# ∞ HTML Repair ## Documentation - Class name: `LLMHtmlRepair` - Category: `SALT/Language Toolkit/Tools/HTML` - Output node: `False` The LLMHtmlRepair node leverages a language model to analyze and correct malformed HTML content, ensuring the output is well-structured and valid HTML without data loss. ## Input types ### Required - **`llm_model`** - Specifies the language model to use for repairing the HTML. It's crucial for interpreting the input and generating the corrected HTML. - Comfy dtype: `LLM_MODEL` - Python dtype: `Dict[str, Any]` - **`text_input`** - The malformed HTML content that needs to be repaired. It serves as the primary input for the correction process. - Comfy dtype: `STRING` - Python dtype: `str` ### Optional - **`extra_directions`** - Optional additional instructions for the language model, guiding the repair process with more specificity. - Comfy dtype: `STRING` - Python dtype: `str` ## Output types - **`html_output`** - Comfy dtype: `STRING` - The repaired HTML content, which is well-structured and valid, ensuring no data is omitted. - Python dtype: `str` ## Usage tips - Infra type: `GPU` - Common nodes: unknown ## Source code ```python class LLMHtmlRepair: @classmethod def INPUT_TYPES(cls): return { "required": { "llm_model": ("LLM_MODEL",), "text_input": ("STRING", {"multiline": True, "dynamicPrompts": False, "placeholder": "Malformed HTML..."}), }, "optional": { "extra_directions": ("STRING", {"multiline": True, "dynamicPrompts": False, "placeholder": "Extra directions for the LLM to follow..."}), } } RETURN_TYPES = ("STRING",) RETURN_NAMES = ("html_output",) FUNCTION = "repair_html" CATEGORY = f"{MENU_NAME}/{SUB_MENU_NAME}/Tools/HTML" def repair_html(self, llm_model, text_input, extra_directions=""): prompt = ( f"{text_input}\n\n###\n\n" "Given the above malformed HTML, please inspect it and repair it so that it's valid HTML, without changing or losing any data if possible." f"{extra_directions}\n\n" "Please ensure the HTML output is well-structured, valid,, and does not omit any data." ) response = llm_model['llm'].complete(prompt) return (response.text,) ```