Anto01
a26914bbe8
Dashboard: - Add Studio page with drag-drop model upload and Claude chat - Add intake system for study creation workflow - Improve session manager and context builder - Add intake API routes and frontend components Optimization Engine: - Add CLI module for command-line operations - Add intake module for study preprocessing - Add validation module with gate checks - Improve Zernike extractor documentation - Update spec models with better validation - Enhance solve_simulation robustness Documentation: - Add ATOMIZER_STUDIO.md planning doc - Add ATOMIZER_UX_SYSTEM.md for UX patterns - Update extractor library docs - Add study-readme-generator skill Tools: - Add test scripts for extraction validation - Add Zernike recentering test Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
397 lines
13 KiB
Python
397 lines
13 KiB
Python
"""
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Claude README Generator Service
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Generates intelligent README.md files for optimization studies
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using Claude Code CLI (not API) with study context from AtomizerSpec.
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"""
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import asyncio
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import json
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import subprocess
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, Optional
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# Base directory
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ATOMIZER_ROOT = Path(__file__).parent.parent.parent.parent.parent
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# Load skill prompt
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SKILL_PATH = ATOMIZER_ROOT / ".claude" / "skills" / "modules" / "study-readme-generator.md"
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def load_skill_prompt() -> str:
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"""Load the README generator skill prompt."""
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if SKILL_PATH.exists():
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return SKILL_PATH.read_text(encoding="utf-8")
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return ""
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class ClaudeReadmeGenerator:
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"""Generate README.md files using Claude Code CLI."""
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def __init__(self):
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self.skill_prompt = load_skill_prompt()
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def generate_readme(
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self,
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study_name: str,
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spec: Dict[str, Any],
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context_files: Optional[Dict[str, str]] = None,
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topic: Optional[str] = None,
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) -> str:
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"""
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Generate a README.md for a study using Claude Code CLI.
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Args:
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study_name: Name of the study
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spec: Full AtomizerSpec v2.0 dict
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context_files: Optional dict of {filename: content} for context
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topic: Optional topic folder name
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Returns:
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Generated README.md content
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"""
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# Build context for Claude
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context = self._build_context(study_name, spec, context_files, topic)
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# Build the prompt
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prompt = self._build_prompt(context)
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try:
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# Run Claude Code CLI synchronously
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result = self._run_claude_cli(prompt)
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# Extract markdown content from response
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readme_content = self._extract_markdown(result)
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if readme_content:
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return readme_content
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# If no markdown found, return the whole response
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return result if result else self._generate_fallback_readme(study_name, spec)
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except Exception as e:
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print(f"Claude CLI error: {e}")
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return self._generate_fallback_readme(study_name, spec)
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async def generate_readme_async(
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self,
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study_name: str,
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spec: Dict[str, Any],
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context_files: Optional[Dict[str, str]] = None,
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topic: Optional[str] = None,
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) -> str:
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"""Async version of generate_readme."""
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# Run in thread pool to not block
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loop = asyncio.get_event_loop()
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return await loop.run_in_executor(
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None, lambda: self.generate_readme(study_name, spec, context_files, topic)
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)
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def _run_claude_cli(self, prompt: str) -> str:
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"""Run Claude Code CLI and get response."""
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try:
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# Use claude CLI with --print flag for non-interactive output
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result = subprocess.run(
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["claude", "--print", prompt],
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capture_output=True,
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text=True,
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timeout=120, # 2 minute timeout
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cwd=str(ATOMIZER_ROOT),
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)
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if result.returncode != 0:
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error_msg = result.stderr or "Unknown error"
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raise Exception(f"Claude CLI error: {error_msg}")
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return result.stdout.strip()
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except subprocess.TimeoutExpired:
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raise Exception("Request timed out")
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except FileNotFoundError:
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raise Exception("Claude CLI not found. Make sure 'claude' is in PATH.")
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def _build_context(
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self,
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study_name: str,
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spec: Dict[str, Any],
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context_files: Optional[Dict[str, str]],
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topic: Optional[str],
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) -> Dict[str, Any]:
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"""Build the context object for Claude."""
