File size: 16,939 Bytes
6a911c8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 |
"""
File processing utilities for handling paper files and related operations.
"""
import json
import os
import re
from typing import Dict, List, Optional, Union
class FileProcessor:
"""
A class to handle file processing operations including path extraction and file reading.
"""
@staticmethod
def extract_file_path(file_info: Union[str, Dict]) -> Optional[str]:
"""
Extract paper directory path from the input information.
Args:
file_info: Either a JSON string or a dictionary containing file information
Returns:
Optional[str]: The extracted paper directory path or None if not found
"""
try:
# Handle direct file path input
if isinstance(file_info, str):
# Check if it's a file path (existing or not)
if file_info.endswith(
(".md", ".pdf", ".txt", ".docx", ".doc", ".html", ".htm")
):
# It's a file path, return the directory
return os.path.dirname(os.path.abspath(file_info))
elif os.path.exists(file_info):
if os.path.isfile(file_info):
return os.path.dirname(os.path.abspath(file_info))
elif os.path.isdir(file_info):
return os.path.abspath(file_info)
# Try to parse as JSON
try:
info_dict = json.loads(file_info)
except json.JSONDecodeError:
# 尝试从文本中提取JSON
info_dict = FileProcessor.extract_json_from_text(file_info)
if not info_dict:
# If not JSON and doesn't look like a file path, raise error
raise ValueError(
f"Input is neither a valid file path nor JSON: {file_info}"
)
else:
info_dict = file_info
# Extract paper path from dictionary
paper_path = info_dict.get("paper_path")
if not paper_path:
raise ValueError("No paper_path found in input dictionary")
# Get the directory path instead of the file path
paper_dir = os.path.dirname(paper_path)
# Convert to absolute path if relative
if not os.path.isabs(paper_dir):
paper_dir = os.path.abspath(paper_dir)
return paper_dir
except (AttributeError, TypeError) as e:
raise ValueError(f"Invalid input format: {str(e)}")
@staticmethod
def find_markdown_file(directory: str) -> Optional[str]:
"""
Find the first markdown file in the given directory.
Args:
directory: Directory path to search
Returns:
Optional[str]: Path to the markdown file or None if not found
"""
if not os.path.isdir(directory):
return None
for file in os.listdir(directory):
if file.endswith(".md"):
return os.path.join(directory, file)
return None
@staticmethod
def parse_markdown_sections(content: str) -> List[Dict[str, Union[str, int, List]]]:
"""
Parse markdown content and organize it by sections based on headers.
Args:
content: The markdown content to parse
Returns:
List[Dict]: A list of sections, each containing:
- level: The header level (1-6)
- title: The section title
- content: The section content
- subsections: List of subsections
"""
# Split content into lines
lines = content.split("\n")
sections = []
current_section = None
current_content = []
for line in lines:
# Check if line is a header
header_match = re.match(r"^(#{1,6})\s+(.+)$", line)
if header_match:
# If we were building a section, save its content
if current_section is not None:
current_section["content"] = "\n".join(current_content).strip()
sections.append(current_section)
# Start a new section
level = len(header_match.group(1))
title = header_match.group(2).strip()
current_section = {
"level": level,
"title": title,
"content": "",
"subsections": [],
}
current_content = []
elif current_section is not None:
current_content.append(line)
# Don't forget to save the last section
if current_section is not None:
current_section["content"] = "\n".join(current_content).strip()
sections.append(current_section)
return FileProcessor._organize_sections(sections)
@staticmethod
def _organize_sections(sections: List[Dict]) -> List[Dict]:
"""
Organize sections into a hierarchical structure based on their levels.
Args:
sections: List of sections with their levels
Returns:
List[Dict]: Organized hierarchical structure of sections
"""
result = []
section_stack = []
for section in sections:
while section_stack and section_stack[-1]["level"] >= section["level"]:
section_stack.pop()
if section_stack:
section_stack[-1]["subsections"].append(section)
else:
result.append(section)
section_stack.append(section)
return result
@staticmethod
async def read_file_content(file_path: str) -> str:
"""
Read the content of a file asynchronously.
Args:
file_path: Path to the file to read
Returns:
str: The content of the file
Raises:
FileNotFoundError: If the file doesn't exist
IOError: If there's an error reading the file
"""
try:
# Ensure the file exists
if not os.path.exists(file_path):
raise FileNotFoundError(f"File not found: {file_path}")
# Check if file is actually a PDF by reading the first few bytes
with open(file_path, "rb") as f:
header = f.read(8)
if header.startswith(b"%PDF"):
raise IOError(
f"File {file_path} is a PDF file, not a text file. Please convert it to markdown format or use PDF processing tools."
)
# Read file content
# Note: Using async with would be better for large files
# but for simplicity and compatibility, using regular file reading
with open(file_path, "r", encoding="utf-8") as f:
content = f.read()
return content
except UnicodeDecodeError as e:
raise IOError(
f"Error reading file {file_path}: File encoding is not UTF-8. Original error: {str(e)}"
)
except Exception as e:
raise IOError(f"Error reading file {file_path}: {str(e)}")
@staticmethod
def format_section_content(section: Dict) -> str:
"""
Format a section's content with standardized spacing and structure.
Args:
section: Dictionary containing section information
Returns:
str: Formatted section content
"""
# Start with section title
formatted = f"\n{'#' * section['level']} {section['title']}\n"
# Add section content if it exists
if section["content"]:
formatted += f"\n{section['content'].strip()}\n"
# Process subsections
if section["subsections"]:
# Add a separator before subsections if there's content
if section["content"]:
formatted += "\n---\n"
# Process each subsection
for subsection in section["subsections"]:
formatted += FileProcessor.format_section_content(subsection)
# Add section separator
formatted += "\n" + "=" * 80 + "\n"
return formatted
@staticmethod
def standardize_output(sections: List[Dict]) -> str:
"""
Convert structured sections into a standardized string format.
