"""
config.py - Environment-driven configuration for the Conflict Checker.

All knobs come from backend/.env (see .env.example). Paths are derived from this
file's location so the app runs regardless of the current working directory.
"""

import os
from dotenv import load_dotenv

load_dotenv(os.path.join(os.path.dirname(os.path.abspath(__file__)), ".env"))

_BASE_DIR = os.path.dirname(os.path.abspath(__file__))

# -- AI Backend (OpenRouter) ----------------------------------------
# One multimodal model does both extraction (Stage 1) and conflict
# reasoning (Stage 3). Override MODEL per-stage if you ever split them.
AI_BASE_URL = os.getenv("AI_BASE_URL", "https://openrouter.ai/api/v1")
AI_API_KEY  = os.getenv("AI_API_KEY",  "")
MODEL       = os.getenv("MODEL",       "google/gemini-2.5-pro")
# Text model when text stages run on OpenRouter (non-hybrid). Falls back to
# MODEL when unset.
TEXT_MODEL  = os.getenv("TEXT_MODEL",  "") or MODEL

# Agent-mode model overrides (OpenRouter IDs). Empty values inherit the
# matching general-purpose model so the skeleton requires no extra config.
AGENT_EXTRACT_MODEL      = os.getenv("AGENT_EXTRACT_MODEL", "") or MODEL
AGENT_INDEX_MODEL        = os.getenv("AGENT_INDEX_MODEL", "") or TEXT_MODEL
AGENT_JURISDICTION_MODEL = os.getenv("AGENT_JURISDICTION_MODEL", "") or TEXT_MODEL
AGENT_LINKER_MODEL       = os.getenv("AGENT_LINKER_MODEL", "") or TEXT_MODEL
AGENT_CONFLICT_MODEL     = os.getenv("AGENT_CONFLICT_MODEL", "") or MODEL
AGENT_CODE_MODEL         = os.getenv("AGENT_CODE_MODEL", "") or TEXT_MODEL
AGENT_CONSTRUCT_MODEL    = os.getenv("AGENT_CONSTRUCT_MODEL", "") or TEXT_MODEL
AGENT_COMPLETENESS_MODEL = os.getenv("AGENT_COMPLETENESS_MODEL", "") or TEXT_MODEL
AGENT_BRAIN_MODEL        = os.getenv("AGENT_BRAIN_MODEL", "") or TEXT_MODEL
AGENT_RFI_MODEL          = os.getenv("AGENT_RFI_MODEL", "") or TEXT_MODEL

# Agent-mode hard scope limits. These are intentionally independent of Classic
# batching so Agent workers can never grow into whole-set reasoning calls.
AGENT_LINK_MAX_ASSERTIONS    = int(os.getenv("AGENT_LINK_MAX_ASSERTIONS", "60"))
AGENT_CLUSTER_MAX_ASSERTIONS = int(os.getenv("AGENT_CLUSTER_MAX_ASSERTIONS", "24"))
AGENT_CONFLICT_MAX_IMAGES    = int(os.getenv("AGENT_CONFLICT_MAX_IMAGES", "6"))
AGENT_CODE_BATCH_SIZE        = int(os.getenv("AGENT_CODE_BATCH_SIZE", "60"))
AGENT_BRAIN_MAX_TOKENS       = int(os.getenv("AGENT_BRAIN_MAX_TOKENS", "16384"))
AGENT_LINK_CONCURRENCY       = int(os.getenv("AGENT_LINK_CONCURRENCY", "4"))
AGENT_CONFLICT_CONCURRENCY   = int(os.getenv("AGENT_CONFLICT_CONCURRENCY", "4"))
AGENT_SPECIALIST_CONCURRENCY = int(os.getenv("AGENT_SPECIALIST_CONCURRENCY", "4"))
AGENT_RFI_CONCURRENCY        = int(os.getenv("AGENT_RFI_CONCURRENCY", "4"))

# Agent-mode human-review gate. When on (default), Agent runs stop after the
# Brain merge and wait for human decisions before RFIs/final report/email go
# out. AGENT_REVIEW_AUDIT_SAMPLE caps how many clean clusters get added to the
# queue as non-blocking spot-checks. REVIEW_AGGREGATE_INCLUDE_TEXT controls
# whether future cross-job review feedback aggregation may include verbatim
# source_text/images/comments (off by default = privacy-preserving).
AGENT_REQUIRE_REVIEW = os.getenv("AGENT_REQUIRE_REVIEW", "true").strip().lower() in ("1", "true", "yes")
AGENT_REVIEW_AUDIT_SAMPLE = int(os.getenv("AGENT_REVIEW_AUDIT_SAMPLE", "5"))
# NOTE: currently unwired - reserved for future cross-job aggregation tooling.
REVIEW_AGGREGATE_INCLUDE_TEXT = os.getenv("REVIEW_AGGREGATE_INCLUDE_TEXT", "false").strip().lower() in ("1", "true", "yes")

# -- Hybrid (local text LLM) ----------------------------------------
# Optional OpenAI-compatible local endpoint (e.g. a vLLM box) for the text-only
# QAQC stages. Vision stages ALWAYS use OpenRouter. The user picks hybrid per
# job in the UI; these say WHERE local is + the toggle's default state. Empty
# LOCAL_BASE_URL = hybrid disabled (falls back to OpenRouter).
LOCAL_BASE_URL   = os.getenv("LOCAL_BASE_URL", "")
LOCAL_API_KEY    = os.getenv("LOCAL_API_KEY", "local")
LOCAL_TEXT_MODEL = os.getenv("LOCAL_TEXT_MODEL", "")
HYBRID_DEFAULT   = os.getenv("HYBRID_DEFAULT", "false").strip().lower() in ("1", "true", "yes")