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meta = spec.get("meta", {})
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model = spec.get("model", {})
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introspection = model.get("introspection", {}) or {}
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context = {
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"study_name": study_name,
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"topic": topic or meta.get("topic", "Other"),
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"description": meta.get("description", ""),
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"created": meta.get("created", datetime.now().isoformat()),
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"status": meta.get("status", "draft"),
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"design_variables": spec.get("design_variables", []),
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"extractors": spec.get("extractors", []),
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"objectives": spec.get("objectives", []),
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"constraints": spec.get("constraints", []),
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"optimization": spec.get("optimization", {}),
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"introspection": {
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"mass_kg": introspection.get("mass_kg"),
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"volume_mm3": introspection.get("volume_mm3"),
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"solver_type": introspection.get("solver_type"),
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"expressions": introspection.get("expressions", []),
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"expressions_count": len(introspection.get("expressions", [])),
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},
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"model_files": {
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"sim": model.get("sim", {}).get("path") if model.get("sim") else None,
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"prt": model.get("prt", {}).get("path") if model.get("prt") else None,
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"fem": model.get("fem", {}).get("path") if model.get("fem") else None,
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},
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}
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# Add context files if provided
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if context_files:
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context["context_files"] = context_files
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return context
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def _build_prompt(self, context: Dict[str, Any]) -> str:
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"""Build the prompt for Claude CLI."""
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# Build context files section if available
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context_files_section = ""
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if context.get("context_files"):
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context_files_section = "\n\n## User-Provided Context Files\n\nIMPORTANT: Use this information to understand the optimization goals, design variables, objectives, and constraints:\n\n"
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for filename, content in context.get("context_files", {}).items():
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context_files_section += f"### {filename}\n```\n{content}\n```\n\n"
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# Remove context_files from JSON dump to avoid duplication
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context_for_json = {k: v for k, v in context.items() if k != "context_files"}
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prompt = f"""Generate a README.md for this FEA optimization study.
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## Study Technical Data
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```json
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{json.dumps(context_for_json, indent=2, default=str)}
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```
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{context_files_section}
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## Requirements
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1. Use the EXACT values from the technical data - no placeholders
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2. If context files are provided, extract:
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- Design variable bounds (min/max)
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- Optimization objectives (minimize/maximize what)
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- Constraints (stress limits, etc.)
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- Any specific requirements mentioned
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3. Format the README with these sections:
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- Title (# Study Name)
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- Overview (topic, date, status, description from context)
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- Engineering Problem (what we're optimizing and why - from context files)
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- Model Information (mass, solver, files)
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- Design Variables (if context specifies bounds, include them in a table)
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- Optimization Objectives (from context files)
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- Constraints (from context files)
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- Expressions Found (table of discovered expressions, highlight candidates)
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- Next Steps (what needs to be configured)
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4. Keep it professional and concise
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5. Use proper markdown table formatting
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6. Include units where applicable
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7. For expressions table, show: name, value, units, is_candidate
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Generate ONLY the README.md content in markdown format, no explanations:"""
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return prompt
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def _extract_markdown(self, response: str) -> Optional[str]:
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"""Extract markdown content from Claude response."""
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if not response:
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return None
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# If response starts with #, it's already markdown
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if response.strip().startswith("#"):
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return response.strip()
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# Try to find markdown block
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if "```markdown" in response:
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start = response.find("```markdown") + len("```markdown")
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end = response.find("```", start)
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if end > start:
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return response[start:end].strip()
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if "```md" in response:
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start = response.find("```md") + len("```md")
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end = response.find("```", start)
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if end > start:
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return response[start:end].strip()
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# Look for first # heading
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lines = response.split("\n")
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for i, line in enumerate(lines):
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if line.strip().startswith("# "):
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return "\n".join(lines[i:]).strip()
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return None
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def _generate_fallback_readme(self, study_name: str, spec: Dict[str, Any]) -> str:
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"""Generate a basic README if Claude fails."""
