#!/usr/bin/env python3
"""
Direct Database Import Script

Imports Claude conversation contexts directly to the database,
bypassing the API. Useful when the API is on a remote server
but the conversation files are local.

Usage:
    python scripts/direct_db_import.py --folder "C:\\Users\\MikeSwanson\\.claude\\projects" --dry-run
    python scripts/direct_db_import.py --folder "C:\\Users\\MikeSwanson\\.claude\\projects" --execute
"""

import argparse
import sys
from pathlib import Path

# Add parent directory to path for imports
sys.path.insert(0, str(Path(__file__).parent.parent))

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from api.utils.conversation_parser import (
    extract_context_from_conversation,
    parse_jsonl_conversation,
    scan_folder_for_conversations,
)
from api.models.conversation_context import ConversationContext
from api.schemas.conversation_context import ConversationContextCreate
import os
from dotenv import load_dotenv


def get_database_url():
    """Get database URL from environment."""
    # Load from .env file
    env_path = Path(__file__).parent.parent / ".env"
    if env_path.exists():
        load_dotenv(env_path)

    db_url = os.getenv("DATABASE_URL")
    if not db_url:
        print("[ERROR] DATABASE_URL not found in .env file")
        sys.exit(1)

    print(f"[OK] Database: {db_url.split('@')[1] if '@' in db_url else 'configured'}")
    return db_url


def import_conversations(folder_path: str, dry_run: bool = True, project_id: str = None):
    """
    Import conversations directly to database.

    Args:
        folder_path: Path to folder containing .jsonl files
        dry_run: If True, preview without saving
        project_id: Optional project ID to associate contexts with
    """
    print("\n" + "=" * 70)
    print("DIRECT DATABASE IMPORT")
    print("=" * 70)
    print(f"Mode: {'DRY RUN (preview only)' if dry_run else 'EXECUTE (will save to database)'}")
    print(f"Folder: {folder_path}")
    print("")

    # Results tracking
    result = {
        "files_scanned": 0,
        "files_processed": 0,
        "contexts_created": 0,
        "errors": [],
        "contexts_preview": [],
        "contexts_data": [],  # Store full context data for database insert
    }

    # Step 1: Scan for conversation files
    print("[1/3] Scanning folder for conversation files...")
    try:
        conversation_files = scan_folder_for_conversations(folder_path)
        result["files_scanned"] = len(conversation_files)
        print(f"   Found {len(conversation_files)} .jsonl files")
    except Exception as e:
        print(f"[ERROR] Failed to scan folder: {e}")
        return result

    if not conversation_files:
        print("[WARNING] No conversation files found")
        return result

    # Step 2: Parse conversations
    print(f"\n[2/3] Parsing conversations...")
    print(f"[DEBUG] dry_run = {dry_run}")

    for file_path in conversation_files:
        try:
            # Parse conversation
            conversation = parse_jsonl_conversation(file_path)

            if not conversation.get("messages"):
                result["errors"].append({
                    "file": file_path,
                    "error": "No messages found"
                })
                continue

            # Extract context
            raw_context = extract_context_from_conversation(conversation)

            # Transform to database format
            metadata = raw_context.get("raw_metadata", {})
            summary_obj = raw_context.get("summary", {})

            # Get title from metadata or generate one
            title = metadata.get("title") or metadata.get("conversation_id") or f"Conversation"

            # Get dense summary from summary object
            dense_summary = summary_obj.get("summary") or summary_obj.get("dense_summary") or "No summary available"

            # Transform context to database format
            import json

            # Convert decisions and tags to JSON strings
            decisions = raw_context.get("decisions", [])
            key_decisions_json = json.dumps(decisions) if decisions else None

            tags = raw_context.get("tags", [])
            tags_json = json.dumps(tags) if tags else None

            context = {
                "project_id": project_id,
                "session_id": None,
                "machine_id": None,
                "context_type": raw_context.get("category", "general_context"),
                "title": title,
                "dense_summary": dense_summary,
                "key_decisions": key_decisions_json,
                "current_state": None,
                "tags": tags_json,
                "relevance_score": raw_context.get("metrics", {}).get("quality_score", 5.0),
            }

            result["files_processed"] += 1
            result["contexts_preview"].append({
                "title": context["title"],
                "type": context["context_type"],
                "message_count": len(conversation["messages"]),
                "tags": context.get("tags", []),
                "relevance_score": context.get("relevance_score", 0.0),
            })

