Atomizer/atomizer-dashboard/backend/api/routes/optimization.py

"""
Optimization API endpoints
Handles study status, history retrieval, and control operations
"""

from fastapi import APIRouter, HTTPException, UploadFile, File, Form
from fastapi.responses import JSONResponse, FileResponse
from pydantic import BaseModel
from pathlib import Path
from typing import List, Dict, Optional
import json
import sys
import sqlite3
import shutil
import subprocess
import psutil
import signal
from datetime import datetime

# Add project root to path
sys.path.append(str(Path(__file__).parent.parent.parent.parent.parent))

router = APIRouter()

# Base studies directory
STUDIES_DIR = Path(__file__).parent.parent.parent.parent.parent / "studies"


def get_results_dir(study_dir: Path) -> Path:
    """Get the results directory for a study, supporting both 2_results and 3_results."""
    results_dir = study_dir / "2_results"
    if not results_dir.exists():
        results_dir = study_dir / "3_results"
    return results_dir


def is_optimization_running(study_id: str) -> bool:
    """Check if an optimization process is currently running for a study.

    Looks for Python processes running run_optimization.py with the study_id in the command line.
    """
    study_dir = STUDIES_DIR / study_id

    for proc in psutil.process_iter(['pid', 'name', 'cmdline', 'cwd']):
        try:
            cmdline = proc.info.get('cmdline') or []
            cmdline_str = ' '.join(cmdline) if cmdline else ''

            # Check if this is a Python process running run_optimization.py for this study
            if 'python' in cmdline_str.lower() and 'run_optimization' in cmdline_str:
                if study_id in cmdline_str or str(study_dir) in cmdline_str:
                    return True
        except (psutil.NoSuchProcess, psutil.AccessDenied):
            continue

    return False


def get_accurate_study_status(study_id: str, trial_count: int, total_trials: int, has_db: bool) -> str:
    """Determine accurate study status based on multiple factors.

    Status can be:
    - not_started: No database or 0 trials
    - running: Active process found
    - paused: Has trials but no active process and not completed
    - completed: Reached trial target
    - failed: Has error indicators (future enhancement)

    Args:
        study_id: The study identifier
        trial_count: Number of completed trials
        total_trials: Target number of trials from config
        has_db: Whether the study database exists

    Returns:
        Status string: "not_started", "running", "paused", or "completed"
    """
    # No database or no trials = not started
    if not has_db or trial_count == 0:
        return "not_started"

    # Check if we've reached the target
    if trial_count >= total_trials:
        return "completed"

    # Check if process is actively running
    if is_optimization_running(study_id):
        return "running"

    # Has trials but not running and not complete = paused
    return "paused"


@router.get("/studies")
async def list_studies():
    """List all available optimization studies"""
    try:
        studies = []
        if not STUDIES_DIR.exists():
            return {"studies": []}

        for study_dir in STUDIES_DIR.iterdir():
            if not study_dir.is_dir():
                continue

            # Look for optimization config (check multiple locations)
            config_file = study_dir / "optimization_config.json"
            if not config_file.exists():
                config_file = study_dir / "1_setup" / "optimization_config.json"
            if not config_file.exists():
                continue

            # Load config
            with open(config_file) as f:
                config = json.load(f)

            # Check if results directory exists (support both 2_results and 3_results)
            results_dir = study_dir / "2_results"
            if not results_dir.exists():
                results_dir = study_dir / "3_results"

            # Check for Optuna database (Protocol 10) or JSON history (other protocols)
            study_db = results_dir / "study.db"
            history_file = results_dir / "optimization_history_incremental.json"

            trial_count = 0
            best_value = None
            has_db = False

            # Protocol 10: Read from Optuna SQLite database
            if study_db.exists():
                has_db = True
                try:
                    # Use timeout to avoid blocking on locked databases
                    conn = sqlite3.connect(str(study_db), timeout=2.0)
                    cursor = conn.cursor()

                    # Get trial count and status
                    cursor.execute("SELECT COUNT(*) FROM trials WHERE state = 'COMPLETE'")
                    trial_count = cursor.fetchone()[0]

                    # Get best trial (for single-objective, or first objective for multi-objective)
                    if trial_count > 0:
                        cursor.execute("""
                            SELECT value FROM trial_values
                            WHERE trial_id IN (
                                SELECT trial_id FROM trials WHERE state = 'COMPLETE'
                            )
                            ORDER BY value ASC
                            LIMIT 1
                        """)
                        result = cursor.fetchone()
                        if result:
                            best_value = result[0]

                    conn.close()

                except Exception as e:
                    print(f"Warning: Failed to read Optuna database for {study_dir.name}: {e}")

            # Legacy: Read from JSON history
            elif history_file.exists():
                has_db = True
                with open(history_file) as f:
                    history = json.load(f)
                    trial_count = len(history)
                    if history:
                        # Find best trial
                        best_trial = min(history, key=lambda x: x['objective'])
                        best_value = best_trial['objective']

            # Get total trials from config (supports both formats)
            total_trials = (
                config.get('optimization_settings', {}).get('n_trials') or
                config.get('trials', {}).get('n_trials', 50)
            )

            # Get accurate status using process detection
            status = get_accurate_study_status(study_dir.name, trial_count, total_trials, has_db)

            # Get creation date from directory or config modification time
            created_at = None
            try:
                # First try to get from database (most accurate)
                if study_db.exists():
                    created_at = datetime.fromtimestamp(study_db.stat().st_mtime).isoformat()
                elif config_file.exists():
                    created_at = datetime.fromtimestamp(config_file.stat().st_mtime).isoformat()
                else:
                    created_at = datetime.fromtimestamp(study_dir.stat().st_ctime).isoformat()
            except:
                created_at = None

            # Get last modified time
            last_modified = None
            try:
                if study_db.exists():
                    last_modified = datetime.fromtimestamp(study_db.stat().st_mtime).isoformat()
                elif history_file.exists():
                    last_modified = datetime.fromtimestamp(history_file.stat().st_mtime).isoformat()
            except:
                last_modified = None

            studies.append({
                "id": study_dir.name,
                "name": study_dir.name.replace("_", " ").title(),
                "status": status,
                "progress": {
                    "current": trial_count,
                    "total": total_trials
                },
                "best_value": best_value,
                "target": config.get('target', {}).get('value'),
                "path": str(study_dir),
                "created_at": created_at,
                "last_modified": last_modified
            })

        return {"studies": studies}

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to list studies: {str(e)}")

@router.get("/studies/{study_id}/status")
async def get_study_status(study_id: str):
    """Get detailed status of a specific study"""
    try:
        study_dir = STUDIES_DIR / study_id
        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        # Load config (check multiple locations)
        config_file = study_dir / "optimization_config.json"
        if not config_file.exists():
            config_file = study_dir / "1_setup" / "optimization_config.json"

        with open(config_file) as f:
            config = json.load(f)

        # Check for results (support both 2_results and 3_results)
        results_dir = get_results_dir(study_dir)
        study_db = results_dir / "study.db"
        history_file = results_dir / "optimization_history_incremental.json"

        # Protocol 10: Read from Optuna database
        if study_db.exists():
            conn = sqlite3.connect(str(study_db))
            cursor = conn.cursor()

            # Get trial counts by state
            cursor.execute("SELECT COUNT(*) FROM trials WHERE state = 'COMPLETE'")
            trial_count = cursor.fetchone()[0]

            cursor.execute("SELECT COUNT(*) FROM trials WHERE state = 'PRUNED'")
            pruned_count = cursor.fetchone()[0]

            # Get best trial (first objective for multi-objective)
            best_trial = None
            if trial_count > 0:
                cursor.execute("""
                    SELECT t.trial_id, t.number, tv.value
                    FROM trials t
                    JOIN trial_values tv ON t.trial_id = tv.trial_id
                    WHERE t.state = 'COMPLETE'
                    ORDER BY tv.value ASC
                    LIMIT 1
                """)
                result = cursor.fetchone()
                if result:
                    trial_id, trial_number, best_value = result

