--- title: "Refactoring with AI: A Developer's Journey" permalink: /futureproof/refactoring-ai-css-cleanup/ meta_description: "A weekend refactor journey: breaking out plugins, leveraging AI with prompt_foo.py, and automating CSS cleanup. Despite challenges, the server's running and UI looks good—a solid foundation for deeper style refactoring." meta_keywords: css refactoring, python, fasthtml, pipulate, inline styles, ast, abstract syntax tree, ai code assistant, gemini, code cleanup, static/styles.css, server.py, plugin architecture, prompt_foo.py, analyze_styles.py, refactor_inline_styles_to_cls.py, pico.css, developer tooling, software development, web development, iterative process, modular design, self-organizing systems, token limits, prompt engineering description: This entry captures my recent deep dive into refactoring Pipulate, a process significantly amplified by FastHTML's simplicity and the emerging power of AI. I've been focusing on organizing the codebase by breaking out plugins and implementing a role-based system, all while wrestling with the temptation to mix concerns that FastHTML allows. A major part of this was figuring out how to best leverage AI for iterative improvements, specifically for cleaning up inline CSS. I developed a Python script, `prompt_foo.py`, to meticulously prepare context for AI tools like Gemini, ensuring they get the "why" behind my requests. This led to a detailed, AI-generated strategy for CSS refactoring which is mostly complete by the end of this article. layout: post sort_order: 1 --- ## Understanding AI-Driven Development & Code Refinement: A Case Study Modern software development is increasingly a partnership between human ingenuity and artificial intelligence. This article extract offers a candid look into this evolving dynamic, specifically through the lens of refactoring a web application's styling (CSS). As projects grow, initially convenient coding practices, like embedding styles directly within HTML-generating code, can become cumbersome and hard to maintain. The challenge then becomes how to transition to cleaner, more organized approaches—like using dedicated stylesheets and reusable classes—without breaking existing functionality or derailing project momentum. This journal entry documents a developer's real-time efforts to tackle this very issue within "Pipulate," a Python-based web framework built with FastHTML. It highlights how simpler frameworks can lower the barrier to web development but also how they might initially encourage "mixed concerns" (like intermingling presentation with logic). The narrative then explores a practical, AI-assisted strategy to analyze and refactor these inline CSS styles, using custom Python scripts and interaction with an AI model (Gemini) to achieve a more robust and maintainable codebase. This process, including the trial-and-error, is invaluable for understanding how AI can act as a powerful assistant in the iterative refinement of software. --- ## AI's Amplification of Personal Agency in Development AI has fundamentally changed development by creating a direct bridge between abstract ideas and concrete implementation. This shift represents a significant enhancement of developer agency. Take my Pipulate project as an example: web development has become much more accessible because FastHTML eliminates the need for extensive custom JavaScript work and removes the CSS build process (compilation) that became standard after SASS (Syntactically Awesome Style Sheets) emerged as part of the full web stack (NodeJS and its associated complexity). But the accessiblity of web tech again isn't the AI story here. That just sets the stage... for code needing cleanup! ## The FastHTML Effect: Returning to Web Development I got out of webdev, hopped off the evermore rapidly spinning hamster-wheel of tech and didn't get back on again until FastHTML. And I got back on again because FastHTML and not AI. But now that I am back onboard webdev with FastHTML *it encourages the mixing of concerns.* In other words, you don't really even need broken out `styles.css` or `scripts.js`. It can all just be in your `server.py` file. Yup. Mixed concerns. And mix concerns, I did. And now I'm concerned. ## Weekend Refactor: Modularizing Pipulate with Plugins and Roles My weekend was supposed to be carved out for slamming out a couple of new workflows, and I still might actually get to that, but instead of busy beaver work, I've been working on accelerators. Passes of simplifying and deeper understanding of my own code, and so with ever-simpler and better understood code comes more control, and with more control comes more speed and acceleration of the type I need to *slam out new workflows*. And so instead, my weekend became a bit