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meta = spec.get("meta", {})
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model = spec.get("model", {})
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introspection = model.get("introspection", {}) or {}
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dvs = spec.get("design_variables", [])
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objs = spec.get("objectives", [])
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cons = spec.get("constraints", [])
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opt = spec.get("optimization", {})
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expressions = introspection.get("expressions", [])
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lines = [
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f"# {study_name.replace('_', ' ').title()}",
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"",
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f"**Topic**: {meta.get('topic', 'Other')}",
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f"**Created**: {meta.get('created', 'Unknown')[:10] if meta.get('created') else 'Unknown'}",
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f"**Status**: {meta.get('status', 'draft')}",
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"",
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]
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if meta.get("description"):
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lines.extend([meta["description"], ""])
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# Model Information
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lines.extend(
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[
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"## Model Information",
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"",
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]
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)
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if introspection.get("mass_kg"):
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lines.append(f"- **Mass**: {introspection['mass_kg']:.2f} kg")
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sim_path = model.get("sim", {}).get("path") if model.get("sim") else None
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if sim_path:
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lines.append(f"- **Simulation**: {sim_path}")
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lines.append("")
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# Expressions Found
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if expressions:
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lines.extend(
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[
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"## Expressions Found",
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"",
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"| Name | Value | Units | Candidate |",
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"|------|-------|-------|-----------|",
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]
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)
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for expr in expressions:
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is_candidate = "✓" if expr.get("is_candidate") else ""
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value = f"{expr.get('value', '-')}"
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units = expr.get("units", "-")
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lines.append(f"| {expr.get('name', '-')} | {value} | {units} | {is_candidate} |")
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lines.append("")
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# Design Variables (if configured)
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if dvs:
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lines.extend(
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[
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"## Design Variables",
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"",
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"| Variable | Expression | Range | Units |",
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"|----------|------------|-------|-------|",
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]
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)
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for dv in dvs:
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bounds = dv.get("bounds", {})
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units = dv.get("units", "-")
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lines.append(
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f"| {dv.get('name', 'Unknown')} | "
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f"{dv.get('expression_name', '-')} | "
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f"[{bounds.get('min', '-')}, {bounds.get('max', '-')}] | "
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f"{units} |"
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)
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lines.append("")
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# Objectives
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if objs:
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lines.extend(
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[
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"## Objectives",
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"",
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"| Objective | Direction | Weight |",
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"|-----------|-----------|--------|",
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]
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)
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for obj in objs:
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lines.append(
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f"| {obj.get('name', 'Unknown')} | "
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f"{obj.get('direction', 'minimize')} | "
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f"{obj.get('weight', 1.0)} |"
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)
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lines.append("")
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# Constraints
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if cons:
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lines.extend(
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[
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"## Constraints",
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"",
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"| Constraint | Condition | Threshold |",
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"|------------|-----------|-----------|",
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]
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)
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for con in cons:
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lines.append(
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f"| {con.get('name', 'Unknown')} | "
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f"{con.get('operator', '<=')} | "
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f"{con.get('threshold', '-')} |"
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)
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lines.append("")
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# Algorithm
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algo = opt.get("algorithm", {})
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budget = opt.get("budget", {})
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lines.extend(
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[
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"## Methodology",
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"",
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f"- **Algorithm**: {algo.get('type', 'TPE')}",
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f"- **Max Trials**: {budget.get('max_trials', 100)}",
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"",
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]
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)
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# Next Steps
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lines.extend(
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[
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"## Next Steps",
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"",
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]
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)
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if not dvs:
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lines.append("- [ ] Configure design variables from discovered expressions")
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if not objs:
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lines.append("- [ ] Define optimization objectives")
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if not dvs and not objs:
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lines.append("- [ ] Open in Canvas Builder to complete configuration")
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else:
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lines.append("- [ ] Run baseline solve to validate setup")
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lines.append("- [ ] Finalize study to move to studies folder")
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lines.append("")
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return "\n".join(lines)
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# Singleton instance
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_generator: Optional[ClaudeReadmeGenerator] = None
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def get_readme_generator() -> ClaudeReadmeGenerator:
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"""Get the singleton README generator instance."""
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global _generator
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if _generator is None:
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_generator = ClaudeReadmeGenerator()
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return _generator
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