log format (backward compatible)"""
        if not v:
            return "json"
        if v.lower() not in ["json", "text"]:
            logger.warning(f"Invalid log format: {v}, using json")
            return "json"
        return v.lower()
    
    # ==================== Pydantic Configuration ====================
    
    class Config:
        """Pydantic configuration"""
        env_file = ".env"
        env_file_encoding = "utf-8"
        case_sensitive = False
        validate_assignment = True
        # Allow extra fields for backward compatibility
        extra = "ignore"
    
    # ==================== Utility Methods ====================
    
    def validate_configuration(self) -> bool:
        """
        Validate configuration and log status.
        
        Returns:
            bool: True if configuration is valid, False otherwise
        """
        try:
            # Validate cache directory
            cache_dir = self.hf_cache_dir
            if logger.isEnabledFor(logging.INFO):
                logger.info("Configuration validated:")
                logger.info(f"  - Cache directory: {cache_dir}")
                logger.info(f"  - Max workers: {self.max_workers}")
                logger.info(f"  - Log level: {self.log_level}")
                logger.info(f"  - HF token: {'Set' if self.hf_token else 'Not set'}")
            
            return True
            
        except Exception as e:
            logger.error(f"Configuration validation failed: {e}")
            return False


# ==================== Global Settings Instance ====================

def get_settings() -> Settings:
    """
    Get or create global settings instance.
    
    Returns:
        Settings: Global settings instance
        
    Note:
        This function ensures settings are loaded once and cached.
    """
    if not hasattr(get_settings, '_instance'):
        get_settings._instance = Settings()
        # Validate on first load (non-blocking)
        try:
            get_settings._instance.validate_configuration()
        except Exception as e:
            logger.warning(f"Configuration validation warning: {e}")
    return get_settings._instance


# Create global settings instance (backward compatible)
settings = get_settings()

# Log configuration on import (at INFO level, non-blocking)
if logger.isEnabledFor(logging.INFO):
    try:
        logger.info("=" * 60)
        logger.info("Configuration Loaded")
        logger.info("=" * 60)
        logger.info(f"Cache directory: {settings.hf_cache_dir}")
        logger.info(f"Max workers: {settings.max_workers}")
        logger.info(f"Log level: {settings.log_level}")
        logger.info("=" * 60)
    except Exception as e:
        logger.debug(f"Configuration logging skipped: {e}")