75d7036193
- Add ConvergencePlot component with running best, statistics, gradient fill - Add ParameterImportanceChart with Pearson correlation analysis - Add StudyReportViewer with KaTeX math rendering and full markdown support - Update pruning endpoint to query Optuna database directly - Add /report endpoint for STUDY_REPORT.md files - Fix chart data transformation for single/multi-objective studies - Update Protocol 13 documentation with new components - Update generate-report skill with dashboard integration 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
949 lines
35 KiB
Python
949 lines
35 KiB
Python
"""
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Optimization API endpoints
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Handles study status, history retrieval, and control operations
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"""
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from fastapi import APIRouter, HTTPException, UploadFile, File, Form
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from fastapi.responses import JSONResponse, FileResponse
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from pathlib import Path
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from typing import List, Dict, Optional
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import json
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import sys
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import sqlite3
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import shutil
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from datetime import datetime
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# Add project root to path
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sys.path.append(str(Path(__file__).parent.parent.parent.parent.parent))
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router = APIRouter()
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# Base studies directory
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STUDIES_DIR = Path(__file__).parent.parent.parent.parent.parent / "studies"
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def get_results_dir(study_dir: Path) -> Path:
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"""Get the results directory for a study, supporting both 2_results and 3_results."""
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results_dir = study_dir / "2_results"
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if not results_dir.exists():
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results_dir = study_dir / "3_results"
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return results_dir
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@router.get("/studies")
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async def list_studies():
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"""List all available optimization studies"""
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try:
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studies = []
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if not STUDIES_DIR.exists():
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return {"studies": []}
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for study_dir in STUDIES_DIR.iterdir():
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if not study_dir.is_dir():
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continue
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# Look for optimization config (check multiple locations)
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config_file = study_dir / "optimization_config.json"
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if not config_file.exists():
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config_file = study_dir / "1_setup" / "optimization_config.json"
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if not config_file.exists():
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continue
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# Load config
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with open(config_file) as f:
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config = json.load(f)
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# Check if results directory exists (support both 2_results and 3_results)
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results_dir = study_dir / "2_results"
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if not results_dir.exists():
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results_dir = study_dir / "3_results"
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# Check for Optuna database (Protocol 10) or JSON history (other protocols)
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study_db = results_dir / "study.db"
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history_file = results_dir / "optimization_history_incremental.json"
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status = "not_started"
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trial_count = 0
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best_value = None
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# Protocol 10: Read from Optuna SQLite database
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if study_db.exists():
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try:
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conn = sqlite3.connect(str(study_db))
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cursor = conn.cursor()
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# Get trial count and status
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cursor.execute("SELECT COUNT(*) FROM trials WHERE state = 'COMPLETE'")
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trial_count = cursor.fetchone()[0]
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# Get best trial (for single-objective, or first objective for multi-objective)
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if trial_count > 0:
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cursor.execute("""
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SELECT value FROM trial_values
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WHERE trial_id IN (
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SELECT trial_id FROM trials WHERE state = 'COMPLETE'
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)
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ORDER BY value ASC
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LIMIT 1
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""")
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result = cursor.fetchone()
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if result:
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best_value = result[0]
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conn.close()
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# Determine status
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total_trials = config.get('optimization_settings', {}).get('n_trials', 50)
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if trial_count >= total_trials:
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status = "completed"
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else:
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status = "running" # Simplified - would need process check
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except Exception as e:
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print(f"Warning: Failed to read Optuna database for {study_dir.name}: {e}")
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status = "error"
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# Legacy: Read from JSON history
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elif history_file.exists():
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with open(history_file) as f:
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history = json.load(f)
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trial_count = len(history)
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if history:
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# Find best trial
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best_trial = min(history, key=lambda x: x['objective'])
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best_value = best_trial['objective']
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# Determine status
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total_trials = config.get('trials', {}).get('n_trials', 50)
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if trial_count >= total_trials:
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status = "completed"
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else:
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status = "running" # Simplified - would need process check
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# Get total trials from config (supports both formats)
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total_trials = (
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config.get('optimization_settings', {}).get('n_trials') or
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config.get('trials', {}).get('n_trials', 50)
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)
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studies.append({
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"id": study_dir.name,
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"name": study_dir.name.replace("_", " ").title(),
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"status": status,
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"progress": {
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"current": trial_count,
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"total": total_trials
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},
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"best_value": best_value,
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"target": config.get('target', {}).get('value'),
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"path": str(study_dir)
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})
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return {"studies": studies}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Failed to list studies: {str(e)}")
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@router.get("/studies/{study_id}/status")
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async def get_study_status(study_id: str):
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"""Get detailed status of a specific study"""
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try:
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study_dir = STUDIES_DIR / study_id
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if not study_dir.exists():
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raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