                    # Get parameters for this trial
                    cursor.execute("""
                        SELECT param_name, param_value
                        FROM trial_params
                        WHERE trial_id = ?
                    """, (trial_id,))
                    params = {row[0]: row[1] for row in cursor.fetchall()}

                    best_trial = {
                        "trial_number": trial_number,
                        "objective": best_value,
                        "design_variables": params,
                        "results": {"first_frequency": best_value}
                    }

            conn.close()

            total_trials = config.get('optimization_settings', {}).get('n_trials', 50)
            status = get_accurate_study_status(study_id, trial_count, total_trials, True)

            return {
                "study_id": study_id,
                "status": status,
                "progress": {
                    "current": trial_count,
                    "total": total_trials,
                    "percentage": (trial_count / total_trials * 100) if total_trials > 0 else 0
                },
                "best_trial": best_trial,
                "pruned_trials": pruned_count,
                "config": config
            }

        # Legacy: Read from JSON history
        if not history_file.exists():
            return {
                "study_id": study_id,
                "status": "not_started",
                "progress": {"current": 0, "total": config.get('trials', {}).get('n_trials', 50)},
                "config": config
            }

        with open(history_file) as f:
            history = json.load(f)

        trial_count = len(history)
        total_trials = config.get('trials', {}).get('n_trials', 50)

        # Find best trial
        best_trial = None
        if history:
            best_trial = min(history, key=lambda x: x['objective'])

        # Check for pruning data
        pruning_file = results_dir / "pruning_history.json"
        pruned_count = 0
        if pruning_file.exists():
            with open(pruning_file) as f:
                pruning_history = json.load(f)
                pruned_count = len(pruning_history)

        status = "completed" if trial_count >= total_trials else "running"

        return {
            "study_id": study_id,
            "status": status,
            "progress": {
                "current": trial_count,
                "total": total_trials,
                "percentage": (trial_count / total_trials * 100) if total_trials > 0 else 0
            },
            "best_trial": best_trial,
            "pruned_trials": pruned_count,
            "config": config
        }

    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to get study status: {str(e)}")

@router.get("/studies/{study_id}/history")
async def get_optimization_history(study_id: str, limit: Optional[int] = None):
    """Get optimization history (all trials)"""
    try:
        study_dir = STUDIES_DIR / study_id
        results_dir = get_results_dir(study_dir)
        study_db = results_dir / "study.db"
        history_file = results_dir / "optimization_history_incremental.json"

        # Protocol 10: Read from Optuna database
        if study_db.exists():
            conn = sqlite3.connect(str(study_db))
            cursor = conn.cursor()

            # Get all completed trials FROM ALL STUDIES in the database
            # This handles adaptive optimizations that create multiple Optuna studies
            # (e.g., v11_fea for FEA trials, v11_iter1_nn for NN trials, etc.)
            cursor.execute("""
                SELECT t.trial_id, t.number, t.datetime_start, t.datetime_complete, s.study_name
                FROM trials t
                JOIN studies s ON t.study_id = s.study_id
                WHERE t.state = 'COMPLETE'
                ORDER BY t.datetime_start DESC
            """ + (f" LIMIT {limit}" if limit else ""))

            trial_rows = cursor.fetchall()

            trials = []
            for trial_id, trial_num, start_time, end_time, study_name in trial_rows:
                # Get objectives for this trial
                cursor.execute("""
                    SELECT value
                    FROM trial_values
                    WHERE trial_id = ?
                    ORDER BY objective
                """, (trial_id,))
                values = [row[0] for row in cursor.fetchall()]

                # Get parameters for this trial
                cursor.execute("""
                    SELECT param_name, param_value
                    FROM trial_params
                    WHERE trial_id = ?
                """, (trial_id,))
                params = {}
                for param_name, param_value in cursor.fetchall():
                    try:
                        params[param_name] = float(param_value) if param_value is not None else None
                    except (ValueError, TypeError):
                        params[param_name] = param_value

                # Get user attributes (extracted results: mass, frequency, stress, displacement, etc.)
                cursor.execute("""
                    SELECT key, value_json
                    FROM trial_user_attributes
                    WHERE trial_id = ?
                """, (trial_id,))
                user_attrs = {}
                for key, value_json in cursor.fetchall():
                    try:
                        user_attrs[key] = json.loads(value_json)
                    except (ValueError, TypeError):
                        user_attrs[key] = value_json

                # Extract ALL numeric metrics from user_attrs for results
                # This ensures multi-objective studies show all Zernike metrics, RMS values, etc.
                results = {}
                excluded_keys = {"design_vars", "constraint_satisfied", "constraint_violations"}
                for key, val in user_attrs.items():
                    if key in excluded_keys:
                        continue
                    # Include numeric values and lists of numbers
                    if isinstance(val, (int, float)):
                        results[key] = val
                    elif isinstance(val, list) and len(val) > 0 and isinstance(val[0], (int, float)):
                        # For lists, store as-is (e.g., Zernike coefficients)
                        results[key] = val
                    elif key == "objectives" and isinstance(val, dict):
                        # Extract nested objectives dict (Zernike multi-objective studies)
                        for obj_key, obj_val in val.items():
                            if isinstance(obj_val, (int, float)):
                                results[obj_key] = obj_val
                # Fallback to first frequency from objectives if available
                if not results and len(values) > 0:
                    results["first_frequency"] = values[0]

                # CRITICAL: Extract design_vars from user_attrs if stored there
                # The optimization code does: trial.set_user_attr("design_vars", design_vars)
                design_vars_from_attrs = user_attrs.get("design_vars", {})

                # Merge with params (prefer user_attrs design_vars if available)
                final_design_vars = {**params, **design_vars_from_attrs} if design_vars_from_attrs else params

                # Extract source for FEA vs NN differentiation
                source = user_attrs.get("source", "FEA")  # Default to FEA for legacy studies

                # Use trial_id as unique identifier when multiple Optuna studies exist
                # This avoids trial number collisions between studies
                unique_trial_num = trial_id if study_name else trial_num

                trials.append({
                    "trial_number": unique_trial_num,
                    "objective": values[0] if len(values) > 0 else None,  # Primary objective
                    "objectives": values if len(values) > 1 else None,  # All objectives for multi-objective
                    "design_variables": final_design_vars,  # Use merged design vars
                    "results": results,
                    "user_attrs": user_attrs,  # Include all user attributes
                    "source": source,  # FEA or NN
                    "start_time": start_time,
                    "end_time": end_time,
                    "study_name": study_name  # Include for debugging
                })

            conn.close()
            return {"trials": trials}

        # Legacy: Read from JSON history
        if not history_file.exists():
            return {"trials": []}

        with open(history_file) as f:
            history = json.load(f)

        # Apply limit if specified
        if limit:
            history = history[-limit:]

        return {"trials": history}

    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to get history: {str(e)}")

@router.get("/studies/{study_id}/pruning")
async def get_pruning_history(study_id: str):
    """Get pruning diagnostics from Optuna database or legacy JSON file"""
    try:
        study_dir = STUDIES_DIR / study_id
        results_dir = get_results_dir(study_dir)
        study_db = results_dir / "study.db"
        pruning_file = results_dir / "pruning_history.json"

        # Protocol 10+: Read from Optuna database
        if study_db.exists():
            conn = sqlite3.connect(str(study_db))
            cursor = conn.cursor()

            # Get all pruned trials from Optuna database
            cursor.execute("""
                SELECT t.trial_id, t.number, t.datetime_start, t.datetime_complete
                FROM trials t
                WHERE t.state = 'PRUNED'
                ORDER BY t.number DESC
            """)
            pruned_rows = cursor.fetchall()

            pruned_trials = []
            for trial_id, trial_num, start_time, end_time in pruned_rows:
                # Get parameters for this trial
                cursor.execute("""
                    SELECT param_name, param_value
                    FROM trial_params
                    WHERE trial_id = ?
                """, (trial_id,))
                params = {row[0]: row[1] for row in cursor.fetchall()}

