Spaces:
Running
Running
File size: 12,496 Bytes
e60fb94 |
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 |
import re
import json
import functools
# Add a simple LRU cache for regex patterns
def get_cached_pattern(pattern, flags=0):
"""Cache compiled regex patterns for better performance."""
@functools.lru_cache(maxsize=32)
def _get_pattern(pattern_str, pattern_flags):
return re.compile(pattern_str, pattern_flags)
return _get_pattern(pattern, flags)
def extract_notebook_info(content):
"""Extract notebook name and description from the AI response."""
# Improved regex pattern that handles multiline and markdown formatting better
name_match = re.search(r"NOTEBOOK_NAME:?\s*(.+?)(?=\n\s*NOTEBOOK_DESCRIPTION|\n\s*---|\n\s*$|$)", content, re.DOTALL)
desc_match = re.search(r"NOTEBOOK_DESCRIPTION:?\s*(.+?)(?=\n\s*---|\n\s*$|$)", content, re.DOTALL)
# Extract and clean up potential markdown formatting
name = name_match.group(1).strip() if name_match else "Generated Notebook"
description = desc_match.group(1).strip() if desc_match else "Notebook generated using NoteGenie"
# Remove markdown formatting from name and description
name = re.sub(r'\*\*(.*?)\*\*', r'\1', name) # Remove bold formatting
name = re.sub(r'\*(.*?)\*', r'\1', name) # Remove italic formatting
name = re.sub(r'_(.*?)_', r'\1', name) # Remove underline formatting
description = re.sub(r'\*\*(.*?)\*\*', r'\1', description) # Remove bold formatting
description = re.sub(r'\*(.*?)\*', r'\1', description) # Remove italic formatting
description = re.sub(r'_(.*?)_', r'\1', description) # Remove underline formatting
return {
"name": name,
"description": description
}
def format_notebook(content):
"""Convert the AI text response into a properly formatted Jupyter notebook JSON.
Optimized for performance with larger texts."""
# Use faster pattern matching approach with improved end-of-file handling
markdown_pattern = get_cached_pattern(r"---\s*MARKDOWN\s*CELL\s*---\s*([\s\S]*?)(?=---\s*(?:MARKDOWN|CODE)\s*CELL\s*---|$)", re.DOTALL)
code_pattern = get_cached_pattern(r"---\s*CODE\s*CELL\s*---\s*```python\s*([\s\S]*?)```", re.DOTALL)
cell_marker_pattern = get_cached_pattern(r"---\s*(MARKDOWN|CODE)\s*CELL\s*---", re.DOTALL)
# OPTIMIZATION: Do a quick initial scan to determine notebook size and complexity
complexity = len(content) // 1000 # Rough estimate based on content length
cell_count = len(cell_marker_pattern.findall(content))
# For very large notebooks, use a more memory-efficient but slower approach
if complexity > 200 or cell_count > 50: # If over ~200KB or 50 cells
return format_large_notebook(content)
# For regular notebooks, use the standard approach which is faster for medium-sized content
try:
# Extract cells from the content in a single pass if possible
markdown_cells = markdown_pattern.findall(content)
code_cells = code_pattern.findall(content)
# If the AI didn't use the expected format, try alternate patterns
if not markdown_cells and not code_cells:
# Simplified handling for non-standard format
sections = re.split(r"```python|```", content)
cells = []
for i, section in enumerate(sections):
section = section.strip()
if section and i % 2 == 0:
# This is markdown content
cells.append({"cell_type": "markdown", "source": section})
elif section:
# This is code content
cells.append({"cell_type": "code", "source": section})
else:
# Interleave markdown and code cells in the correct order
cells = []
# Find overall ordering of cells
all_matches = list(cell_marker_pattern.finditer(content))
all_types = [m.group(1) for m in all_matches]
md_idx = 0
code_idx = 0
for i, cell_type in enumerate(all_types):
marker = all_matches[i]
marker_end = marker.end()
next_marker_start = all_matches[i+1].start() if i+1 < len(all_matches) else len(content)
cell_content = content[marker_end:next_marker_start].strip()
if cell_type == "MARKDOWN":
if md_idx < len(markdown_cells) or (i == len(all_types) - 1 and cell_content):
if md_idx < len(markdown_cells):
cell_source = markdown_cells[md_idx].strip()
md_idx += 1
else:
# Handle the last markdown cell if it wasn't captured by the pattern
cell_source = cell_content
cells.append({
"cell_type": "markdown",
"source": cell_source
})
elif cell_type == "CODE":
if code_idx < len(code_cells) or (i == len(all_types) - 1 and "```python" in cell_content):
if code_idx < len(code_cells):
cell_source = code_cells[code_idx].strip()
code_idx += 1
else:
# Handle the last code cell if it wasn't captured by the pattern
code_match = re.search(r"```python\s*([\s\S]*?)```", cell_content, re.DOTALL)
cell_source = code_match.group(1).strip() if code_match else ""
cells.append({
"cell_type": "code",
"source": cell_source
})
# Ensure we have at least a title cell if nothing was extracted
if not cells:
notebook_info = extract_notebook_info(content)
cells.append({
"cell_type": "markdown",
"source": f"# {notebook_info['name']}\n\n{notebook_info['description']}"
})
# Try to extract any code blocks that might be present - only if needed
code_blocks = re.findall(r"```python\s*(.*?)```", content, re.DOTALL)
for block in code_blocks:
cells.append({
"cell_type": "code",
"source": block.strip()
})
# Format cells for Jupyter notebook structure - optimize by processing in chunks
formatted_cells = []
for cell in cells:
cell_source = cell["source"]
# Only split if it's a string, not if it's already a list
if isinstance(cell_source, str):
# OPTIMIZATION: For very large cells, process line by line to avoid memory issues
if len(cell_source) > 10000: # If cell is over 10KB
source_lines = []
for line in cell_source.splitlines():
source_lines.append(line)
else:
source_lines = cell_source.split("\n")
else:
source_lines = cell_source
formatted_cell = {
"cell_type": cell["cell_type"],
"metadata": {},
"source": source_lines
}
if cell["cell_type"] == "code":
formatted_cell["execution_count"] = None
formatted_cell["outputs"] = []
formatted_cells.append(formatted_cell)
# Create the notebook structure
notebook = {
"cells": formatted_cells,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.8.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
return notebook
except Exception as e:
# If standard approach fails, fall back to the more robust method
print(f"Error in standard format_notebook: {e}. Using fallback method.")
return format_large_notebook(content)
def format_large_notebook(content):
"""Memory-efficient formatter for very large notebooks.
Processes content in chunks to avoid memory issues."""
# Get notebook info
notebook_info = extract_notebook_info(content)
# Initialize cells with the title
cells = [{
"cell_type": "markdown",
"metadata": {},
"source": [f"# {notebook_info['name']}", "", notebook_info['description']]
}]
# Process content in chunks using incremental parsing
# Find cell markers and their positions
marker_positions = []
for match in re.finditer(r"---\s*(MARKDOWN|CODE)\s*CELL\s*---", content):
marker_positions.append((match.start(), match.end(), match.group(1)))
# If no markers are found, try to extract code blocks directly
if not marker_positions:
# Just extract code blocks and treat everything else as markdown
remaining_text = content
last_end = 0
for match in re.finditer(r"```python\s*(.*?)```", content, re.DOTALL):
# If there's text before this code block, add it as markdown
if match.start() > last_end:
markdown_text = content[last_end:match.start()].strip()
if markdown_text:
cells.append({
"cell_type": "markdown",
"metadata": {},
"source": markdown_text.split("\n")
})
# Add the code block
code_text = match.group(1).strip()
if code_text:
cells.append({
"cell_type": "code",
"metadata": {},
"source": code_text.split("\n"),
"execution_count": None,
"outputs": []
})
last_end = match.end()
# If there's text after the last code block, add it as markdown
if last_end < len(content):
markdown_text = content[last_end:].strip()
if markdown_text:
cells.append({
"cell_type": "markdown",
"metadata": {},
"source": markdown_text.split("\n")
})
else:
# Process each cell based on its markers
for i, (start, end, cell_type) in enumerate(marker_positions):
# Find the end of this cell (start of next cell or end of content)
cell_end = marker_positions[i+1][0] if i+1 < len(marker_positions) else len(content)
cell_content = content[end:cell_end].strip()
if cell_type == "MARKDOWN":
cells.append({
"cell_type": "markdown",
"metadata": {},
"source": cell_content.split("\n")
})
elif cell_type == "CODE":
# Extract code from between triple backticks
code_match = re.search(r"```python\s*(.*?)```", cell_content, re.DOTALL)
if code_match:
code_text = code_match.group(1).strip()
cells.append({
"cell_type": "code",
"metadata": {},
"source": code_text.split("\n"),
"execution_count": None,
"outputs": []
})
# Create the notebook structure
notebook = {
"cells": cells,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.8.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
return notebook
|