refactor

changedetectionio/diff/tokenizers/words_and_html.py

@@ -0,0 +1,61 @@
+"""
+Tokenizer that preserves HTML tags as atomic units while splitting on whitespace.
+
+This tokenizer is specifically designed for HTML content where:
+- HTML tags should remain intact (e.g., '<p>', '<a href="...">')
+- Whitespace tokens are preserved for accurate diff reconstruction
+- Words are split on whitespace boundaries
+"""
+
+from typing import List
+
+
+def tokenize_words_and_html(text: str) -> List[str]:
+    """
+    Split text into words and boundaries (spaces, HTML tags).
+
+    This tokenizer preserves HTML tags as atomic units while splitting on whitespace.
+    Useful for content that contains HTML markup.
+
+    Args:
+        text: Input text to tokenize
+
+    Returns:
+        List of tokens (words, spaces, HTML tags)
+
+    Examples:
+        >>> tokenize_words_and_html("<p>Hello world</p>")
+        ['<p>', 'Hello', ' ', 'world', '</p>']
+        >>> tokenize_words_and_html("<a href='test.com'>link</a>")
+        ['<a href=\\'test.com\\'>', 'link', '</a>']
+    """
+    tokens = []
+    current = ''
+    in_tag = False
+
+    for char in text:
+        if char == '<':
+            # Start of HTML tag
+            if current:
+                tokens.append(current)
+                current = ''
+            current = '<'
+            in_tag = True
+        elif char == '>' and in_tag:
+            # End of HTML tag
+            current += '>'
+            tokens.append(current)
+            current = ''
+            in_tag = False
+        elif char.isspace() and not in_tag:
+            # Space outside of tag
+            if current:
+                tokens.append(current)
+                current = ''
+            tokens.append(char)
+        else:
+            current += char
+
+    if current:
+        tokens.append(current)
+    return tokens