                # Get user attributes (may contain pruning cause)
                cursor.execute("""
                    SELECT key, value_json
                    FROM trial_user_attributes
                    WHERE trial_id = ?
                """, (trial_id,))
                user_attrs = {}
                for key, value_json in cursor.fetchall():
                    try:
                        user_attrs[key] = json.loads(value_json)
                    except (ValueError, TypeError):
                        user_attrs[key] = value_json

                pruned_trials.append({
                    "trial_number": trial_num,
                    "params": params,
                    "pruning_cause": user_attrs.get("pruning_cause", "Unknown"),
                    "start_time": start_time,
                    "end_time": end_time
                })

            conn.close()
            return {"pruned_trials": pruned_trials, "count": len(pruned_trials)}

        # Legacy: Read from JSON history
        if not pruning_file.exists():
            return {"pruned_trials": [], "count": 0}

        with open(pruning_file) as f:
            pruning_history = json.load(f)

        return {"pruned_trials": pruning_history, "count": len(pruning_history)}

    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to get pruning history: {str(e)}")

def _infer_objective_unit(objective: Dict) -> str:
    """Infer unit from objective name and description"""
    name = objective.get("name", "").lower()
    desc = objective.get("description", "").lower()

    # Common unit patterns
    if "frequency" in name or "hz" in desc:
        return "Hz"
    elif "stiffness" in name or "n/mm" in desc:
        return "N/mm"
    elif "mass" in name or "kg" in desc:
        return "kg"
    elif "stress" in name or "mpa" in desc or "pa" in desc:
        return "MPa"
    elif "displacement" in name or "mm" in desc:
        return "mm"
    elif "force" in name or "newton" in desc:
        return "N"
    elif "%" in desc or "percent" in desc:
        return "%"

    # Check if unit is explicitly mentioned in description (e.g., "(N/mm)")
    import re
    unit_match = re.search(r'\(([^)]+)\)', desc)
    if unit_match:
        return unit_match.group(1)

    return ""  # No unit found

@router.get("/studies/{study_id}/metadata")
async def get_study_metadata(study_id: str):
    """Read optimization_config.json for objectives, design vars, units (Protocol 13)"""
    try:
        study_dir = STUDIES_DIR / study_id
        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        # Load config (check multiple locations)
        config_file = study_dir / "optimization_config.json"
        if not config_file.exists():
            config_file = study_dir / "1_setup" / "optimization_config.json"

        if not config_file.exists():
            raise HTTPException(status_code=404, detail=f"Config file not found for study {study_id}")

        with open(config_file) as f:
            config = json.load(f)

        # Enhance objectives with inferred units if not present
        objectives = config.get("objectives", [])
        for obj in objectives:
            if "unit" not in obj or not obj["unit"]:
                obj["unit"] = _infer_objective_unit(obj)

        return {
            "objectives": objectives,
            "design_variables": config.get("design_variables", []),
            "constraints": config.get("constraints", []),
            "study_name": config.get("study_name", study_id),
            "description": config.get("description", "")
        }

    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to get study metadata: {str(e)}")

@router.get("/studies/{study_id}/optimizer-state")
async def get_optimizer_state(study_id: str):
    """Read realtime optimizer state from intelligent_optimizer/ (Protocol 13)"""
    try:
        study_dir = STUDIES_DIR / study_id
        results_dir = get_results_dir(study_dir)
        state_file = results_dir / "intelligent_optimizer" / "optimizer_state.json"

        if not state_file.exists():
            return {"available": False}

        with open(state_file) as f:
            state = json.load(f)

        return {"available": True, **state}

    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to get optimizer state: {str(e)}")

@router.get("/studies/{study_id}/pareto-front")
async def get_pareto_front(study_id: str):
    """Get Pareto-optimal solutions for multi-objective studies (Protocol 13)"""
    try:
        study_dir = STUDIES_DIR / study_id
        results_dir = get_results_dir(study_dir)
        study_db = results_dir / "study.db"

        if not study_db.exists():
            return {"is_multi_objective": False, "pareto_front": []}

        # Import optuna here to avoid loading it for all endpoints
        import optuna

        storage = optuna.storages.RDBStorage(f"sqlite:///{study_db}")
        study = optuna.load_study(study_name=study_id, storage=storage)

        # Check if multi-objective
        if len(study.directions) == 1:
            return {"is_multi_objective": False, "pareto_front": []}

        # Get Pareto front
        pareto_trials = study.best_trials

        return {
            "is_multi_objective": True,
            "pareto_front": [
                {
                    "trial_number": t.number,
                    "values": t.values,
                    "params": t.params,
                    "user_attrs": dict(t.user_attrs),
                    "constraint_satisfied": t.user_attrs.get("constraint_satisfied", True)
                }
                for t in pareto_trials
            ]
        }

    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to get Pareto front: {str(e)}")


@router.get("/studies/{study_id}/nn-pareto-front")
async def get_nn_pareto_front(study_id: str):
    """Get NN surrogate Pareto front from nn_pareto_front.json"""
    try:
        study_dir = STUDIES_DIR / study_id
        results_dir = get_results_dir(study_dir)
        nn_pareto_file = results_dir / "nn_pareto_front.json"

        if not nn_pareto_file.exists():
            return {"has_nn_results": False, "pareto_front": []}

        with open(nn_pareto_file) as f:
            nn_pareto = json.load(f)

        # Transform to match Trial interface format
        transformed = []
        for trial in nn_pareto:
            transformed.append({
                "trial_number": trial.get("trial_number"),
                "values": [trial.get("mass"), trial.get("frequency")],
                "params": trial.get("params", {}),
                "user_attrs": {
                    "source": "NN",
                    "feasible": trial.get("feasible", False),
                    "predicted_stress": trial.get("predicted_stress"),
                    "predicted_displacement": trial.get("predicted_displacement"),
                    "mass": trial.get("mass"),
                    "frequency": trial.get("frequency")
                },
                "constraint_satisfied": trial.get("feasible", False),
                "source": "NN"
            })

        return {
            "has_nn_results": True,
            "pareto_front": transformed,
            "count": len(transformed)
        }

    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to get NN Pareto front: {str(e)}")


@router.get("/studies/{study_id}/nn-state")
async def get_nn_optimization_state(study_id: str):
    """Get NN optimization state/summary from nn_optimization_state.json"""
    try:
        study_dir = STUDIES_DIR / study_id
        results_dir = get_results_dir(study_dir)
        nn_state_file = results_dir / "nn_optimization_state.json"

        if not nn_state_file.exists():
            return {"has_nn_state": False}

        with open(nn_state_file) as f:
            state = json.load(f)

        return {
            "has_nn_state": True,
            "total_fea_count": state.get("total_fea_count", 0),
            "total_nn_count": state.get("total_nn_count", 0),
            "pareto_front_size": state.get("pareto_front_size", 0),
            "best_mass": state.get("best_mass"),
            "best_frequency": state.get("best_frequency"),
            "timestamp": state.get("timestamp")
        }

    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to get NN state: {str(e)}")


@router.post("/studies")
async def create_study(
    config: str = Form(...),
    prt_file: Optional[UploadFile] = File(None),
    sim_file: Optional[UploadFile] = File(None),
    fem_file: Optional[UploadFile] = File(None)
):
    """
    Create a new optimization study
    Accepts:
    - config: JSON string with study configuration
    - prt_file: NX part file (optional if using existing study)
    - sim_file: NX simulation file (optional)
    - fem_file: NX FEM file (optional)
    """
    try:
        # Parse config
        config_data = json.loads(config)
        study_name = config_data.get("name") # Changed from study_name to name to match frontend

        if not study_name:
            raise HTTPException(status_code=400, detail="name is required in config")

        # Create study directory structure
        study_dir = STUDIES_DIR / study_name
        if study_dir.exists():
            raise HTTPException(status_code=400, detail=f"Study {study_name} already exists")

        setup_dir = study_dir / "1_setup"
        model_dir = setup_dir / "model"
        results_dir = study_dir / "2_results"

        setup_dir.mkdir(parents=True, exist_ok=True)
        model_dir.mkdir(parents=True, exist_ok=True)
        results_dir.mkdir(parents=True, exist_ok=True)