of a calculated risk in simplification and understanding. And so far this weekend I broke the last remaining embedded plugin out of core, and the part of Pipulate that controls user profiles that was in `server.py` is now broken out as `000_profiles.py` in the `plugins` folder. In addition, I added `010_roles.py` and made each plugin belong to a particular role: - Core - Botify Employee - Tutorial - Developer - SEO Practitioner ## Self-Organizing Systems: Using Pipulate to Manage Pipulate At least one of these *roles* is now embedded into each of the plugin files in the `plugins` directory of Pipulate. And so now the APP dropdown menu is controlled by a standard plugin app like the tasks or todo-list. It's a list of roles that's generated from out of the plugins discovered at initialization time, with Core and Botify Employee always being automatically checked, which controls the dropdown menus. If you want to expose the Tutorial workflows, you go into the Roles app now and check Tutorials and they appear on the dropdown menu. Likewise for the Developer plugins. This is part of getting organized and using the system to control itself. This system-organizing-itself is also a principle like in the world of SQL where SQL system-tables controls the SQL system. I guess it's pretty common and only logical practice when you build a system for organization that the same said system is used to organize itself. Features of the product being used to control features of the product. It'd be silly if I didn't use it this way, and so breaking out Profiles into a proper plugin app and a new plugin app for Roles, and the customizations of each and references to them in `server.py` in order to make them have the right behavior and control the system. ## The Iterative Refinement Loop: From Idea to Clean Code There was a lot of refactoring in all that and it was so intense there were no public-facing articles for 2 days as Friday and Saturday were basically intensive refactoring and my new remarkable iterative process, which brings us full back around to how I started this journal entry: going from abstract to implementation. I can do iterative sweeps to simplify the system. I can jump in first taking advantage of FastHTML's rapid mock-up efficiency that strips away all the complexity of the full web stack, allowing me to mold a system like clay. However at the end of that process I've got embedded inline CSS styles all over the place. I also have a couple of JavaScript files that should probably be unified but aren't because of Python parameterization when they're called. I'm itching to address the former to really tidy up the code throughout, and I am going to defer the later because messing with the JavaScript — the bit that still has to exist as standalone JavaScript and hasn't faded into HTMX — feels like refactoring and I've got refactoring fatigue after these 2 days. ## The `prompt_foo.py` Strategy: Engineering AI Understanding Oh yeah, what that rapid tiny chisel-strike AI-assisted iterative refactoring process is... that remarkable process I'm using now... `prompt_foo.py`! Let's use it now to gently clean up and consolidate my inline CSS styles in a non-breaking way. First, we write an article like this. We craft the story. We crate the super-context to give the AI coding assistants the "why" in addition to asking for the "how". Ya hear that, Gemini? Okay, let's be a bit more precise about it. Help me clean up my CSS patterns. I'm looking for a very easy to implement 80/20-rule win. That's 80% of the benefit from the first 20% of the effort. It should be safe and non-breaking. It should be something I can apply easily with either implementation instructions to an AI coding assistant in Cursor or the use of a Python helper script, sed or that sort of thing. I also have the recursive find/replace built into Cursor (VSCode). Emphasis on safe and non-breaking and light touch for big wins. I want to maintain the same look for the CSS for the most part. Consolidation should wrap it up into CSS styles, constants for server.py and that sort of thing. Give me a strategy, or better yet, a Python script. And with that, I gather up the context. There's tons of ways to give AIs full context these days, but I find them rather willy nilly. You can do it directly in Cursor AI for example just using their various methods of putting it in context. But what about order-sensitivity? What about *designing the reveal* for the AI: first look at this, then look at that. That's precisely what I do with `prompt_foo.py`, picking the files from among not only the git repo I'm working on, but from wherever else in my system. In this case: ```python FILES_TO_INCLUDE = """\ README.md flake.nix requirements.txt server.py /home/mike/repos/pipulate/static/chat-interface.js /home/mike/repos/pipulate/static/chat-scripts.js /home/mike/repos/pipulate/static/pico.css /home/mike/repos/pipulate/static/styles.css /home/mike/repos/pipulate/static/widget-scripts.js /home/mike/repos/pipulate/plugins/000_profiles.py /home/mike/repos/pipulate/plugins/010_roles.py /home/mike/repos/pipulate/plugins/020_tasks.py /home/mike/repos/pipulate/plugins/520_widget_examples.py /home/mike/repos/pipulate/helpers/create_workflow.py /home/mike/repos/pipulate/helpers/splice_workflow_step.py """.strip().splitlines() ``` ...which gathers it all up and puts it in an XML wrapper per Anthropic's articles on the XML schematics and semantics work well. Here's the overall structure: ```python output_xml = f'\n\n{create_xml_element("manifest", manifest)}\n{create_xml_element("pre_prompt", pre_prompt)}\n{create_xml_element("content", "\n".join(lines))}\n{create_xml_element("post_prompt", post_prompt)}\n{create_xml_element("token_summary", [ f"{format_token_count(token_counts['total'])}", f"{format_token_count(token_counts['files'])}", f"{format_token_count(prompt_tokens)}" ])}\n' ``` Here's the schematic XML, with explanations for each part: ```xml Runtime
Python 3.12 in a Nix-managed virtualenv (.venv)
 Package Management
Hybrid approach using Nix flakes for system dependencies + pip for Python packages
 This codebase uses a hybrid approach with Nix for system dependencies and virtualenv for Python packages. Now that you've reviewed the codebase context, I'd love your insights and analysis! Dear AI Assistant: I've provided you with the core architecture of a Python web application that takes an interesting approach to modern web development. I'd appreciate your thoughtful analysis on any of these aspects: Technical Architecture Analysis How does this hybrid Nix+virtualenv approach compare to other deployment patterns? What are the tradeoffs of using HTMX with server-side state vs traditional SPAs? How does the plugin system architecture enable extensibility? Pattern Recognition & Insights What patterns emerge from the codebase that surprise you? How does this approach to web development differ from current trends? What potential scaling challenges or opportunities do you see? Forward-Looking Perspective How does this architecture align with or diverge from emerging web development patterns? What suggestions would you make for future evolution of the system? How might this approach need to adapt as web technologies advance? The interplay between modern and traditional web development approaches Architectural decisions that stand out as novel or counterintuitive Potential implications for developer experience and system maintenance ``` **Key Points on XML Construction:** * The `create_xml_element(tag, content)` function wraps the `content` (which can be a string or a list of strings joined by newlines) within the given `tag`. If `content` is already an XML string, it will be nested as such. * The `FILES_TO_INCLUDE` variable at the top of the script determines which files are processed and included in the `` and later in the `` block. * The specific content of `` and `` depends on the template selected (via the `-t` argument, defaulting to template 0). The script has two templates defined. * The `` block is a bit unusual because it re-embeds the string content of the manifest XML and the pre/post prompts as text, along with the actual file contents. * Some inner XML tags within the templates (like `` or `<area>` inside `focus_areas`) are defined as strings directly in the Python `prompt_templates` list (e.g., `"<title>Technical Architecture Analysis"`) rather than being built with `create_xml_element`. This means they appear literally as those strings within their parent XML elements. I've used `pattern_tag_content`, `title_tag_content`, and `area_tag_content` in the schematic to highlight where the script's string literals would be, to avoid confusion with actual nested XML elements that would be built by `create_xml_element`. The script's parsing logic in `print_structured_output` handles these string-embedded tags. And finally, there's the console output from `prompt_foo.py` that shows me what I'm about to feed to some poor unexpecting frontier AI model (I joke — Gemini must expect this of me by now). [mike@nixos:~/repos/pipulate/helpers]$ python prompt_foo.py --prompt prompt.md Using prompt file: /home/mike/repos/pipulate/helpers/prompt.md Using template 1: Material Analysis Mode Output will be written to: foo.txt Using template 1: Material Analysis Mode Output will be written to: foo.txt === Prompt Structure === --- Pre-Prompt --- System Information: You are about to review a codebase and related documentation. Please study and understand the provided