"""
Temporal Graph API Router
Provides endpoints for temporal force-directed graph visualization.
"""

import logging
from typing import List, Dict, Any
from fastapi import APIRouter, Depends, HTTPException
from sqlalchemy.orm import Session

from backend.dependencies import get_db
from backend.database.utils import get_temporal_windows_by_trace_id, get_trace
from backend.database.models import KnowledgeGraph

logger = logging.getLogger(__name__)

router = APIRouter(prefix="/api", tags=["temporal-graphs"])


@router.get("/temporal-graph/{trace_id}")
async def get_temporal_graph_data(trace_id: str, processing_run_id: str = None, db: Session = Depends(get_db)):
    """
    Get temporal graph data for a specific trace.
    Returns all knowledge graph windows in sequence for animation, plus the full/merged version.
    
    Args:
        trace_id: ID of the trace
        processing_run_id: Optional processing run ID to filter by specific run
    """
    try:
        logger.info(f"Attempting to load temporal graph for trace_id: {trace_id}")
        if processing_run_id:
            logger.info(f"Filtering by processing_run_id: {processing_run_id}")
        
        # Get temporal windows data (includes windowed KGs and full KG)
        temporal_data = get_temporal_windows_by_trace_id(db, trace_id, processing_run_id)
        
        if not temporal_data["trace_info"]:
            logger.warning(f"Trace with ID {trace_id} not found in database")
            raise HTTPException(status_code=404, detail=f"Trace with ID {trace_id} not found")
        
        windows = temporal_data["windows"]
        full_kg = temporal_data["full_kg"]
        trace_info = temporal_data["trace_info"]
        
        logger.info(f"Found trace: {trace_info['trace_id']} (title: {trace_info['title']})")
        logger.info(f"Found {len(windows)} windows and {'1' if full_kg else '0'} full KG for trace {trace_id}")
        
        if not windows:
            raise HTTPException(
                status_code=404, 
                detail=f"No knowledge graph windows found for trace {trace_id}" + 
                       (f" with processing_run_id {processing_run_id}" if processing_run_id else "")
            )
        
        # Check if we have enough windows for temporal visualization
        if len(windows) < 2:
            raise HTTPException(
                status_code=400,
                detail=f"Trace {trace_id} has insufficient windows for temporal visualization (found {len(windows)}, need at least 2)"
            )
        
        # Build response data for windowed KGs
        windows_data = []
        for window in windows:
            graph_data = window.graph_data or {}
            
            window_info = {
                "window_index": window.window_index,
                "entities": graph_data.get("entities", []),
                "relations": graph_data.get("relations", []),
                "metadata": {
                    "entity_count": window.entity_count,
                    "relation_count": window.relation_count,
                    "window_start_char": window.window_start_char,
                    "window_end_char": window.window_end_char,
                    "creation_timestamp": window.creation_timestamp.isoformat() if window.creation_timestamp else None,
                    "status": window.status,
                    "filename": window.filename
                }
            }
            windows_data.append(window_info)
        
        # Build response data for full KG (if available)
        full_kg_data = None
        if full_kg:
            graph_data = full_kg.graph_data or {}
            full_kg_data = {
                "entities": graph_data.get("entities", []),
                "relations": graph_data.get("relations", []),
                "metadata": {
                    "entity_count": full_kg.entity_count,
                    "relation_count": full_kg.relation_count,
                    "window_total": full_kg.window_total,
                    "creation_timestamp": full_kg.creation_timestamp.isoformat() if full_kg.creation_timestamp else None,
                    "status": full_kg.status,
                    "filename": full_kg.filename
                }
            }
        
        # Sort windowed data by window_index to ensure proper temporal order
        windows_data.sort(key=lambda x: x["window_index"])
        
        response = {
            "trace_id": trace_id,
            "trace_title": trace_info["title"] or f"Trace {trace_id[:8]}",
            "trace_description": trace_info["description"],
            "total_windows": len(windows_data),
            "windows": windows_data,
            "full_kg": full_kg_data,
            "has_full_version": full_kg is not None
        }
        
        logger.info(f"Returning temporal graph data for trace {trace_id} with {len(windows_data)} windows and {'full version' if full_kg else 'no full version'}")
        return response
        
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error getting temporal graph data for trace {trace_id}: {str(e)}")
        raise HTTPException(status_code=500, detail="An internal error occurred")


@router.get("/traces-with-windows")
async def get_traces_with_windows(db: Session = Depends(get_db)):
    """
    Get all traces that have multiple knowledge graph windows suitable for temporal visualization.
    Includes both traces with records and orphaned KGs (KGs with trace_id but no trace record).
    """
    try:
        from sqlalchemy import func
        from backend.database.models import Trace, KnowledgeGraph
        
        # Query traces that have multiple knowledge graph windows (with trace records)
        traces_with_records = (
            db.query(
                Trace.trace_id,
                Trace.title,
                Trace.description,
                Trace.upload_timestamp,
                func.count(KnowledgeGraph.id).label('window_count')
            )
            .join(KnowledgeGraph, Trace.trace_id == KnowledgeGraph.trace_id)
            .group_by(Trace.trace_id, Trace.title, Trace.description, Trace.upload_timestamp)
            .having(func.count(KnowledgeGraph.id) >= 2)
            .order_by(Trace.upload_timestamp.desc())
            .all()
        )
        
