Creating a custom reference figure is a common stumbling block for teams that need a clear, reusable visual of their workflow. The process often fails because of missing font files, unclear directory handling, inconsistent box styling, or difficulty adding connectors and annotations. A practical solution is to automate the figure generation with a small, self‑contained script that handles each step reliably.
First, define an output root and create a dedicated folder for the reference image, ensuring the path exists with parents=True and exist_ok=True. Next, instantiate a white canvas of fixed dimensions (e.g., 1333×750 pixels) and prepare a drawing context. Load the desired fonts—bold for titles and module labels, regular for subtitles—wrapping the load in a try/except block so the script falls back to the default font if the specific TTF files are unavailable. Draw a centered title, then iterate over a list of module specifications to render rounded rectangles with consistent radius, fill, outline, and width. Place the module name and a short subtitle inside each box using the appropriate fonts. Between boxes, draw straight lines and simple arrowheads to indicate flow direction. Add a final rounded rectangle at the bottom to host a design cue or caption, keeping the visual clean and academically styled. Save the image as PNG, confirm the file path, and optionally display it in the environment.
With the reference figure in place, configure the API‑backed AutoFigure agent. Retrieve the API key for the chosen provider; if missing, skip cloud‑based sections and notify the user. Otherwise, build a Config object that bundles the generation, methodology, and enhancement settings, pointing the custom_references list to the newly saved figure. Validate the configuration, report any errors, and verify that the reference is recognized. Initialize the AutoFigureAgent with the validated config. Preview the initial prompt template to ensure the description, format, and length are correct. If the API key is present and text‑to‑figure generation is enabled, create the output directory, invoke the agent’s generate method with the full description, iteration limits, quality threshold, output format, enhancement flags, art style, and the custom reference. Finally, summarize the result to confirm success or diagnose issues.
This end‑to‑end approach removes manual drawing steps, guarantees reproducible styling, handles missing resources gracefully, and connects seamlessly to automated figure generation pipelines. #AI #Productivity #DataScience #MachineLearning #Automation #Visualization