materials thoroughly before responding. Key Points: • Focus on understanding the architecture and patterns in the codebase • Note how existing patterns could be leveraged in your response • Consider both technical and conceptual aspects in your analysis --- Files Included --- • README.md (6,900 tokens) • flake.nix (5,301 tokens) • requirements.txt (125 tokens) • server.py (38,074 tokens) • /home/mike/repos/pipulate/static/chat-interface.js (826 tokens) • /home/mike/repos/pipulate/static/chat-scripts.js (1,212 tokens) • /home/mike/repos/pipulate/static/pico.css (27,719 tokens) • /home/mike/repos/pipulate/static/styles.css (559 tokens) • /home/mike/repos/pipulate/static/widget-scripts.js (1,990 tokens) • /home/mike/repos/pipulate/plugins/000_profiles.py (2,998 tokens) • /home/mike/repos/pipulate/plugins/010_roles.py (2,333 tokens) • /home/mike/repos/pipulate/plugins/020_tasks.py (2,573 tokens) • /home/mike/repos/pipulate/plugins/520_widget_examples.py (25,995 tokens) • /home/mike/repos/pipulate/helpers/create_workflow.py (1,914 tokens) • /home/mike/repos/pipulate/helpers/splice_workflow_step.py (2,586 tokens) • /home/mike/repos/pipulate/helpers/prompt.md (1,271 tokens) --- Post-Prompt --- --- Token Summary --- Total tokens: 122,786 tokens tokens === End Prompt Structure === Output written to 'foo.txt' Output copied to clipboard Script finished. Now the thing to really take notice of with the command-line output is the **Total tokens** count, which is ~123K tokens. Now I find that the submit fails at right around this point. Sometimes it's as low as 100K tokens. Sometimes I can squeeze it up to 130K tokens. And these tokens are by the way of the sort reported by OpenAI's PyPI `tiktoken` utility. And when Gemini 2.5 is asked directly about how many tokens can be submitted to it in one go, it's somewhat out of sync with what the reality of testing and hands-on use proves: **Gemini**: Ah, you're referring to the capabilities of Gemini 2.5 Pro, specifically when accessed through the Gemini Advanced interface! For Gemini Advanced users utilizing the Gemini 2.5 Pro (experimental/preview) model, the context window for a single submission is **1 million tokens**. Here's a breakdown of what that means: * **Large Input Capacity:** This 1-million-token window allows you to input a vast amount of information in a single go. This could be: * Around 750,000 words or roughly 1,500 pages of text. * Up to 30,000 lines of code. * Significant amounts of multimodal data like audio, images, and video (though the exact token calculation for these varies). * **Output Limit:** While the input context is very large, the **maximum output tokens** for Gemini 2.5 Pro (preview version `gemini-2.5-pro-preview-05-06`) is **65,536 tokens**. This is still a substantial amount of output. * **How it Works:** You can provide a large amount of context (up to 1 million tokens) in your prompt, and the model will consider all of that information when generating its response (up to 65,536 tokens). **Important Considerations:** * **Preview Model:** Since you mentioned "Gemini Advanced 2.5 Pro (preview)," it's important to remember that features and limits for preview models can sometimes change as they move towards wider availability. * **Interface vs. API:** The way tokens and file uploads are handled can sometimes differ slightly between the direct Gemini Advanced web/app interface and the Gemini API (used by developers in Google AI Studio or Vertex AI). For instance, the Gemini web app might have limits on the number of files you can upload at once (e.g., 10 PDFs), even if the total token count of those files is within the 1 million limit. To fully utilize the entire 1 million token window with many files, users sometimes turn to API-based tools like Google AI Studio. * **Workspace Variations:** There have been some user reports and discussions indicating that Gemini Advanced features, including the effective context window for models like 2.5 Pro, might have different behavior or limitations when accessed through a Google Workspace (business) account compared to a personal Gemini Advanced subscription. Some Workspace users have reported encountering smaller effective context limits (e.g., 32k tokens) for 2.5 Pro. * **"Coming Soon" for 2 Million:** While the underlying Gemini 1.5 Pro (and potentially future iterations of 2.5 Pro) has demonstrated capabilities up to 2 million tokens, the currently advertised and available limit for Gemini 2.5 Pro in Gemini Advanced is 1 million tokens. The 2 million token capability for Gemini 1.5 Pro is becoming more widely available via the API. So, in a single submit via Gemini Advanced with 2.5 Pro, you can leverage that impressive **1 million token context window** for your input. --- ## Token