Args:
sections: List of section dictionaries
Returns:
str: Standardized string output
"""
output = []
# Process each top-level section
for section in sections:
output.append(FileProcessor.format_section_content(section))
# Join all sections with clear separation
return "\n".join(output)
@classmethod
async def process_file_input(
cls, file_input: Union[str, Dict], base_dir: str = None
) -> Dict:
"""
Process file input information and return the structured content.
Args:
file_input: File input information (JSON string, dict, or direct file path)
base_dir: Optional base directory to use for creating paper directories (for sync support)
Returns:
Dict: The structured content with sections and standardized text
"""
try:
# 首先尝试从字符串中提取markdown文件路径
if isinstance(file_input, str):
import re
file_path_match = re.search(r"`([^`]+\.md)`", file_input)
if file_path_match:
paper_path = file_path_match.group(1)
file_input = {"paper_path": paper_path}
# Extract paper directory path
paper_dir = cls.extract_file_path(file_input)
# If base_dir is provided, adjust paper_dir to be relative to base_dir
if base_dir and paper_dir:
# If paper_dir is using default location, move it to base_dir
if paper_dir.endswith(("deepcode_lab", "agent_folders")):
paper_dir = base_dir
else:
# Extract the relative part and combine with base_dir
paper_name = os.path.basename(paper_dir)
# 保持原始目录名不变,不做任何替换
paper_dir = os.path.join(base_dir, "papers", paper_name)
# Ensure the directory exists
os.makedirs(paper_dir, exist_ok=True)
if not paper_dir:
raise ValueError("Could not determine paper directory path")
# Get the actual file path
file_path = None
if isinstance(file_input, str):
# 尝试解析为JSON(处理下载结果)
try:
parsed_json = json.loads(file_input)
if isinstance(parsed_json, dict) and "paper_path" in parsed_json:
file_path = parsed_json.get("paper_path")
# 如果文件不存在,尝试查找markdown文件
if file_path and not os.path.exists(file_path):
paper_dir = os.path.dirname(file_path)
if os.path.isdir(paper_dir):
file_path = cls.find_markdown_file(paper_dir)
if not file_path:
raise ValueError(
f"No markdown file found in directory: {paper_dir}"
)
else:
raise ValueError("Invalid JSON format: missing paper_path")
except json.JSONDecodeError:
# 尝试从文本中提取JSON(处理包含额外文本的下载结果)
extracted_json = cls.extract_json_from_text(file_input)
if extracted_json and "paper_path" in extracted_json:
file_path = extracted_json.get("paper_path")
# 如果文件不存在,尝试查找markdown文件
if file_path and not os.path.exists(file_path):
paper_dir = os.path.dirname(file_path)
if os.path.isdir(paper_dir):
file_path = cls.find_markdown_file(paper_dir)
if not file_path:
raise ValueError(
f"No markdown file found in directory: {paper_dir}"
)
else:
# 不是JSON,按文件路径处理
# Check if it's a file path (existing or not)
if file_input.endswith(
(".md", ".pdf", ".txt", ".docx", ".doc", ".html", ".htm")
):
if os.path.exists(file_input):
file_path = file_input
else:
# File doesn't exist, try to find markdown in the directory
file_path = cls.find_markdown_file(paper_dir)
if not file_path:
raise ValueError(
f"No markdown file found in directory: {paper_dir}"
)
elif os.path.exists(file_input):
if os.path.isfile(file_input):
file_path = file_input
elif os.path.isdir(file_input):
# If it's a directory, find the markdown file
file_path = cls.find_markdown_file(file_input)
if not file_path:
raise ValueError(
f"No markdown file found in directory: {file_input}"
)
else:
raise ValueError(f"Invalid input: {file_input}")
else:
# Dictionary input
file_path = file_input.get("paper_path")
# If the file doesn't exist, try to find markdown in the directory
if file_path and not os.path.exists(file_path):
paper_dir = os.path.dirname(file_path)
if os.path.isdir(paper_dir):
file_path = cls.find_markdown_file(paper_dir)
if not file_path:
raise ValueError(
f"No markdown file found in directory: {paper_dir}"
)
if not file_path:
raise ValueError("No valid file path found")
# Read file content
content = await cls.read_file_content(file_path)
# Parse and structure the content
structured_content = cls.parse_markdown_sections(content)
# Generate standardized text output
standardized_text = cls.standardize_output(structured_content)
return {
"paper_dir": paper_dir,
"file_path": file_path,
"sections": structured_content,
"standardized_text": standardized_text,
}
except Exception as e:
raise ValueError(f"Error processing file input: {str(e)}")
@staticmethod
def extract_json_from_text(text: str) -> Optional[Dict]:
"""
Extract JSON from text that may contain markdown code blocks or other content.
Args:
text: Text that may contain JSON
Returns:
Optional[Dict]: Extracted JSON as dictionary or None if not found
"""
import re
# Try to find JSON in markdown code blocks
json_pattern = r"```json\s*(\{.*?\})\s*```"
match = re.search(json_pattern, text, re.DOTALL)
if match:
try:
return json.loads(match.group(1))
except json.JSONDecodeError:
pass
# Try to find standalone JSON
json_pattern = r"(\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\})"
matches = re.findall(json_pattern, text, re.DOTALL)
for match in matches:
try:
parsed = json.loads(match)
if isinstance(parsed, dict) and "paper_path" in parsed:
return parsed
except json.JSONDecodeError:
continue
return None
|