        # Save config file
        config_file = setup_dir / "optimization_config.json"
        with open(config_file, 'w') as f:
            json.dump(config_data, f, indent=2)

        # Save uploaded files
        files_saved = {}
        if prt_file:
            prt_path = model_dir / prt_file.filename
            with open(prt_path, 'wb') as f:
                content = await prt_file.read()
                f.write(content)
            files_saved['prt_file'] = str(prt_path)

        if sim_file:
            sim_path = model_dir / sim_file.filename
            with open(sim_path, 'wb') as f:
                content = await sim_file.read()
                f.write(content)
            files_saved['sim_file'] = str(sim_path)

        if fem_file:
            fem_path = model_dir / fem_file.filename
            with open(fem_path, 'wb') as f:
                content = await fem_file.read()
                f.write(content)
            files_saved['fem_file'] = str(fem_path)

        return JSONResponse(
            status_code=201,
            content={
                "status": "created",
                "study_id": study_name,
                "study_path": str(study_dir),
                "config_path": str(config_file),
                "files_saved": files_saved,
                "message": f"Study {study_name} created successfully. Ready to run optimization."
            }
        )

    except json.JSONDecodeError as e:
        raise HTTPException(status_code=400, detail=f"Invalid JSON in config: {str(e)}")
    except Exception as e:
        # Clean up on error
        if 'study_dir' in locals() and study_dir.exists():
            shutil.rmtree(study_dir)
        raise HTTPException(status_code=500, detail=f"Failed to create study: {str(e)}")

@router.post("/studies/{study_id}/convert-mesh")
async def convert_study_mesh(study_id: str):
    """
    Convert study mesh to GLTF for 3D visualization
    Creates a web-viewable 3D model with FEA results as vertex colors
    """
    try:
        study_dir = STUDIES_DIR / study_id
        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        # Import mesh converter
        sys.path.append(str(Path(__file__).parent.parent.parent.parent.parent))
        from optimization_engine.mesh_converter import convert_study_mesh

        # Convert mesh
        output_path = convert_study_mesh(study_dir)

        if output_path and output_path.exists():
            return {
                "status": "success",
                "gltf_path": str(output_path),
                "gltf_url": f"/api/optimization/studies/{study_id}/mesh/model.gltf",
                "metadata_url": f"/api/optimization/studies/{study_id}/mesh/model.json",
                "message": "Mesh converted successfully"
            }
        else:
            raise HTTPException(status_code=500, detail="Mesh conversion failed")

    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to convert mesh: {str(e)}")

@router.get("/studies/{study_id}/mesh/{filename}")
async def get_mesh_file(study_id: str, filename: str):
    """
    Serve GLTF mesh files and metadata
    Supports .gltf, .bin, and .json files
    """
    try:
        # Validate filename to prevent directory traversal
        if '..' in filename or '/' in filename or '\\' in filename:
            raise HTTPException(status_code=400, detail="Invalid filename")

        study_dir = STUDIES_DIR / study_id
        visualization_dir = study_dir / "3_visualization"

        file_path = visualization_dir / filename

        if not file_path.exists():
            raise HTTPException(status_code=404, detail=f"File {filename} not found")

        # Determine content type
        suffix = file_path.suffix.lower()
        content_types = {
            '.gltf': 'model/gltf+json',
            '.bin': 'application/octet-stream',
            '.json': 'application/json',
            '.glb': 'model/gltf-binary'
        }

        content_type = content_types.get(suffix, 'application/octet-stream')

        return FileResponse(
            path=str(file_path),
            media_type=content_type,
            filename=filename
        )

    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"File not found")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to serve mesh file: {str(e)}")

@router.get("/studies/{study_id}/optuna-url")
async def get_optuna_dashboard_url(study_id: str):
    """
    Get the Optuna dashboard URL for a specific study.
    Returns the URL to access the study in Optuna dashboard.

    The Optuna dashboard should be started with a relative path from the Atomizer root:
    sqlite:///studies/{study_id}/2_results/study.db
    """
    try:
        study_dir = STUDIES_DIR / study_id
        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        results_dir = get_results_dir(study_dir)
        study_db = results_dir / "study.db"

        if not study_db.exists():
            raise HTTPException(status_code=404, detail=f"No Optuna database found for study {study_id}")

        # Get the study name from the database (may differ from folder name)
        import optuna
        storage = optuna.storages.RDBStorage(f"sqlite:///{study_db}")
        studies = storage.get_all_studies()

        if not studies:
            raise HTTPException(status_code=404, detail=f"No Optuna study found in database for {study_id}")

        # Use the actual study name from the database
        optuna_study_name = studies[0].study_name

        # Return URL info for the frontend
        # The dashboard should be running on port 8081 with the correct database
        return {
            "study_id": study_id,
            "optuna_study_name": optuna_study_name,
            "database_path": f"studies/{study_id}/2_results/study.db",
            "dashboard_url": f"http://localhost:8081/dashboard/studies/{studies[0]._study_id}",
            "dashboard_base": "http://localhost:8081",
            "note": "Optuna dashboard must be started with: sqlite:///studies/{study_id}/2_results/study.db"
        }

    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to get Optuna URL: {str(e)}")

@router.post("/studies/{study_id}/generate-report")
async def generate_report(
    study_id: str,
    format: str = "markdown",
    include_llm_summary: bool = False
):
    """
    Generate an optimization report in the specified format

    Args:
        study_id: Study identifier
        format: Report format ('markdown', 'html', or 'pdf')
        include_llm_summary: Whether to include LLM-generated executive summary

    Returns:
        Information about the generated report including download URL
    """
    try:
        study_dir = STUDIES_DIR / study_id
        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        # Validate format
        valid_formats = ['markdown', 'md', 'html', 'pdf']
        if format.lower() not in valid_formats:
            raise HTTPException(status_code=400, detail=f"Invalid format. Must be one of: {', '.join(valid_formats)}")

        # Import report generator
        sys.path.append(str(Path(__file__).parent.parent.parent.parent.parent))
        from optimization_engine.report_generator import generate_study_report

        # Generate report
        output_path = generate_study_report(
            study_dir=study_dir,
            output_format=format.lower(),
            include_llm_summary=include_llm_summary
        )

        if output_path and output_path.exists():
            # Get relative path for URL
            rel_path = output_path.relative_to(study_dir)

            return {
                "status": "success",
                "format": format,
                "file_path": str(output_path),
                "download_url": f"/api/optimization/studies/{study_id}/reports/{output_path.name}",
                "file_size": output_path.stat().st_size,
                "message": f"Report generated successfully in {format} format"
            }
        else:
            raise HTTPException(status_code=500, detail="Report generation failed")

    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to generate report: {str(e)}")

@router.get("/studies/{study_id}/reports/{filename}")
async def download_report(study_id: str, filename: str):
    """
    Download a generated report file

    Args:
        study_id: Study identifier
        filename: Report filename

    Returns:
        Report file for download
    """
    try:
        # Validate filename to prevent directory traversal
        if '..' in filename or '/' in filename or '\\' in filename:
            raise HTTPException(status_code=400, detail="Invalid filename")

        study_dir = STUDIES_DIR / study_id
        results_dir = get_results_dir(study_dir)

        file_path = results_dir / filename

        if not file_path.exists():
            raise HTTPException(status_code=404, detail=f"Report file {filename} not found")

        # Determine content type
        suffix = file_path.suffix.lower()
        content_types = {
            '.md': 'text/markdown',
            '.html': 'text/html',
            '.pdf': 'application/pdf',
            '.json': 'application/json'
        }

        content_type = content_types.get(suffix, 'application/octet-stream')

        return FileResponse(
            path=str(file_path),
            media_type=content_type,
            filename=filename,
            headers={"Content-Disposition": f"attachment; filename={filename}"}
        )

    except FileNotFoundError:
        raise HTTPException(status_code=404, detail=f"Report file not found")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to download report: {str(e)}")


@router.get("/studies/{study_id}/console")
async def get_console_output(study_id: str, lines: int = 200):
    """
    Get the latest console output/logs from the optimization run