Window-size Not Equal To Single Submit Size **Gemini**: "So, in a single submit via Gemini Advanced with 2.5 Pro, you can leverage that impressive **1 million token context window** for your input." > Untrue. Maybe with some roundabout techniques like file-upload or using the > `Canvas` UI. But on a single web-form submit, 100K to 130K tokens is the > limit. Anything more and it will say something's gone wrong. Anyhow, enough preamble. Let's feed this article thus far and all the rest of the bundled-up context of the cobease into Gemini and see what it tells us in terms of my goals. You know what I'm looking for, don't ya? **Gemini**: Okay, I've reviewed the Pipulate codebase and the `prompt.md`. The request to "gently clean up and consolidate inline CSS styles in a non-breaking way" is a great next step for maintaining and scaling the project's Python-centric HTML generation. Here's a strategy focusing on the 80/20 rule for maximum impact with manageable effort, emphasizing safety and alignment with Pipulate's existing patterns: ## Strategy for CSS Cleanup and Consolidation in Pipulate The core idea is to move from scattered inline styles towards a more centralized and maintainable approach, leveraging both Python constants (for component-specific or dynamic styles) and dedicated CSS classes (for common, reusable utility styles). This will be a phased approach to ensure changes are non-breaking and easy to implement. **Guiding Principles:** 1. **Safety First:** Prioritize non-breaking changes. Test visually after each small set of changes. 2. **80/20 Rule:** Focus on the most common and impactful style duplications. Don't aim for a complete CSS overhaul in one go. 3. **Leverage Existing Patterns:** Pipulate already uses Python constants for some styles (e.g., `Pipulate.ERROR_STYLE`) and a `static/styles.css` file. We'll build on these. 4. **Maintain Readability:** Both Python and CSS should remain clear. 5. **Respect Pico.css:** Utilize Pico.css variables and utility classes where possible before creating custom ones. ### Phase 1: Centralize Styles with Python Constants (Low-Risk, High Python Code Clarity) This phase focuses on making the Python code cleaner by centralizing inline style strings without necessarily changing the final rendered HTML structure (they'll still be inline styles, but defined in one place). **1. Identify Candidates:** * Look for identical or very similar multi-property inline style strings that are repeated within `server.py` or plugin files. * Pay attention to styles defined directly in FastHTML component calls: `Div(..., style="property1: value1; property2: value2;")`. * **Prime Candidates from your code:** * `Pipulate.revert_control`: The `default_style` and `article_style` variables. * `Pipulate.widget_container` and `Pipulate.finalized_content`: The main `Div` styling. * `create_chat_interface`: The `style` for the `Div(id="chat-interface")`. * Various UI creation functions (`create_nav_menu`, `create_profile_menu`, etc.) often have repeated styling for summaries, list items, or containers. * Plugin files (like `000_profiles.py`'s `render_profile`) have specific component styles. **2. Create Python Constants:** * **Location:** * For styles specific to a component generated by a method in the `Pipulate` class, add them as class attributes (e.g., `self.REVERT_BUTTON_STYLE = "..."`). * For more general UI styles used across multiple functions in `server.py`, define them as global constants at the top of `server.py` (e.g., `PICO_CARD_STYLE_BASE = "background-color: var(--pico-card-background-color); border-radius: var(--pico-border-radius);"`) * **Naming:** Use descriptive uppercase names (e.g., `NAV_DROPDOWN_SUMMARY_STYLE`). **3. Implement:** * Replace the inline string literals with these constants in your Python code. * Example (from `Pipulate.revert_control`): ```python # Before default_style = ( "background-color: var(--pico-del-color);" # ... more styles ) form = Form(..., Button(..., style=default_style)) # After (assuming REVERT_BUTTON_STYLE is a new constant in server.py or Pipulate class) from server import REVERT_BUTTON_STYLE # or self.REVERT_BUTTON_STYLE form = Form(..., Button(..., style=REVERT_BUTTON_STYLE)) ``` **Benefits of this phase:** * Reduces redundancy in Python code. * Makes styles easier to find and modify globally (within Python). * Minimal risk as the rendered HTML output (inline styles) remains the same. ### Phase 2: Introduce Utility CSS Classes (Reduce HTML Bloat, Better Separation) This phase introduces CSS classes for very common, simple, and reusable style declarations. **1. Identify Candidates for Utility Classes:** * After Phase 1, analyze the Python style constants and any remaining inline styles. * Look for *individual CSS declarations* or