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# Load config (check multiple locations)
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config_file = study_dir / "optimization_config.json"
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if not config_file.exists():
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config_file = study_dir / "1_setup" / "optimization_config.json"
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with open(config_file) as f:
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config = json.load(f)
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# Check for results (support both 2_results and 3_results)
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results_dir = get_results_dir(study_dir)
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study_db = results_dir / "study.db"
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history_file = results_dir / "optimization_history_incremental.json"
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# Protocol 10: Read from Optuna database
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if study_db.exists():
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conn = sqlite3.connect(str(study_db))
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cursor = conn.cursor()
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# Get trial counts by state
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cursor.execute("SELECT COUNT(*) FROM trials WHERE state = 'COMPLETE'")
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trial_count = cursor.fetchone()[0]
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cursor.execute("SELECT COUNT(*) FROM trials WHERE state = 'PRUNED'")
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pruned_count = cursor.fetchone()[0]
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# Get best trial (first objective for multi-objective)
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best_trial = None
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if trial_count > 0:
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cursor.execute("""
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SELECT t.trial_id, t.number, tv.value
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FROM trials t
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JOIN trial_values tv ON t.trial_id = tv.trial_id
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WHERE t.state = 'COMPLETE'
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ORDER BY tv.value ASC
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LIMIT 1
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""")
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result = cursor.fetchone()
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if result:
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trial_id, trial_number, best_value = result
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# Get parameters for this trial
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cursor.execute("""
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SELECT param_name, param_value
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FROM trial_params
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WHERE trial_id = ?
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""", (trial_id,))
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params = {row[0]: row[1] for row in cursor.fetchall()}
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best_trial = {
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"trial_number": trial_number,
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"objective": best_value,
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"design_variables": params,
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"results": {"first_frequency": best_value}
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}
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conn.close()
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total_trials = config.get('optimization_settings', {}).get('n_trials', 50)
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status = "completed" if trial_count >= total_trials else "running"
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return {
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"study_id": study_id,
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"status": status,
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"progress": {
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"current": trial_count,
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"total": total_trials,
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"percentage": (trial_count / total_trials * 100) if total_trials > 0 else 0
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},
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"best_trial": best_trial,
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"pruned_trials": pruned_count,
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"config": config
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}
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# Legacy: Read from JSON history
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if not history_file.exists():
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return {
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"study_id": study_id,
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"status": "not_started",
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"progress": {"current": 0, "total": config.get('trials', {}).get('n_trials', 50)},
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"config": config
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}
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with open(history_file) as f:
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history = json.load(f)
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trial_count = len(history)
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total_trials = config.get('trials', {}).get('n_trials', 50)
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# Find best trial
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best_trial = None
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if history:
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best_trial = min(history, key=lambda x: x['objective'])
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# Check for pruning data
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pruning_file = results_dir / "pruning_history.json"
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pruned_count = 0
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if pruning_file.exists():
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with open(pruning_file) as f:
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pruning_history = json.load(f)
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pruned_count = len(pruning_history)
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status = "completed" if trial_count >= total_trials else "running"
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return {
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"study_id": study_id,
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"status": status,
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"progress": {
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"current": trial_count,
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"total": total_trials,
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"percentage": (trial_count / total_trials * 100) if total_trials > 0 else 0
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},
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"best_trial": best_trial,
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"pruned_trials": pruned_count,
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"config": config
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}
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except FileNotFoundError:
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raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Failed to get study status: {str(e)}")
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@router.get("/studies/{study_id}/history")
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async def get_optimization_history(study_id: str, limit: Optional[int] = None):
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"""Get optimization history (all trials)"""
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try:
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study_dir = STUDIES_DIR / study_id
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results_dir = get_results_dir(study_dir)
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study_db = results_dir / "study.db"
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history_file = results_dir / "optimization_history_incremental.json"
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# Protocol 10: Read from Optuna database
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if study_db.exists():
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conn = sqlite3.connect(str(study_db))
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cursor = conn.cursor()
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# Get all completed trials
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cursor.execute("""
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SELECT trial_id, number, datetime_start, datetime_complete
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FROM trials
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WHERE state = 'COMPLETE'
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ORDER BY number DESC
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""" + (f" LIMIT {limit}" if limit else ""))
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trial_rows = cursor.fetchall()
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trials = []
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for trial_id, trial_num, start_time, end_time in trial_rows:
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# Get objectives for this trial
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cursor.execute("""
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SELECT value
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FROM trial_values
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WHERE trial_id = ?
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ORDER BY objective
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""", (trial_id,))
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values = [row[0] for row in cursor.fetchall()]
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# Get parameters for this trial
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cursor.execute("""
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SELECT param_name, param_value
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FROM trial_params
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WHERE trial_id = ?