    Args:
        study_id: Study identifier
        lines: Number of lines to return (default: 200)

    Returns:
        JSON with console output lines
    """
    try:
        study_dir = STUDIES_DIR / study_id

        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        # Look for log files in various locations
        log_paths = [
            study_dir / "optimization.log",
            study_dir / "2_results" / "optimization.log",
            study_dir / "3_results" / "optimization.log",
            study_dir / "run.log",
        ]

        log_content = None
        log_path_used = None

        for log_path in log_paths:
            if log_path.exists():
                log_path_used = log_path
                break

        if log_path_used is None:
            return {
                "lines": [],
                "total_lines": 0,
                "log_file": None,
                "message": "No log file found. Optimization may not have started yet."
            }

        # Read the last N lines efficiently
        with open(log_path_used, 'r', encoding='utf-8', errors='replace') as f:
            all_lines = f.readlines()

        # Get last N lines
        last_lines = all_lines[-lines:] if len(all_lines) > lines else all_lines

        # Clean up lines (remove trailing newlines)
        last_lines = [line.rstrip('\n\r') for line in last_lines]

        return {
            "lines": last_lines,
            "total_lines": len(all_lines),
            "displayed_lines": len(last_lines),
            "log_file": str(log_path_used),
            "timestamp": datetime.now().isoformat()
        }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to read console output: {str(e)}")


@router.get("/studies/{study_id}/report")
async def get_study_report(study_id: str):
    """
    Get the STUDY_REPORT.md file content for a study

    Args:
        study_id: Study identifier

    Returns:
        JSON with the markdown content
    """
    try:
        study_dir = STUDIES_DIR / study_id

        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        # Look for STUDY_REPORT.md in the study root
        report_path = study_dir / "STUDY_REPORT.md"

        if not report_path.exists():
            raise HTTPException(status_code=404, detail="No STUDY_REPORT.md found for this study")

        with open(report_path, 'r', encoding='utf-8') as f:
            content = f.read()

        return {
            "content": content,
            "path": str(report_path),
            "study_id": study_id
        }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to read study report: {str(e)}")


# ============================================================================
# Study README and Config Endpoints
# ============================================================================

@router.get("/studies/{study_id}/readme")
async def get_study_readme(study_id: str):
    """
    Get the README.md file content for a study (from 1_setup folder)

    Args:
        study_id: Study identifier

    Returns:
        JSON with the markdown content
    """
    try:
        study_dir = STUDIES_DIR / study_id

        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        # Look for README.md in various locations
        readme_paths = [
            study_dir / "README.md",
            study_dir / "1_setup" / "README.md",
            study_dir / "readme.md",
        ]

        readme_content = None
        readme_path = None

        for path in readme_paths:
            if path.exists():
                readme_path = path
                with open(path, 'r', encoding='utf-8') as f:
                    readme_content = f.read()
                break

        if readme_content is None:
            # Generate a basic README from config if none exists
            config_file = study_dir / "1_setup" / "optimization_config.json"
            if not config_file.exists():
                config_file = study_dir / "optimization_config.json"

            if config_file.exists():
                with open(config_file) as f:
                    config = json.load(f)

                readme_content = f"""# {config.get('study_name', study_id)}

{config.get('description', 'No description available.')}

## Design Variables
{chr(10).join([f"- **{dv['name']}**: {dv.get('min', '?')} - {dv.get('max', '?')} {dv.get('units', '')}" for dv in config.get('design_variables', [])])}

## Objectives
{chr(10).join([f"- **{obj['name']}**: {obj.get('description', '')} ({obj.get('direction', 'minimize')})" for obj in config.get('objectives', [])])}
"""
            else:
                readme_content = f"# {study_id}\n\nNo README or configuration found for this study."

        return {
            "content": readme_content,
            "path": str(readme_path) if readme_path else None,
            "study_id": study_id
        }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to read README: {str(e)}")


@router.get("/studies/{study_id}/config")
async def get_study_config(study_id: str):
    """
    Get the full optimization_config.json for a study

    Args:
        study_id: Study identifier

    Returns:
        JSON with the complete configuration
    """
    try:
        study_dir = STUDIES_DIR / study_id

        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        # Look for config in various locations
        config_file = study_dir / "1_setup" / "optimization_config.json"
        if not config_file.exists():
            config_file = study_dir / "optimization_config.json"

        if not config_file.exists():
            raise HTTPException(status_code=404, detail=f"Config file not found for study {study_id}")

        with open(config_file) as f:
            config = json.load(f)

        return {
            "config": config,
            "path": str(config_file),
            "study_id": study_id
        }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to read config: {str(e)}")


# ============================================================================
# Process Control Endpoints
# ============================================================================

# Track running processes by study_id
_running_processes: Dict[str, int] = {}

def _find_optimization_process(study_id: str) -> Optional[psutil.Process]:
    """Find a running optimization process for a given study"""
    study_dir = STUDIES_DIR / study_id

    for proc in psutil.process_iter(['pid', 'name', 'cmdline', 'cwd']):
        try:
            cmdline = proc.info.get('cmdline') or []
            cmdline_str = ' '.join(cmdline) if cmdline else ''

            # Check if this is a Python process running run_optimization.py for this study
            if 'python' in cmdline_str.lower() and 'run_optimization' in cmdline_str:
                if study_id in cmdline_str or str(study_dir) in cmdline_str:
                    return proc
        except (psutil.NoSuchProcess, psutil.AccessDenied):
            continue

    return None


@router.get("/studies/{study_id}/process")
async def get_process_status(study_id: str):
    """
    Get the process status for a study's optimization run

    Args:
        study_id: Study identifier

    Returns:
        JSON with process status (is_running, pid, iteration counts)
    """
    try:
        study_dir = STUDIES_DIR / study_id

        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        # Check if process is running
        proc = _find_optimization_process(study_id)
        is_running = proc is not None
        pid = proc.pid if proc else None

        # Get iteration counts from database
        results_dir = get_results_dir(study_dir)
        study_db = results_dir / "study.db"

        fea_count = 0
        nn_count = 0
        iteration = None

        if study_db.exists():
            try:
                conn = sqlite3.connect(str(study_db))
                cursor = conn.cursor()

                # Count FEA trials (from main study or studies with "_fea" suffix)
                cursor.execute("""
                    SELECT COUNT(*) FROM trials t
                    JOIN studies s ON t.study_id = s.study_id
                    WHERE t.state = 'COMPLETE'
                    AND (s.study_name LIKE '%_fea' OR s.study_name NOT LIKE '%_nn%')
                """)
                fea_count = cursor.fetchone()[0]

                # Count NN trials
                cursor.execute("""
                    SELECT COUNT(*) FROM trials t
                    JOIN studies s ON t.study_id = s.study_id
                    WHERE t.state = 'COMPLETE'
                    AND s.study_name LIKE '%_nn%'
                """)
                nn_count = cursor.fetchone()[0]

                # Try to get current iteration from study names
                cursor.execute("""
                    SELECT study_name FROM studies
                    WHERE study_name LIKE '%_iter%'
                    ORDER BY study_name DESC LIMIT 1
                """)
                result = cursor.fetchone()
                if result:
                    import re
                    match = re.search(r'iter(\d+)', result[0])
                    if match:
                        iteration = int(match.group(1))

                conn.close()
            except Exception as e:
                print(f"Warning: Failed to read database for process status: {e}")

        return {
            "is_running": is_running,
            "pid": pid,
            "iteration": iteration,
            "fea_count": fea_count,
            "nn_count": nn_count,
            "study_id": study_id
        }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to get process status: {str(e)}")


class StartOptimizationRequest(BaseModel):
    freshStart: bool = False
    maxIterations: int = 100
    feaBatchSize: int = 5
    tuneTrials: int = 30
    ensembleSize: int = 3
    patience: int = 5


@router.post("/studies/{study_id}/start")
async def start_optimization(study_id: str, request: StartOptimizationRequest = None):
    """
    Start the optimization process for a study

    Args:
        study_id: Study identifier
        request: Optional start options

    Returns:
        JSON with process info
    """
    try:
        study_dir = STUDIES_DIR / study_id