small, logical groups of declarations that are frequently used. * Examples: `display: flex; align-items: center;`, `text-align: right;`, `margin-bottom: var(--pico-spacing);`, `font-weight: bold;`. * Prioritize styles that define common layout patterns (flex centering, spacing) or text appearances not covered by Pico's defaults. * Use the Python analysis script (provided below) to help identify these. **2. Define Utility Classes in `static/styles.css`:** * Use meaningful names (e.g., `.flex-center-items`, `.text-right`, `.mb-pico` (for margin-bottom pico spacing), `.font-bold`). * Leverage Pico.css CSS variables where appropriate to maintain theme consistency: ```css /* static/styles.css */ .mb-pico { margin-bottom: var(--pico-spacing); } .text-error { color: var(--pico-del-color); /* Using a Pico variable */ } .flex-center-items { display: flex; align-items: center; justify-content: center; /* Or other justify values as needed */ } ``` **3. Implement:** * **Refactor Python Constants:** If a Python style constant can be simplified by using one or more new utility classes, update the constant. * Example: ```python # server.py # Before constant OLD_COMPLEX_STYLE = "display: flex; align-items: center; color: red; padding: 1rem;" # static/styles.css .flex-acenter { display: flex; align-items: center; } .text-red { color: red; } .p-1rem { padding: 1rem; } # server.py # After constant - this might be overkill, but illustrates the point # Option A: Still a constant, but less in it NEW_SIMPLER_STYLE_CONST = "color: red;" # if padding and flex are common enough for classes # Python code: Div(..., cls="flex-acenter p-1rem", style=NEW_SIMPLER_STYLE_CONST) # Option B: If all parts are covered by classes # Python code: Div(..., cls="flex-acenter text-red p-1rem") ``` * **Update FastHTML Components:** In your Python code, add the `cls="your-utility-class"` attribute to FastHTML components and remove the corresponding CSS properties from their `style` attribute or Python style constant. * Example: ```python # Before Div("Content", style="text-align: center; margin-bottom: var(--pico-spacing);") # static/styles.css .text-center { text-align: center; } /* .mb-pico already defined */ # After Div("Content", cls="text-center mb-pico") ``` **Benefits of this phase:** * Reduces the amount of inline CSS in the final HTML, leading to smaller HTML documents. * Promotes better separation of concerns (styling moves to CSS files). * Makes styles more cacheable by the browser. ### Phase 3: Component-Specific Classes (For More Complex Components) For UI elements that have a distinct structure and repeated styling not easily covered by simple utilities (e.g., the specific layout of a `render_profile` list item, or a custom card variant), consider creating component-specific CSS classes. **1. Identify:** Look for groups of styles consistently applied to a specific type of UI element you generate. **2. Define in `styles.css`:** Create classes like `.profile-item-display`, `.workflow-step-card`. **3. Implement:** Apply these classes using `cls=` in your Python code. This is a more involved step, so tackle it after the lower-hanging fruit of Phases 1 and 2. ### Python Script for Style Analysis Here's a Python script using `ast` to help identify common inline style strings and individual declarations. Save this as `analyze_styles.py` in your project root (e.g., `pipulate/analyze_styles.py`) and run it from there. ````python import ast import os from pathlib import Path from collections import Counter import re # Expand this list with all FastHTML component names used in your project # This helps the script focus on relevant style attributes. FASTHTML_COMPONENT_NAMES = { 'Div', 'Button', 'Span', 'P', 'H1', 'H2', 'H3', 'H4', 'H5', 'H6', 'A', 'Form', 'Input', 'Li', 'Ul', 'Card', 'Grid', 'Container', 'Details', 'Summary', 'Label', 'Textarea', 'Select', 'Option', 'Pre', 'Code', 'Hr', 'Script', 'Link', 'Meta', 'Title', 'Group', 'Main', 'Header', 'Footer', 'Article', 'Nav', 'Aside', 'Section', 'Figure', 'Figcaption', 'Blockquote', 'Table', 'Thead', 'Tbody', 'Tfoot', 'Tr', 'Th', 'Td', 'Fieldset', 'Legend' # Add any custom components if they also take a 'style' kwarg } class StyleFinder(ast.NodeVisitor): def __init__(self): self.styles_counter = Counter() self.style_locations = {} # Stores (file, line_no, component_name) for each style string self.current_file = "" def visit_Call(self, node): func_name = '' if isinstance(node.func, ast.Name): func_name = node.func.id elif isinstance(node.func, ast.Attribute): func_name = node.func.attr # Check if the function call is likely a FastHTML component # Or, to be broader, check any function call that has a 'style' keyword argument. # For simplicity, we'll check if func_name is in our