File size: 5,431 Bytes
e0265b9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | from __future__ import annotations
import re
from dataclasses import dataclass
from pathlib import Path
from adam.config import ConfigManager
@dataclass(frozen=True, slots=True)
class ToolFolderDefinition:
tool_id: str
name: str
expected_files: tuple[str, ...] = ()
@dataclass(frozen=True, slots=True)
class ToolFolderStatus:
tool_id: str
name: str
path: str
exists: bool
valid: bool
entry_points: tuple[str, ...]
message: str
TOOL_FOLDER_DEFINITIONS = (
ToolFolderDefinition(
"dataset_collector",
"Dataset Collector",
("collector.py", "dataset_collector.py", "main.py"),
),
ToolFolderDefinition(
"caption_generator",
"Caption Generator",
("caption.py", "caption_generator.py", "main.py"),
),
ToolFolderDefinition(
"lora_trainer",
"LoRA Trainer",
("train.py", "lora_train.py", "main.py", "src/loratrainer/main.py"),
),
ToolFolderDefinition(
"ddpm_trainer",
"DDPM Trainer",
("train.py", "appStableDiffusion.py"),
),
ToolFolderDefinition(
"flow_trainer",
"Flow Matching Trainer",
("flow_matching_app.py", "roblox_action_flow_app.py"),
),
ToolFolderDefinition(
"preview_generator",
"Preview Generator",
("generate.py", "preview.py", "main.py"),
),
)
class ToolFolderManager:
def __init__(self, config: ConfigManager) -> None:
self.config = config
self.definitions = {
definition.tool_id: definition
for definition in TOOL_FOLDER_DEFINITIONS
}
def paths(self) -> dict[str, str]:
stored = self.config.get("tool_folders", {})
return dict(stored) if isinstance(stored, dict) else {}
def get(self, tool_id: str) -> str:
return str(self.paths().get(tool_id, ""))
def set(self, tool_id: str, path: str) -> ToolFolderStatus:
if tool_id not in self.definitions:
raise KeyError(f"Unknown tool folder: {tool_id}")
stored = self.paths()
stored[tool_id] = path.strip().strip('"')
self.config.update({"tool_folders": stored})
return self.scan(tool_id)
def update(self, values: dict[str, str]) -> dict[str, ToolFolderStatus]:
stored = self.paths()
for tool_id, value in values.items():
if tool_id in self.definitions:
stored[tool_id] = value.strip().strip('"')
self.config.update({"tool_folders": stored})
return {tool_id: self.scan(tool_id) for tool_id in values}
def scan(self, tool_id: str) -> ToolFolderStatus:
definition = self.definitions[tool_id]
raw_path = self.get(tool_id)
if not raw_path:
return ToolFolderStatus(
tool_id,
definition.name,
"",
False,
False,
(),
"Not configured",
)
folder = Path(raw_path).expanduser()
if not folder.is_dir():
return ToolFolderStatus(
tool_id,
definition.name,
str(folder),
False,
False,
(),
"Folder not found",
)
entry_points = tuple(
filename
for filename in definition.expected_files
if (folder / filename).is_file()
)
if entry_points:
message = f"Detected 路 {', '.join(entry_points)}"
valid = True
else:
top_level_python = sorted(path.name for path in folder.glob("*.py"))
entry_points = tuple(top_level_python[:5])
valid = bool(entry_points)
message = (
f"Python project detected 路 review {entry_points[0]}"
if entry_points
else "Folder found 路 no Python entry point detected"
)
return ToolFolderStatus(
tool_id,
definition.name,
str(folder.resolve()),
True,
valid,
entry_points,
message,
)
def scan_all(self) -> dict[str, ToolFolderStatus]:
return {
tool_id: self.scan(tool_id)
for tool_id in self.definitions
}
def parse_assignments(self, text: str) -> dict[str, str]:
"""Recognize folder assignments pasted into chat without executing them."""
patterns = {
"ddpm_trainer": r"(?im)^\s*DDPM(?:\s+Trainer)?\s*:\s*(.+?)\s*$",
"flow_trainer": (
r"(?im)^\s*Flow(?:\s+Matching)?(?:\s+Trainer)?\s*:\s*(.+?)\s*$"
),
"lora_trainer": r"(?im)^\s*LoRA(?:\s+Trainer)?\s*:\s*(.+?)\s*$",
"dataset_collector": (
r"(?im)^\s*Dataset(?:\s+Collector)?\s*:\s*(.+?)\s*$"
),
"caption_generator": (
r"(?im)^\s*Caption(?:\s+Generator)?\s*:\s*(.+?)\s*$"
),
"preview_generator": (
r"(?im)^\s*Preview(?:\s+Generator)?\s*:\s*(.+?)\s*$"
),
}
assignments: dict[str, str] = {}
for tool_id, pattern in patterns.items():
match = re.search(pattern, text)
if match:
assignments[tool_id] = match.group(1).strip().strip('"')
return assignments
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