        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        # Check if already running
        existing_proc = _find_optimization_process(study_id)
        if existing_proc:
            return {
                "success": False,
                "message": f"Optimization already running (PID: {existing_proc.pid})",
                "pid": existing_proc.pid
            }

        # Find run_optimization.py
        run_script = study_dir / "run_optimization.py"
        if not run_script.exists():
            raise HTTPException(status_code=404, detail=f"run_optimization.py not found for study {study_id}")

        # Build command with arguments
        python_exe = sys.executable
        cmd = [python_exe, str(run_script), "--start"]

        if request:
            if request.freshStart:
                cmd.append("--fresh")
            cmd.extend(["--fea-batch", str(request.feaBatchSize)])
            cmd.extend(["--tune-trials", str(request.tuneTrials)])
            cmd.extend(["--ensemble-size", str(request.ensembleSize)])
            cmd.extend(["--patience", str(request.patience)])

        # Start process in background
        proc = subprocess.Popen(
            cmd,
            cwd=str(study_dir),
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
            start_new_session=True
        )

        _running_processes[study_id] = proc.pid

        return {
            "success": True,
            "message": f"Optimization started successfully",
            "pid": proc.pid,
            "command": ' '.join(cmd)
        }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to start optimization: {str(e)}")


class StopRequest(BaseModel):
    force: bool = True  # Default to force kill


@router.post("/studies/{study_id}/stop")
async def stop_optimization(study_id: str, request: StopRequest = None):
    """
    Stop the optimization process for a study (hard kill by default)

    Args:
        study_id: Study identifier
        request.force: If True (default), immediately kill. If False, try graceful first.

    Returns:
        JSON with result
    """
    if request is None:
        request = StopRequest()

    try:
        study_dir = STUDIES_DIR / study_id

        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        # Find running process
        proc = _find_optimization_process(study_id)

        if not proc:
            return {
                "success": False,
                "message": "No running optimization process found"
            }

        pid = proc.pid
        killed_pids = []

        try:
            # FIRST: Get all children BEFORE killing parent
            children = []
            try:
                children = proc.children(recursive=True)
            except (psutil.NoSuchProcess, psutil.AccessDenied):
                pass

            if request.force:
                # Hard kill: immediately kill parent and all children
                # Kill children first (bottom-up)
                for child in reversed(children):
                    try:
                        child.kill()  # SIGKILL on Unix, TerminateProcess on Windows
                        killed_pids.append(child.pid)
                    except (psutil.NoSuchProcess, psutil.AccessDenied):
                        pass

                # Then kill parent
                try:
                    proc.kill()
                    killed_pids.append(pid)
                except psutil.NoSuchProcess:
                    pass
            else:
                # Graceful: try SIGTERM first, then force
                try:
                    proc.terminate()
                    proc.wait(timeout=5)
                except psutil.TimeoutExpired:
                    # Didn't stop gracefully, force kill
                    for child in reversed(children):
                        try:
                            child.kill()
                            killed_pids.append(child.pid)
                        except (psutil.NoSuchProcess, psutil.AccessDenied):
                            pass
                    proc.kill()
                    killed_pids.append(pid)
                except psutil.NoSuchProcess:
                    pass

            # Clean up tracking
            if study_id in _running_processes:
                del _running_processes[study_id]

            return {
                "success": True,
                "message": f"Optimization killed (PID: {pid}, +{len(children)} children)",
                "pid": pid,
                "killed_pids": killed_pids
            }

        except psutil.NoSuchProcess:
            if study_id in _running_processes:
                del _running_processes[study_id]
            return {
                "success": True,
                "message": "Process already terminated"
            }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to stop optimization: {str(e)}")


class ValidateRequest(BaseModel):
    topN: int = 5


@router.post("/studies/{study_id}/validate")
async def validate_optimization(study_id: str, request: ValidateRequest = None):
    """
    Run final FEA validation on top NN predictions

    Args:
        study_id: Study identifier
        request: Validation options (topN)

    Returns:
        JSON with process info
    """
    try:
        study_dir = STUDIES_DIR / study_id

        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        # Check if optimization is still running
        existing_proc = _find_optimization_process(study_id)
        if existing_proc:
            return {
                "success": False,
                "message": "Cannot validate while optimization is running. Stop optimization first."
            }

        # Look for final_validation.py script
        validation_script = study_dir / "final_validation.py"

        if not validation_script.exists():
            # Fall back to run_optimization.py with --validate flag if script doesn't exist
            run_script = study_dir / "run_optimization.py"
            if not run_script.exists():
                raise HTTPException(status_code=404, detail="No validation script found")

            python_exe = sys.executable
            top_n = request.topN if request else 5
            cmd = [python_exe, str(run_script), "--validate", "--top", str(top_n)]
        else:
            python_exe = sys.executable
            top_n = request.topN if request else 5
            cmd = [python_exe, str(validation_script), "--top", str(top_n)]

        # Start validation process
        proc = subprocess.Popen(
            cmd,
            cwd=str(study_dir),
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
            start_new_session=True
        )

        return {
            "success": True,
            "message": f"Validation started for top {top_n} NN predictions",
            "pid": proc.pid,
            "command": ' '.join(cmd)
        }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to start validation: {str(e)}")


# ============================================================================
# Optuna Dashboard Launch
# ============================================================================

_optuna_processes: Dict[str, subprocess.Popen] = {}

@router.post("/studies/{study_id}/optuna-dashboard")
async def launch_optuna_dashboard(study_id: str):
    """
    Launch Optuna dashboard for a specific study

    Args:
        study_id: Study identifier

    Returns:
        JSON with dashboard URL and process info
    """
    import time
    import socket

    def is_port_in_use(port: int) -> bool:
        """Check if a port is already in use"""
        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
            return s.connect_ex(('localhost', port)) == 0

    try:
        study_dir = STUDIES_DIR / study_id

        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        results_dir = get_results_dir(study_dir)
        study_db = results_dir / "study.db"

        if not study_db.exists():
            raise HTTPException(status_code=404, detail=f"No Optuna database found for study {study_id}")

        port = 8081

        # Check if dashboard is already running on this port
        if is_port_in_use(port):
            # Check if it's our process
            if study_id in _optuna_processes:
                proc = _optuna_processes[study_id]
                if proc.poll() is None:  # Still running
                    return {
                        "success": True,
                        "url": f"http://localhost:{port}",
                        "pid": proc.pid,
                        "message": "Optuna dashboard already running"
                    }
            # Port in use but not by us - still return success since dashboard is available
            return {
                "success": True,
                "url": f"http://localhost:{port}",
                "pid": None,
                "message": "Optuna dashboard already running on port 8081"
            }

        # Launch optuna-dashboard using Python script
        python_exe = sys.executable
        # Use absolute path with POSIX format for SQLite URL
        abs_db_path = study_db.absolute().as_posix()
        storage_url = f"sqlite:///{abs_db_path}"

        # Create a small Python script to run optuna-dashboard
        launch_script = f'''
from optuna_dashboard import run_server
run_server("{storage_url}", host="0.0.0.0", port={port})
'''
        cmd = [python_exe, "-c", launch_script]

        # On Windows, use CREATE_NEW_PROCESS_GROUP and DETACHED_PROCESS flags
        import platform
        if platform.system() == 'Windows':
            # Windows-specific: create detached process
            DETACHED_PROCESS = 0x00000008
            CREATE_NEW_PROCESS_GROUP = 0x00000200
            proc = subprocess.Popen(
                cmd,
                stdout=subprocess.PIPE,
                stderr=subprocess.PIPE,
                creationflags=DETACHED_PROCESS | CREATE_NEW_PROCESS_GROUP
            )
        else:
            proc = subprocess.Popen(
                cmd,
                stdout=subprocess.PIPE,
                stderr=subprocess.PIPE,
                start_new_session=True
            )