known set, # OR if 'style' is among its keywords. is_potential_component = func_name in FASTHTML_COMPONENT_NAMES has_style_kwarg = any(kw.arg == 'style' for kw in node.keywords) if is_potential_component or has_style_kwarg: for keyword in node.keywords: if keyword.arg == 'style': style_value_node = keyword.value style_str = None if isinstance(style_value_node, ast.Constant) and isinstance(style_value_node.value, str): style_str = style_value_node.value.strip() elif isinstance(style_value_node, ast.Name): # style=SOME_PYTHON_CONSTANT style_str = f"PYTHON_CONSTANT:{style_value_node.id}" elif isinstance(style_value_node, ast.JoinedStr): # f-string # Create a generic representation for f-strings # This helps group them if their structure is similar style_str_parts = [] for val_node in style_value_node.values: if isinstance(val_node, ast.Constant): style_str_parts.append(val_node.value) elif isinstance(val_node, ast.FormattedValue): style_str_parts.append(f"{{{getattr(val_node.value, 'id', 'expr')}}}") # Generic placeholder style_str = f"F_STRING:{''.join(style_str_parts).strip()}" if style_str and style_str: # Ignore empty styles self.styles_counter[style_str] += 1 location_key = (self.current_file, node.lineno, func_name or "UnknownComponent") if style_str not in self.style_locations: self.style_locations[style_str] = [] self.style_locations[style_str].append(location_key) self.generic_visit(node) def analyze_project_styles(project_root_str): project_root = Path(project_root_str) finder = StyleFinder() # Files to analyze py_files_to_scan = [] py_files_to_scan.extend(project_root.glob("plugins/**/*.py")) # Recursive for plugins py_files_to_scan.extend(project_root.glob("helpers/**/*.py")) # Recursive for helpers py_files_to_scan.append(project_root / "server.py") print(f"Scanning {len(py_files_to_scan)} Python files for inline styles...\n") for filepath in py_files_to_scan: if not filepath.is_file(): continue # Exclude the analysis script itself or other non-UI generating helpers if filepath.name in ["analyze_styles.py", "create_workflow.py", "splice_workflow_step.py"]: continue try: with open(filepath, "r", encoding="utf-8") as f: content = f.read() finder.current_file = str(filepath.relative_to(project_root)) tree = ast.parse(content) finder.visit(tree) except Exception as e: print(f"Could not parse {filepath}: {e}") print("--- Analysis Complete ---") print(f"\n--- Top {min(20, len(finder.styles_counter))} Most Common Inline Style Strings/Representations ---") for i, (style, count) in enumerate(finder.styles_counter.most_common(20)): print(f"\n{i+1}. Style (Count: {count}):") if style.startswith("PYTHON_CONSTANT:"): print(f" [PY_CONST] {style}") elif style.startswith("F_STRING:"): print(f" [F_STRING] {style[9:]}") # Print the structure without "F_STRING:" else: print(f" ```css\n {style}\n ```") # print(" First 3 Locations:") # for f, l, c_name in finder.style_locations.get(style, [])[:3]: # print(f" - {f}:L{l} (in {c_name or 'unknown'})") # Analysis of individual declarations declaration_counts = Counter() for style_str, num in finder.styles_counter.items(): if style_str.startswith("PYTHON_CONSTANT:") or style_str.startswith("F_STRING:"): continue # Skip constants and f-strings for this part # Normalize: remove extra spaces around semicolons and colons style_str_normalized = re.sub(r'\s*;\s*', ';', style_str) style_str_normalized = re.sub(r'\s*:\s*', ':', style_str_normalized) declarations = [d.strip() for d in style_str_normalized.split(';') if d.strip()] for decl in declarations: declaration_counts[decl] += num # Weight by how many times the block appeared print(f"\n--- Top {min(20, len(declaration_counts))} Most Common Individual CSS Declarations ---") print("(These are good candidates for utility CSS classes in static/styles.css)") for i, (decl, count) in enumerate(declaration_counts.most_common(20)): if count > 1: # Only show if declaration is used more than once print(f"{i+1}. `{decl}` (Appears in style blocks ~{count} times)") if __name__ == "__main__": # Assuming the script is in pipulate/ (project root) or pipulate/helpers/ # Adjust CWD if running from a different location. current_script_path = Path(__file__).resolve() project_root_dir = current_script_path.parent # If in helpers/ # If analyze_styles.py is in the project root itself, use: # project_root_dir = current_script_path.parent # If you are sure that this script is inside the 'helpers' directory, one level below project root: if project_root_dir.name == "helpers" and project_root_dir.parent.name == "pipulate": project_root_dir = project_root_dir.parent elif project_root_dir.name == "pipulate": # If script is directly in pipulate/ pass # project_root_dir is already correct else: print(f"Warning: Could not reliably determine project root from {current_script_path}. Assuming current directory.") project_root_dir = Path.cwd() print(f"Using project root: {project_root_dir}") analyze_project_styles(str(project_root_dir)) ```` **Instructions for using `analyze_styles.py`:** 1. Save the script as `analyze_styles.py` in your Pipulate project's root directory (or in `helpers/` and adjust the root path detection if needed). 