        # Query orphaned KGs (KGs with trace_id but no trace record) that have multiple windows
        orphaned_kg_traces = (
            db.query(
                KnowledgeGraph.trace_id,
                func.count(KnowledgeGraph.id).label('window_count'),
                func.min(KnowledgeGraph.creation_timestamp).label('earliest_creation')
            )
            .outerjoin(Trace, KnowledgeGraph.trace_id == Trace.trace_id)
            .filter(Trace.trace_id.is_(None))  # No trace record exists
            .filter(KnowledgeGraph.trace_id.isnot(None))  # But KG has a trace_id
            .group_by(KnowledgeGraph.trace_id)
            .having(func.count(KnowledgeGraph.id) >= 2)
            .order_by(func.min(KnowledgeGraph.creation_timestamp).desc())
            .all()
        )
        
        result = []
        
        # Add traces with records
        for trace_info in traces_with_records:
            result.append({
                "trace_id": trace_info.trace_id,
                "title": trace_info.title or f"Trace {trace_info.trace_id[:8]}...",
                "description": trace_info.description,
                "upload_timestamp": trace_info.upload_timestamp.isoformat() if trace_info.upload_timestamp else None,
                "window_count": trace_info.window_count,
                "has_trace_record": True
            })
        
        # Add orphaned KG traces
        for kg_trace_info in orphaned_kg_traces:
            result.append({
                "trace_id": kg_trace_info.trace_id,
                "title": f"Trace {kg_trace_info.trace_id[:8]}...",
                "description": "Knowledge graphs exist but no trace record found",
                "upload_timestamp": kg_trace_info.earliest_creation.isoformat() if kg_trace_info.earliest_creation else None,
                "window_count": kg_trace_info.window_count,
                "has_trace_record": False
            })
        
        # Sort by upload/creation timestamp
        result.sort(key=lambda x: x["upload_timestamp"] or "1970-01-01", reverse=True)
        
        logger.info(f"Found {len(traces_with_records)} traces with records and {len(orphaned_kg_traces)} orphaned KG traces")
        return {"traces": result}
        
    except Exception as e:
        logger.error(f"Error getting traces with windows: {str(e)}")
        raise HTTPException(status_code=500, detail="An internal error occurred")


@router.get("/trace/{trace_id}/processing-runs")
async def get_processing_runs_for_trace(trace_id: str, db: Session = Depends(get_db)):
    """
    Get all processing runs for a specific trace.
    Returns information about each processing run including run ID, timestamp, and window count.
    """
    try:
        from sqlalchemy import func
        from backend.database.models import KnowledgeGraph
        
        # Query all processing runs for this trace
        processing_runs = (
            db.query(
                KnowledgeGraph.processing_run_id,
                func.count(KnowledgeGraph.id).label('kg_count'),
                func.min(KnowledgeGraph.creation_timestamp).label('earliest_creation'),
                func.max(KnowledgeGraph.creation_timestamp).label('latest_creation'),
                func.sum(KnowledgeGraph.entity_count).label('total_entities'),
                func.sum(KnowledgeGraph.relation_count).label('total_relations')
            )
            .filter(KnowledgeGraph.trace_id == trace_id)
            .filter(KnowledgeGraph.processing_run_id.isnot(None))
            .group_by(KnowledgeGraph.processing_run_id)
            .order_by(func.min(KnowledgeGraph.creation_timestamp).desc())
            .all()
        )
        
        # Also check for KGs without processing_run_id (legacy)
        legacy_kgs = (
            db.query(func.count(KnowledgeGraph.id).label('kg_count'))
            .filter(KnowledgeGraph.trace_id == trace_id)
            .filter(KnowledgeGraph.processing_run_id.is_(None))
            .first()
        )
        
        result = []
        
        # Add processing runs
        for run_info in processing_runs:
            result.append({
                "processing_run_id": run_info.processing_run_id,
                "kg_count": run_info.kg_count,
                "earliest_creation": run_info.earliest_creation.isoformat() if run_info.earliest_creation else None,
                "latest_creation": run_info.latest_creation.isoformat() if run_info.latest_creation else None,
                "total_entities": run_info.total_entities or 0,
                "total_relations": run_info.total_relations or 0,
                "is_legacy": False
            })
        
        # Add legacy KGs if any exist
        if legacy_kgs and legacy_kgs.kg_count > 0:
            result.append({
                "processing_run_id": None,
                "kg_count": legacy_kgs.kg_count,
                "earliest_creation": None,
                "latest_creation": None,
                "total_entities": 0,
                "total_relations": 0,
                "is_legacy": True
            })
        
        logger.info(f"Found {len(processing_runs)} processing runs for trace {trace_id}")
        return {"processing_runs": result}
        
    except Exception as e:
        logger.error(f"Error getting processing runs for trace {trace_id}: {str(e)}")
        raise HTTPException(status_code=500, detail="An internal error occurred")