        _optuna_processes[study_id] = proc

        # Wait for dashboard to start (check port repeatedly)
        max_wait = 5  # seconds
        start_time = time.time()
        while time.time() - start_time < max_wait:
            if is_port_in_use(port):
                return {
                    "success": True,
                    "url": f"http://localhost:{port}",
                    "pid": proc.pid,
                    "message": "Optuna dashboard launched successfully"
                }
            # Check if process died
            if proc.poll() is not None:
                stderr = ""
                try:
                    stderr = proc.stderr.read().decode() if proc.stderr else ""
                except:
                    pass
                return {
                    "success": False,
                    "message": f"Failed to start Optuna dashboard: {stderr}"
                }
            time.sleep(0.5)

        # Timeout - process might still be starting
        if proc.poll() is None:
            return {
                "success": True,
                "url": f"http://localhost:{port}",
                "pid": proc.pid,
                "message": "Optuna dashboard starting (may take a moment)"
            }
        else:
            stderr = ""
            try:
                stderr = proc.stderr.read().decode() if proc.stderr else ""
            except:
                pass
            return {
                "success": False,
                "message": f"Failed to start Optuna dashboard: {stderr}"
            }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to launch Optuna dashboard: {str(e)}")


# ============================================================================
# Model Files Endpoint
# ============================================================================

@router.get("/studies/{study_id}/model-files")
async def get_model_files(study_id: str):
    """
    Get list of NX model files (.prt, .sim, .fem, .bdf, .dat, .op2) for a study

    Args:
        study_id: Study identifier

    Returns:
        JSON with list of model files and their paths
    """
    try:
        study_dir = STUDIES_DIR / study_id

        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        # Look for model directory (check multiple locations)
        model_dirs = [
            study_dir / "1_setup" / "model",
            study_dir / "model",
            study_dir / "1_setup",
            study_dir
        ]

        model_files = []
        model_dir_path = None

        # NX and FEA file extensions to look for
        nx_extensions = {'.prt', '.sim', '.fem', '.bdf', '.dat', '.op2', '.f06', '.inp'}

        for model_dir in model_dirs:
            if model_dir.exists() and model_dir.is_dir():
                for file_path in model_dir.iterdir():
                    if file_path.is_file() and file_path.suffix.lower() in nx_extensions:
                        model_files.append({
                            "name": file_path.name,
                            "path": str(file_path),
                            "extension": file_path.suffix.lower(),
                            "size_bytes": file_path.stat().st_size,
                            "size_display": _format_file_size(file_path.stat().st_size),
                            "modified": datetime.fromtimestamp(file_path.stat().st_mtime).isoformat()
                        })
                        if model_dir_path is None:
                            model_dir_path = str(model_dir)

        # Sort by extension for better display (prt first, then sim, fem, etc.)
        extension_order = {'.prt': 0, '.sim': 1, '.fem': 2, '.bdf': 3, '.dat': 4, '.op2': 5, '.f06': 6, '.inp': 7}
        model_files.sort(key=lambda x: (extension_order.get(x['extension'], 99), x['name']))

        return {
            "study_id": study_id,
            "model_dir": model_dir_path or str(study_dir / "1_setup" / "model"),
            "files": model_files,
            "count": len(model_files)
        }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to get model files: {str(e)}")


def _format_file_size(size_bytes: int) -> str:
    """Format file size in human-readable form"""
    if size_bytes < 1024:
        return f"{size_bytes} B"
    elif size_bytes < 1024 * 1024:
        return f"{size_bytes / 1024:.1f} KB"
    elif size_bytes < 1024 * 1024 * 1024:
        return f"{size_bytes / (1024 * 1024):.1f} MB"
    else:
        return f"{size_bytes / (1024 * 1024 * 1024):.2f} GB"


@router.post("/studies/{study_id}/open-folder")
async def open_model_folder(study_id: str, folder_type: str = "model"):
    """
    Open the model folder in system file explorer

    Args:
        study_id: Study identifier
        folder_type: Type of folder to open (model, results, setup)

    Returns:
        JSON with success status
    """
    import os
    import platform

    try:
        study_dir = STUDIES_DIR / study_id

        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        # Determine which folder to open
        if folder_type == "model":
            target_dir = study_dir / "1_setup" / "model"
            if not target_dir.exists():
                target_dir = study_dir / "1_setup"
        elif folder_type == "results":
            target_dir = get_results_dir(study_dir)
        elif folder_type == "setup":
            target_dir = study_dir / "1_setup"
        else:
            target_dir = study_dir

        if not target_dir.exists():
            target_dir = study_dir

        # Open in file explorer based on platform
        system = platform.system()
        try:
            if system == "Windows":
                os.startfile(str(target_dir))
            elif system == "Darwin":  # macOS
                subprocess.Popen(["open", str(target_dir)])
            else:  # Linux
                subprocess.Popen(["xdg-open", str(target_dir)])

            return {
                "success": True,
                "message": f"Opened {target_dir}",
                "path": str(target_dir)
            }
        except Exception as e:
            return {
                "success": False,
                "message": f"Failed to open folder: {str(e)}",
                "path": str(target_dir)
            }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to open folder: {str(e)}")


@router.get("/studies/{study_id}/best-solution")
async def get_best_solution(study_id: str):
    """Get the best trial(s) for a study with improvement metrics"""
    try:
        study_dir = STUDIES_DIR / study_id
        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study '{study_id}' not found")

        results_dir = get_results_dir(study_dir)
        db_path = results_dir / "study.db"

        if not db_path.exists():
            return {
                "study_id": study_id,
                "best_trial": None,
                "first_trial": None,
                "improvements": {},
                "total_trials": 0
            }

        conn = sqlite3.connect(str(db_path))
        conn.row_factory = sqlite3.Row
        cursor = conn.cursor()

        # Get best trial (single objective - minimize by default)
        cursor.execute("""
            SELECT t.trial_id, t.number, tv.value as objective,
                   datetime(tv.value_id, 'unixepoch') as timestamp
            FROM trials t
            JOIN trial_values tv ON t.trial_id = tv.trial_id
            WHERE t.state = 'COMPLETE'
            ORDER BY tv.value ASC
            LIMIT 1
        """)
        best_row = cursor.fetchone()

        # Get first completed trial for comparison
        cursor.execute("""
            SELECT t.trial_id, t.number, tv.value as objective
            FROM trials t
            JOIN trial_values tv ON t.trial_id = tv.trial_id
            WHERE t.state = 'COMPLETE'
            ORDER BY t.number ASC
            LIMIT 1
        """)
        first_row = cursor.fetchone()

        # Get total trial count
        cursor.execute("SELECT COUNT(*) FROM trials WHERE state = 'COMPLETE'")
        total_trials = cursor.fetchone()[0]

        best_trial = None
        first_trial = None
        improvements = {}

        if best_row:
            best_trial_id = best_row['trial_id']

            # Get design variables
            cursor.execute("""
                SELECT param_name, param_value
                FROM trial_params
                WHERE trial_id = ?
            """, (best_trial_id,))
            params = {row['param_name']: row['param_value'] for row in cursor.fetchall()}

            # Get user attributes (including results)
            cursor.execute("""
                SELECT key, value_json
                FROM trial_user_attributes
                WHERE trial_id = ?
            """, (best_trial_id,))
            user_attrs = {}
            for row in cursor.fetchall():
                try:
                    user_attrs[row['key']] = json.loads(row['value_json'])
                except:
                    user_attrs[row['key']] = row['value_json']

            best_trial = {
                "trial_number": best_row['number'],
                "objective": best_row['objective'],
                "design_variables": params,
                "user_attrs": user_attrs,
                "timestamp": best_row['timestamp']
            }

        if first_row:
            first_trial_id = first_row['trial_id']

            cursor.execute("""
                SELECT param_name, param_value
                FROM trial_params
                WHERE trial_id = ?
            """, (first_trial_id,))
            first_params = {row['param_name']: row['param_value'] for row in cursor.fetchall()}

            first_trial = {
                "trial_number": first_row['number'],
                "objective": first_row['objective'],
                "design_variables": first_params
            }