# -- Pipeline -------------------------------------------------------
PDF_DPI       = int(os.getenv("PDF_DPI",       "100"))
MAX_PAGES     = int(os.getenv("MAX_PAGES",     "60"))
MAX_DIMENSION = int(os.getenv("MAX_DIMENSION", "2400"))  # px cap on the long edge
LLM_TIMEOUT   = int(os.getenv("LLM_TIMEOUT",   "180"))   # seconds per call
EXTRACT_MAX_TOKENS = int(os.getenv("EXTRACT_MAX_TOKENS", "16384"))
REASON_MAX_TOKENS  = int(os.getenv("REASON_MAX_TOKENS",  "4096"))

# -- QAQC stage knobs (Stages 0-1, 3, 6-11) -------------------------
# Token caps per stage. Most are single whole-set calls, so they need more
# headroom than a per-cluster reason call.
JURISDICTION_MAX_TOKENS  = int(os.getenv("JURISDICTION_MAX_TOKENS",  "4096"))
SHEET_INDEX_MAX_TOKENS   = int(os.getenv("SHEET_INDEX_MAX_TOKENS",   "16384"))
NORMALIZE_MAX_TOKENS     = int(os.getenv("NORMALIZE_MAX_TOKENS",     "16384"))
QAQC_MAX_TOKENS          = int(os.getenv("QAQC_MAX_TOKENS",          "16384"))
CODE_MAX_TOKENS          = int(os.getenv("CODE_MAX_TOKENS",          "16384"))
CONSTRUCT_MAX_TOKENS     = int(os.getenv("CONSTRUCT_MAX_TOKENS",     "16384"))
VALIDATE_MAX_TOKENS      = int(os.getenv("VALIDATE_MAX_TOKENS",      "16384"))
RISK_MAX_TOKENS          = int(os.getenv("RISK_MAX_TOKENS",          "16384"))
RFI_MAX_TOKENS           = int(os.getenv("RFI_MAX_TOKENS",           "16384"))

# Stage 4 clustering engine: "llm" (semantic, fuzzy matches + catches more) or
# "deterministic" (clusterer.py, free/reproducible). Swap via env to A/B.
CLUSTERER          = os.getenv("CLUSTERER", "llm").strip().lower()
CLUSTER_MAX_TOKENS = int(os.getenv("CLUSTER_MAX_TOKENS", "16384"))
CLUSTER_MAX        = int(os.getenv("CLUSTER_MAX", "120"))

# Disk-backed LLM response cache for the testing loop. When on, identical calls
# (same model/prompt/images/params) replay the saved response at zero API cost,
# so re-running a set only pays for stages whose input actually changed. Off by
# default so production never serves stale results. Clear: rm -rf the dir.
LLM_CACHE     = os.getenv("LLM_CACHE", "false").strip().lower() in ("1", "true", "yes")
LLM_CACHE_DIR = os.getenv("LLM_CACHE_DIR", os.path.join(_BASE_DIR, ".llm_cache"))

# Parallelism (ThreadPoolExecutor workers)
EXTRACT_CONCURRENCY = int(os.getenv("EXTRACT_CONCURRENCY", "4"))
REASON_CONCURRENCY  = int(os.getenv("REASON_CONCURRENCY",  "4"))
# Batched stages (normalization, per-sheet code/constructability) reuse this.
NORMALIZE_CONCURRENCY = int(os.getenv("NORMALIZE_CONCURRENCY", "4"))
CODE_CONCURRENCY      = int(os.getenv("CODE_CONCURRENCY",      "4"))
# Max assertions per code-review call. Code review batches sheets to keep each
# call small (avoids the truncation that zeroed out a whole-set call).
CODE_BATCH_SIZE       = int(os.getenv("CODE_BATCH_SIZE",       "80"))
CONSTRUCT_CONCURRENCY = int(os.getenv("CONSTRUCT_CONCURRENCY", "4"))
# Assertions per batch for the normalization stage.
NORMALIZE_BATCH_SIZE  = int(os.getenv("NORMALIZE_BATCH_SIZE",  "60"))

# -- App ------------------------------------------------------------
UPLOAD_DIR  = os.path.join(_BASE_DIR, "uploads")
OUTPUT_DIR  = os.path.join(_BASE_DIR, "outputs")
APP_TITLE   = "Conflict Checker"
APP_VERSION = "0.1.0"
# Public base URL used to build the "view results" link in notification
# emails. Set to whatever address users reach this server on (e.g. the
# Tailscale/LAN URL) so the link in the email actually resolves.
APP_BASE_URL = os.getenv("APP_BASE_URL", "http://localhost:8099")

# -- Email / SMTP (optional notification on completion) -------------
# If unset, the app still works; it just logs "SMTP not configured" and
# skips the email. Mirrors IronBid's graceful behavior.
SMTP_HOST     = os.getenv("SMTP_HOST",     "")
SMTP_PORT     = int(os.getenv("SMTP_PORT", "587"))
SMTP_USER     = os.getenv("SMTP_USER",     "")
SMTP_PASSWORD = os.getenv("SMTP_PASSWORD", "")
SMTP_FROM     = os.getenv("SMTP_FROM",     "")
SMTP_USE_TLS  = os.getenv("SMTP_USE_TLS",  "true").lower()  == "true"
SMTP_USE_SSL  = os.getenv("SMTP_USE_SSL",  "false").lower() == "true"