import os
from functools import lru_cache
from typing import Any, Self

from pydantic import Field, model_validator

from pydantic_settings import BaseSettings, SettingsConfigDict


class Settings(BaseSettings):
    model_config = SettingsConfigDict(
        env_file=".env",
        env_file_encoding="utf-8",
        extra="ignore",
        case_sensitive=False,
        populate_by_name=True,
    )

    @model_validator(mode="before")
    @classmethod
    def _map_max_upload_env_alias(cls, data: Any) -> Any:
        if not isinstance(data, dict):
            return data
        out = dict(data)
        if out.get("max_file_size_mb") in (None, "") and out.get("max_upload_size_mb") not in (None, ""):
            out["max_file_size_mb"] = out.pop("max_upload_size_mb")
        elif "max_upload_size_mb" in out and "max_file_size_mb" not in out:
            out["max_file_size_mb"] = out.pop("max_upload_size_mb")
        return out

    app_name: str = Field(default="DocuAudit AI", description="FastAPI title and product name")
    app_version: str = Field(default="1.0.0", description="Application version")
    app_description: str = Field(
        default=(
            "Multi-document RAG API for high-stakes consulting environments. "
            "Every answer is grounded in source documents with full audit trails."
        ),
        description="OpenAPI /docs description",
    )
    llm_provider: str = Field(default="ollama", description="Embedding provider")
    
    openai_api_key: str | None = Field(default=None, description="OpenAI API key")
    openai_model: str = "gpt-4o"
    openai_embedding_model: str = "text-embedding-3-small"
    
    anthropic_api_key: str = ""
    anthropic_model: str = "claude-3-5-sonnet-20241022"

    huggingface_api_key: str = ""
    huggingface_model: str = Field(
        default="meta-llama/Meta-Llama-3-8B-Instruct",
        description=(
            "HF chat model id (use a repo your Hub account already has access to; Llama 3.1 needs the "
            "separate Llama 3.1 gate). Chat tries hf-inference then router auto when unset."
        ),
    )
    huggingface_embedding_model: str = "sentence-transformers/all-MiniLM-L6-v2"
    huggingface_inference_provider: str | None = Field(
        default=None,
        description=(
            "Optional huggingface_hub InferenceClient provider (e.g. hf-inference, together). "
            "Unset uses hf-inference in chat code; set to `auto` for router auto-routing."
        ),
    )

    ollama_base_url: str = Field(default="http://localhost:11434", description="Ollama base URL")
    ollama_chat_model: str = "llama3.1:8b"
    ollama_embedding_model: str = "nomic-embed-text"

    chroma_persist_directory: str = Field(default="./data/chroma", description="Chroma persistence path")

    chroma_persist_dir: str = Field(default="./chroma", description="Chroma persistence path")
    chroma_collection_name: str = "docuaudit_docs"

    chunk_size: int = Field(default=1000, ge=100, le=8000, description="Chunk size for splitting")
    chunk_overlap: int = Field(default=200, ge=0, le=2000, description="Chunk overlap for splitting")
    top_k_results: int = Field(default=5, ge=1, le=20, description="Default number of chunks to retrieve")

    audit_db_path: str = "./audit.db"
    jobs_db_path: str = Field(default="./data/jobs.db", description="SQLite path for ingest job tracking")

    max_file_size_mb: int = Field(default=50, ge=1, le=200, description="Max upload file size (MB)")
    max_documents_per_batch: int = Field(default=100, ge=1, le=1000, description="Max documents per batch")

    @model_validator(mode="after")
    def _space_default_llm_provider(self) -> Self:
        """Hugging Face Spaces do not run Ollama locally; use Hub inference unless the user set LLM_PROVIDER."""
        if not (os.environ.get("SPACE_ID") or "").strip():
            return self
        if "LLM_PROVIDER" in os.environ:
            return self
        if self.llm_provider.lower() != "ollama":
            return self
        self.llm_provider = "huggingface"
        return self

    @model_validator(mode="after")
    def _huggingface_token_from_hub_env(self) -> Self:
        """When using the Hugging Face inference stack, accept the Hub token from standard env names.

        Spaces often expose `HF_TOKEN` (read/write per Space secrets). Map it into `huggingface_api_key`
        when `HUGGINGFACE_API_KEY` is unset so embedder/chat clients receive a token.
        """
        if self.llm_provider.lower() != "huggingface":
            return self
        if (self.huggingface_api_key or "").strip():
            return self
        for key in ("HF_TOKEN", "HUGGING_FACE_HUB_TOKEN"):
            token = (os.environ.get(key) or "").strip()
            if token:
                self.huggingface_api_key = token
                break
        return self


@lru_cache
def get_settings() -> Settings:
    return Settings()