            # Calculate improvement
            if best_row and first_row['objective'] != 0:
                improvement_pct = ((first_row['objective'] - best_row['objective']) / abs(first_row['objective'])) * 100
                improvements["objective"] = {
                    "initial": first_row['objective'],
                    "final": best_row['objective'],
                    "improvement_pct": round(improvement_pct, 2),
                    "absolute_change": round(first_row['objective'] - best_row['objective'], 6)
                }

        conn.close()

        return {
            "study_id": study_id,
            "best_trial": best_trial,
            "first_trial": first_trial,
            "improvements": improvements,
            "total_trials": total_trials
        }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to get best solution: {str(e)}")


@router.get("/studies/{study_id}/runs")
async def get_study_runs(study_id: str):
    """
    Get all optimization runs/studies in the database for comparison.
    Many studies have multiple Optuna studies (e.g., v11_fea, v11_iter1_nn, v11_iter2_nn).
    This endpoint returns metrics for each sub-study.
    """
    try:
        study_dir = STUDIES_DIR / study_id
        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study '{study_id}' not found")

        results_dir = get_results_dir(study_dir)
        db_path = results_dir / "study.db"

        if not db_path.exists():
            return {"runs": [], "total_runs": 0}

        conn = sqlite3.connect(str(db_path))
        conn.row_factory = sqlite3.Row
        cursor = conn.cursor()

        # Get all Optuna studies in this database
        cursor.execute("""
            SELECT study_id, study_name
            FROM studies
            ORDER BY study_id
        """)
        studies = cursor.fetchall()

        runs = []
        for study_row in studies:
            optuna_study_id = study_row['study_id']
            study_name = study_row['study_name']

            # Get trial count
            cursor.execute("""
                SELECT COUNT(*) FROM trials
                WHERE study_id = ? AND state = 'COMPLETE'
            """, (optuna_study_id,))
            trial_count = cursor.fetchone()[0]

            if trial_count == 0:
                continue

            # Get best value (first objective)
            cursor.execute("""
                SELECT MIN(tv.value) as best_value
                FROM trial_values tv
                JOIN trials t ON tv.trial_id = t.trial_id
                WHERE t.study_id = ? AND t.state = 'COMPLETE' AND tv.objective = 0
            """, (optuna_study_id,))
            best_result = cursor.fetchone()
            best_value = best_result['best_value'] if best_result else None

            # Get average value
            cursor.execute("""
                SELECT AVG(tv.value) as avg_value
                FROM trial_values tv
                JOIN trials t ON tv.trial_id = t.trial_id
                WHERE t.study_id = ? AND t.state = 'COMPLETE' AND tv.objective = 0
            """, (optuna_study_id,))
            avg_result = cursor.fetchone()
            avg_value = avg_result['avg_value'] if avg_result else None

            # Get time range
            cursor.execute("""
                SELECT MIN(datetime_start) as first_trial, MAX(datetime_complete) as last_trial
                FROM trials
                WHERE study_id = ? AND state = 'COMPLETE'
            """, (optuna_study_id,))
            time_result = cursor.fetchone()

            # Determine source type (FEA or NN)
            source = "NN" if "_nn" in study_name.lower() else "FEA"

            runs.append({
                "run_id": optuna_study_id,
                "name": study_name,
                "source": source,
                "trial_count": trial_count,
                "best_value": best_value,
                "avg_value": avg_value,
                "first_trial": time_result['first_trial'] if time_result else None,
                "last_trial": time_result['last_trial'] if time_result else None
            })

        conn.close()

        return {
            "runs": runs,
            "total_runs": len(runs),
            "study_id": study_id
        }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to get runs: {str(e)}")


class UpdateConfigRequest(BaseModel):
    config: dict


@router.put("/studies/{study_id}/config")
async def update_study_config(study_id: str, request: UpdateConfigRequest):
    """
    Update the optimization_config.json for a study

    Args:
        study_id: Study identifier
        request: New configuration data

    Returns:
        JSON with success status
    """
    try:
        study_dir = STUDIES_DIR / study_id

        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study {study_id} not found")

        # Check if optimization is running - don't allow config changes while running
        if is_optimization_running(study_id):
            raise HTTPException(
                status_code=409,
                detail="Cannot modify config while optimization is running. Stop the optimization first."
            )

        # Find config file location
        config_file = study_dir / "1_setup" / "optimization_config.json"
        if not config_file.exists():
            config_file = study_dir / "optimization_config.json"

        if not config_file.exists():
            raise HTTPException(status_code=404, detail=f"Config file not found for study {study_id}")

        # Backup existing config
        backup_file = config_file.with_suffix('.json.backup')
        shutil.copy(config_file, backup_file)

        # Write new config
        with open(config_file, 'w') as f:
            json.dump(request.config, f, indent=2)

        return {
            "success": True,
            "message": "Configuration updated successfully",
            "path": str(config_file),
            "backup_path": str(backup_file)
        }

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to update config: {str(e)}")


@router.get("/studies/{study_id}/export/{format}")
async def export_study_data(study_id: str, format: str):
    """Export study data in various formats: csv, json, excel"""
    try:
        study_dir = STUDIES_DIR / study_id
        if not study_dir.exists():
            raise HTTPException(status_code=404, detail=f"Study '{study_id}' not found")

        results_dir = get_results_dir(study_dir)
        db_path = results_dir / "study.db"

        if not db_path.exists():
            raise HTTPException(status_code=404, detail="No study data available")

        conn = sqlite3.connect(str(db_path))
        conn.row_factory = sqlite3.Row
        cursor = conn.cursor()

        # Get all completed trials with their params and values
        cursor.execute("""
            SELECT t.trial_id, t.number, tv.value as objective
            FROM trials t
            JOIN trial_values tv ON t.trial_id = tv.trial_id
            WHERE t.state = 'COMPLETE'
            ORDER BY t.number
        """)
        trials_data = []

        for row in cursor.fetchall():
            trial_id = row['trial_id']

            # Get params
            cursor.execute("""
                SELECT param_name, param_value
                FROM trial_params
                WHERE trial_id = ?
            """, (trial_id,))
            params = {r['param_name']: r['param_value'] for r in cursor.fetchall()}

            # Get user attrs
            cursor.execute("""
                SELECT key, value_json
                FROM trial_user_attributes
                WHERE trial_id = ?
            """, (trial_id,))
            user_attrs = {}
            for r in cursor.fetchall():
                try:
                    user_attrs[r['key']] = json.loads(r['value_json'])
                except:
                    user_attrs[r['key']] = r['value_json']

            trials_data.append({
                "trial_number": row['number'],
                "objective": row['objective'],
                "params": params,
                "user_attrs": user_attrs
            })

        conn.close()

        if format.lower() == "json":
            return JSONResponse(content={
                "study_id": study_id,
                "total_trials": len(trials_data),
                "trials": trials_data
            })

        elif format.lower() == "csv":
            import io
            import csv

            if not trials_data:
                return JSONResponse(content={"error": "No data to export"})

            # Build CSV
            output = io.StringIO()

            # Get all param names
            param_names = sorted(set(
                key for trial in trials_data
                for key in trial['params'].keys()
            ))

            fieldnames = ['trial_number', 'objective'] + param_names
            writer = csv.DictWriter(output, fieldnames=fieldnames)
            writer.writeheader()

            for trial in trials_data:
                row_data = {
                    'trial_number': trial['trial_number'],
                    'objective': trial['objective']
                }
                row_data.update(trial['params'])
                writer.writerow(row_data)

            csv_content = output.getvalue()

            return JSONResponse(content={
                "filename": f"{study_id}_data.csv",
                "content": csv_content,
                "content_type": "text/csv"
            })

        elif format.lower() == "config":
            # Export optimization config
            setup_dir = study_dir / "1_setup"
            config_path = setup_dir / "optimization_config.json"

            if config_path.exists():
                with open(config_path, 'r') as f:
                    config = json.load(f)
                return JSONResponse(content={
                    "filename": f"{study_id}_config.json",
                    "content": json.dumps(config, indent=2),
                    "content_type": "application/json"
                })
            else:
                raise HTTPException(status_code=404, detail="Config file not found")

        else:
            raise HTTPException(status_code=400, detail=f"Unsupported format: {format}")

    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to export data: {str(e)}")