            # Store full context data for database insert
            if not dry_run:
                result["contexts_data"].append(context)
                print(f"   [DEBUG] Stored context: {context['title'][:50]}")

        except Exception as e:
            print(f"[DEBUG] Exception for {Path(file_path).name}: {e}")
            result["errors"].append({
                "file": file_path,
                "error": str(e)
            })

    print(f"   Processed {result['files_processed']} files successfully")
    print(f"   Errors: {len(result['errors'])}")

    # Step 3: Save to database (if execute mode)
    if not dry_run:
        print(f"\n[3/3] Saving to database...")

        try:
            # Create database connection
            db_url = get_database_url()
            engine = create_engine(db_url)
            SessionLocal = sessionmaker(bind=engine)
            db = SessionLocal()

            # Save each context
            saved_count = 0
            for context_data in result["contexts_data"]:
                try:
                    # Create context object
                    context_obj = ConversationContext(
                        project_id=context_data.get("project_id"),
                        session_id=context_data.get("session_id"),
                        machine_id=context_data.get("machine_id"),
                        context_type=context_data["context_type"],
                        title=context_data["title"],
                        dense_summary=context_data["dense_summary"],
                        key_decisions=context_data.get("key_decisions"),
                        current_state=context_data.get("current_state"),
                        tags=context_data.get("tags", []),
                        relevance_score=context_data.get("relevance_score", 5.0),
                    )

                    db.add(context_obj)
                    saved_count += 1

                except Exception as e:
                    print(f"[WARNING] Failed to save context '{context_data.get('title', 'Unknown')}': {e}")

            # Commit all changes
            db.commit()
            db.close()

            result["contexts_created"] = saved_count
            print(f"   Saved {saved_count} contexts to database")

        except Exception as e:
            print(f"[ERROR] Database error: {e}")
            return result
    else:
        print(f"\n[3/3] Skipping database save (dry run mode)")

    # Display results
    print("\n" + "=" * 70)
    print("IMPORT RESULTS")
    print("=" * 70)
    print(f"\nFiles scanned:     {result['files_scanned']}")
    print(f"Files processed:   {result['files_processed']}")
    print(f"Contexts created:  {result['contexts_created'] if not dry_run else 'N/A (dry run)'}")
    print(f"Errors:            {len(result['errors'])}")

    # Show preview of contexts
    if result["contexts_preview"]:
        print(f"\n[PREVIEW] First 5 contexts:")
        for i, ctx in enumerate(result["contexts_preview"][:5], 1):
            print(f"\n   {i}. {ctx['title']}")
            print(f"      Type: {ctx['type']}")
            print(f"      Messages: {ctx['message_count']}")
            print(f"      Tags: {', '.join(ctx.get('tags', [])[:5])}")
            print(f"      Relevance: {ctx.get('relevance_score', 0.0):.1f}/10.0")

    # Show errors
    if result["errors"]:
        print(f"\n[ERRORS] First 5 errors:")
        for i, err in enumerate(result["errors"][:5], 1):
            print(f"\n   {i}. File: {Path(err['file']).name}")
            print(f"      Error: {err['error']}")

        if len(result["errors"]) > 5:
            print(f"\n   ... and {len(result['errors']) - 5} more errors")

    print("\n" + "=" * 70)

    return result


def main():
    """Main entry point."""
    parser = argparse.ArgumentParser(
        description="Import Claude conversations directly to database (bypasses API)"
    )

    parser.add_argument(
        "--folder",
        required=True,
        help="Path to folder containing .jsonl conversation files"
    )

    mode_group = parser.add_mutually_exclusive_group(required=True)
    mode_group.add_argument(
        "--dry-run",
        action="store_true",
        help="Preview import without saving"
    )
    mode_group.add_argument(
        "--execute",
        action="store_true",
        help="Execute import and save to database"
    )

    parser.add_argument(
        "--project-id",
        help="Associate all contexts with this project ID"
    )

    args = parser.parse_args()

    # Validate folder
    folder_path = Path(args.folder)
    if not folder_path.exists():
        print(f"[ERROR] Folder does not exist: {folder_path}")
        sys.exit(1)

    # Run import
    try:
        result = import_conversations(
            folder_path=str(folder_path),
            dry_run=args.dry_run,
            project_id=args.project_id
        )

        # Success message
        if args.dry_run:
            print("\n[SUCCESS] Dry run completed")
            print("   Run with --execute to save to database")
        else:
            print(f"\n[SUCCESS] Import completed")
            print(f"   Created {result['contexts_created']} contexts")

        sys.exit(0)

    except Exception as e:
        print(f"\n[ERROR] Import failed: {e}")
        import traceback
        traceback.print_exc()
        sys.exit(1)


if __name__ == "__main__":
    main()