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""", (trial_id,))
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params = {}
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for param_name, param_value in cursor.fetchall():
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try:
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params[param_name] = float(param_value) if param_value is not None else None
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except (ValueError, TypeError):
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params[param_name] = param_value
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# Get user attributes (extracted results: mass, frequency, stress, displacement, etc.)
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cursor.execute("""
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SELECT key, value_json
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FROM trial_user_attributes
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WHERE trial_id = ?
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""", (trial_id,))
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user_attrs = {}
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for key, value_json in cursor.fetchall():
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try:
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user_attrs[key] = json.loads(value_json)
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except (ValueError, TypeError):
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user_attrs[key] = value_json
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# Extract ALL numeric metrics from user_attrs for results
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# This ensures multi-objective studies show all Zernike metrics, RMS values, etc.
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results = {}
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excluded_keys = {"design_vars", "constraint_satisfied", "constraint_violations"}
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for key, val in user_attrs.items():
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if key in excluded_keys:
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continue
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# Include numeric values and lists of numbers
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if isinstance(val, (int, float)):
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results[key] = val
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elif isinstance(val, list) and len(val) > 0 and isinstance(val[0], (int, float)):
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# For lists, store as-is (e.g., Zernike coefficients)
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results[key] = val
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elif key == "objectives" and isinstance(val, dict):
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# Extract nested objectives dict (Zernike multi-objective studies)
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for obj_key, obj_val in val.items():
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if isinstance(obj_val, (int, float)):
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results[obj_key] = obj_val
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# Fallback to first frequency from objectives if available
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if not results and len(values) > 0:
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results["first_frequency"] = values[0]
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# CRITICAL: Extract design_vars from user_attrs if stored there
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# The optimization code does: trial.set_user_attr("design_vars", design_vars)
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design_vars_from_attrs = user_attrs.get("design_vars", {})
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# Merge with params (prefer user_attrs design_vars if available)
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final_design_vars = {**params, **design_vars_from_attrs} if design_vars_from_attrs else params
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trials.append({
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"trial_number": trial_num,
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"objective": values[0] if len(values) > 0 else None, # Primary objective
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"objectives": values if len(values) > 1 else None, # All objectives for multi-objective
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"design_variables": final_design_vars, # Use merged design vars
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"results": results,
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"user_attrs": user_attrs, # Include all user attributes
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"start_time": start_time,
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"end_time": end_time
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})
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conn.close()
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return {"trials": trials}
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# Legacy: Read from JSON history
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if not history_file.exists():
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return {"trials": []}
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with open(history_file) as f:
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history = json.load(f)
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# Apply limit if specified
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if limit:
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history = history[-limit:]
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return {"trials": history}
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except FileNotFoundError:
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raise HTTPException(status_code=404, detail=f"Study {study_id} not found")
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Failed to get history: {str(e)}")
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@router.get("/studies/{study_id}/pruning")
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async def get_pruning_history(study_id: str):
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"""Get pruning diagnostics from Optuna database or legacy JSON file"""
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try:
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study_dir = STUDIES_DIR / study_id
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results_dir = get_results_dir(study_dir)
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study_db = results_dir / "study.db"
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pruning_file = results_dir / "pruning_history.json"
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# Protocol 10+: Read from Optuna database
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if study_db.exists():
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conn = sqlite3.connect(str(study_db))
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cursor = conn.cursor()
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# Get all pruned trials from Optuna database
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cursor.execute("""
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SELECT t.trial_id, t.number, t.datetime_start, t.datetime_complete
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FROM trials t
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WHERE t.state = 'PRUNED'
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ORDER BY t.number DESC
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""")
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pruned_rows = cursor.fetchall()
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|
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pruned_trials = []
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for trial_id, trial_num, start_time, end_time in pruned_rows:
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# Get parameters for this trial
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cursor.execute("""
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SELECT param_name, param_value
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FROM trial_params
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WHERE trial_id = ?
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""", (trial_id,))
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params = {row[0]: row[1] for row in cursor.fetchall()}
|
|
|
|
# Get user attributes (may contain pruning cause)
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cursor.execute("""
|
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SELECT key, value_json
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FROM trial_user_attributes
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WHERE trial_id = ?
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""", (trial_id,))
|
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user_attrs = {}
|
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for key, value_json in cursor.fetchall():
|
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try:
|
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user_attrs[key] = json.loads(value_json)
|
|
except (ValueError, TypeError):
|
|
user_attrs[key] = value_json
|
|
|
|
pruned_trials.append({
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"trial_number": trial_num,
|
|
"params": params,
|
|
"pruning_cause": user_attrs.get("pruning_cause", "Unknown"),
|
|
"start_time": start_time,
|
|
"end_time": end_time
|
|
})
|
|
|
|
conn.close()
|
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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:
|
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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:
|
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return "MPa"
|
|
elif "displacement" in name or "mm" in desc:
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return "mm"
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elif "force" in name or "newton" in desc:
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return "N"
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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.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}/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)}")
|