2. Run it from your project root: `python analyze_styles.py` 3. Review the output: * "Most Common Inline Style Strings": Shows exact style strings and how often they appear. Good candidates for Python constants if they are complex or specific. * "Most Common Individual CSS Declarations": Shows individual `property: value` pairs. Good candidates for utility CSS classes if they are simple and widely used. ### Implementation with AI (Cursor) You can use your AI assistant in Cursor effectively with this strategy: * **For Phase 1 (Python Constants):** * **Prompt:** "In `server.py`, within the `Pipulate` class (or globally if more appropriate), find the inline style string `/* PASTE THE STYLE STRING HERE */`. Create a Python constant named `MY_DESCRIPTIVE_STYLE_NAME` for this string. Then, find all occurrences of this exact style string within `server.py` and the `plugins/` directory where it's used as a `style` attribute in FastHTML components (like `Div`, `Button`, `Span`, etc.) and replace them with the new Python constant." * **For Phase 2 (Utility CSS Classes):** * **Prompt:** "The CSS declaration `/* PASTE CSS DECLARATION e.g., display: flex; align-items: center; */` is used frequently. Create a utility class named `.my-utility-class` in `static/styles.css` for this. Then, show me examples in `server.py` or plugin files where I can replace this inline CSS with `cls=\"my-utility-class\"` in FastHTML components." * **Follow-up:** "Now, apply this change to all occurrences where this exact declaration is used as part of an inline style." (Be cautious with automated replacements and review changes). ### Example: Cleaning up `Pipulate.revert_control` Let's take the `default_style` from `Pipulate.revert_control` as an example: ```python # server.py (current in Pipulate.revert_control) default_style = ( "background-color: var(--pico-del-color);" "display: inline-flex;" "padding: 0.5rem 0.5rem;" "border-radius: 4px;" "font-size: 0.85rem;" "cursor: pointer;" "margin: 0;" "line-height: 1;" "align-items: center;" ) ``` **Applying Phase 1:** ```python # server.py (near top or in Pipulate class constants) REVERT_BUTTON_STYLE = ( "background-color: var(--pico-del-color); " # Added spaces for safety "display: inline-flex; " "padding: 0.5rem 0.5rem; " "border-radius: 4px; " # Pico uses var(--pico-border-radius) "font-size: 0.85rem; " "cursor: pointer; " "margin: 0; " "line-height: 1; " "align-items: center;" ) # server.py (in Pipulate.revert_control) # ... form = Form( # ... Button( step_button(step_id, refill, revert_label), type="submit", style=self.REVERT_BUTTON_STYLE # or REVERT_BUTTON_STYLE if global ), # ... ) ``` **Applying Phase 2 (Further Refinement - Optional):** Looking at `REVERT_BUTTON_STYLE`, `display: inline-flex; align-items: center;` is common. ```css /* static/styles.css */ .inline-flex-acenter { display: inline-flex; align-items: center; } .p-05rem { padding: 0.5rem; } /* Example, Pico might have spacing utils */ .fs-085rem { font-size: 0.85rem; } .lh-1 { line-height: 1; } .cursor-pointer { cursor: pointer; } .m-0 { margin: 0; } /* Pico uses var(--pico-border-radius) for border-radius generally, and var(--pico-del-color) isn't a standard Pico variable, but if you define it, use it. Pico's buttons often handle their own background/padding via classes like .secondary, .contrast or .outline. This particular button is styled to look like a Pico destructive action, which Pico doesn't have a specific class for. */ .btn-revert-look { /* More specific class for this button's unique aspects */ background-color: var(--pico-del-color, #d32f2f); /* provide fallback if --pico-del-color isn't defined */ border-radius: 4px; /* Or var(--pico-border-radius) if it matches */ /* Other unique styles specific to this button that aren't general utilities */ } ``` ```python # server.py # REVERT_BUTTON_STYLE might become less necessary if all covered by classes, # or it might compose only the unique aspects like background-color if that's not made a class. # In Pipulate.revert_control form = Form( # ... Button( step_button(step_id, refill, revert_label), type="submit", # Apply multiple classes cls="inline-flex-acenter p-05rem fs-085rem cursor-pointer m-0 lh-1 btn-revert-look", # style="" # Hopefully empty now or just for truly unique properties ), # ... ) ``` For the `revert_control` specifically, Pico's button classes (`