import os
import sys
import uuid
import hashlib
import re
from pathlib import Path
from qdrant_client.http import models
from dotenv import load_dotenv

# Ajouter le dossier Engine au path pour importer qdrant_store
# Dossier des scripts (backend/api)
SCRIPTS_DIR = Path(__file__).resolve().parent
# Racine du projet (darkmedia-x_studio)
ROOT_DIR = SCRIPTS_DIR.parent.parent
# Dossier Engine à la racine
ENGINE_DIR = ROOT_DIR / "engine"
sys.path.insert(0, str(ENGINE_DIR))

# Charger le .env depuis la racine
load_dotenv(ROOT_DIR / ".env")

try:
    from qdrant_store import _get_client, _get_embedding, init_collections
except ImportError:
    print("❌ Erreur: Impossible d'importer qdrant_store. Vérifiez les chemins.")
    sys.exit(1)

# Collection dédiée au codebase
COLLECTION_NAME = "codebase"

# Fichiers et dossiers à ignorer par défaut (si pas de .ragignore)
IGNORE_DIRS = {".git", "node_modules", "vendor", "brain", "assets", "tmp", "__pycache__", "dist", "build", ".claude", ".mcp", ".next", "venv", ".venv", "target"}
IGNORE_FILES = {"package-lock.json", "yarn.lock", "pnpm-lock.yaml", ".env"}
SUPPORTED_EXTENSIONS = {".py", ".js", ".ts", ".md", ".css", ".html", ".json", ".yml", ".yaml", ".ps1", ".bat", ".rs"}

def load_ragignore():
    ragignore_path = ROOT_DIR / ".ragignore"
    patterns = []
    if ragignore_path.exists():
        print(f"📖 Chargement des exclusions depuis {ragignore_path}...")
        with open(ragignore_path, "r", encoding="utf-8") as f:
            for line in f:
                line = line.strip()
                if line and not line.startswith("#"):
                    # Nettoyer le pattern (ex: target/ -> target)
                    p = line.rstrip("/").rstrip("\\")
                    patterns.append(p)
    return patterns

def is_ignored(path_str, rag_patterns):
    # Chemin relatif pour faciliter le matching
    rel_path = os.path.relpath(path_str, ROOT_DIR)
    parts = rel_path.split(os.sep)
    
    # 1. Vérifier les hardcoded IGNORE_DIRS/FILES (pour la compatibilité)
    for part in parts:
        if part in IGNORE_DIRS: return True
    if os.path.basename(path_str) in IGNORE_FILES: return True
    
    # 2. Vérifier les patterns .ragignore
    for p in rag_patterns:
        # Simple matching: si le pattern est contenu dans le chemin ou match le début
        if p in parts or rel_path.startswith(p):
            return True
    return False

def init_codebase_collection():
    client = _get_client()
    try:
        client.get_collection(COLLECTION_NAME)
    except Exception:
        print(f"📦 Création de la collection '{COLLECTION_NAME}'...")
        client.create_collection(
            collection_name=COLLECTION_NAME,
            vectors_config=models.VectorParams(size=384, distance=models.Distance.COSINE),
        )

def chunk_text(text, filename, chunk_size=1200, overlap=150):
    # Split by double newline for better logical blocks
    blocks = re.split(r'\n\s*\n', text)
    chunks = []
    current_chunk = f"File: {filename}\n\n"
    
    for block in blocks:
        if len(current_chunk) + len(block) < chunk_size:
            current_chunk += block + "\n\n"
        else:
            chunks.append(current_chunk.strip())
            current_chunk = f"File: {filename}\n\n" + block + "\n\n"
            
    if current_chunk:
        chunks.append(current_chunk.strip())
    
    # Fallback for very long blocks
    final_chunks = []
    for c in chunks:
        if len(c) > chunk_size * 1.5:
            # Hard split
            for i in range(0, len(c), chunk_size - overlap):
                final_chunks.append(c[i:i + chunk_size])
        else:
            final_chunks.append(c)
            
    return final_chunks

import json

CACHE_FILE = ROOT_DIR / ".indexing_cache.json"

def load_cache():
    if CACHE_FILE.exists():
        try:
            with open(CACHE_FILE, "r") as f:
                return json.load(f)
        except Exception:
            return {}
    return {}

def save_cache(cache):
    try:
        with open(CACHE_FILE, "w") as f:
            json.dump(cache, f, indent=2)
    except Exception as e:
        print(f"⚠️ Erreur sauvegarde cache: {e}")

def get_file_hash(content):
    return hashlib.sha256(content.encode("utf-8")).hexdigest()

def index_codebase():
    client = _get_client()
    init_codebase_collection()
    
    rag_patterns = load_ragignore()
    cache = load_cache()
    new_cache = {}
    
    print(f"🔍 Indexation incrémentale du codebase ({len(rag_patterns)} exclusions)")
    
    points = []
    file_count = 0
    skipped_count = 0
    chunk_count = 0
    
    for root, dirs, files in os.walk(ROOT_DIR):
        # 1. Filtrage des dossiers
        dirs[:] = [d for d in dirs if not is_ignored(os.path.join(root, d), rag_patterns)]
        
        for file in files:
            file_path = os.path.join(root, file)
            if is_ignored(file_path, rag_patterns):
                continue
                
            ext = os.path.splitext(file)[1].lower()
            if ext not in SUPPORTED_EXTENSIONS:
                continue
                
            rel_path = str(Path(root).relative_to(ROOT_DIR))
            full_rel_path = os.path.join(rel_path, file)
            
            try:
                content = Path(file_path).read_text(encoding="utf-8", errors="ignore")
            except Exception:
                continue
                
            if not content.strip():
                continue

            # Vérifier le hash pour l'incrémental
            file_hash = get_file_hash(content)
            if cache.get(full_rel_path) == file_hash:
                new_cache[full_rel_path] = file_hash
                skipped_count += 1
                continue
                
            file_count += 1
            new_cache[full_rel_path] = file_hash
            chunks = chunk_text(content, full_rel_path)
            
            for i, chunk in enumerate(chunks):
                chunk_id = f"{full_rel_path}_{i}"
                q_id = str(uuid.uuid5(uuid.NAMESPACE_DNS, chunk_id))
                vector = _get_embedding(chunk)
                
                payload = {
                    "path": full_rel_path,
                    "filename": file,
                    "extension": ext,
                    "chunk_index": i,
                    "total_chunks": len(chunks),
                    "content": chunk,
                    "type": "codebase"
                }
                
                points.append(models.PointStruct(id=q_id, vector=vector, payload=payload))
                chunk_count += 1
                
                if len(points) >= 50:
                    client.upsert(collection_name=COLLECTION_NAME, points=points)
                    points = []
                    print(f"   🚀 [{file_count:3d}] {full_rel_path} ({chunk_count} chunks)")

    if points:
        client.upsert(collection_name=COLLECTION_NAME, points=points)
        
    save_cache(new_cache)
    print(f"\n✨ Indexation terminée !")
    print(f"   📂 Fichiers nouveaux/modifiés : {file_count}")
    print(f"   ⏭️  Fichiers déjà à jour : {skipped_count}")
    print(f"   🧩 Total chunks indexés : {chunk_count}")

if __name__ == "__main__":
    index_codebase()