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| import graphviz | |
| import json | |
| from tempfile import NamedTemporaryFile | |
| import os | |
| from graph_generator_utils import add_nodes_and_edges | |
| def generate_radial_diagram(json_input: str, output_format: str) -> str: | |
| """ | |
| Generates a radial (center-expanded) diagram from JSON input. | |
| Args: | |
| json_input (str): A JSON string describing the radial diagram structure. | |
| It must follow the Expected JSON Format Example below. | |
| Expected JSON Format Example: | |
| { | |
| "central_node": "AI Core Concepts & Domains", | |
| "nodes": [ | |
| { | |
| "id": "foundational_ml", | |
| "label": "Foundational ML", | |
| "relationship": "builds on", | |
| "subnodes": [ | |
| {"id": "supervised_l", "label": "Supervised Learning", "relationship": "e.g."}, | |
| {"id": "unsupervised_l", "label": "Unsupervised Learning", "relationship": "e.g."} | |
| ] | |
| }, | |
| { | |
| "id": "dl_architectures", | |
| "label": "Deep Learning Arch.", | |
| "relationship": "evolved from", | |
| "subnodes": [ | |
| {"id": "cnns_rad", "label": "CNNs", "relationship": "e.g."}, | |
| {"id": "rnns_rad", "label": "RNNs", "relationship": "e.g."} | |
| ] | |
| }, | |
| { | |
| "id": "major_applications", | |
| "label": "Major AI Applications", | |
| "relationship": "applied in", | |
| "subnodes": [ | |
| {"id": "nlp_rad", "label": "Natural Language Processing", "relationship": "e.g."}, | |
| {"id": "cv_rad", "label": "Computer Vision", "relationship": "e.g."} | |
| ] | |
| }, | |
| { | |
| "id": "ethical_concerns", | |
| "label": "Ethical AI Concerns", | |
| "relationship": "addresses", | |
| "subnodes": [ | |
| {"id": "fairness_rad", "label": "Fairness & Bias", "relationship": "e.g."}, | |
| {"id": "explainability", "label": "Explainability (XAI)", "relationship": "e.g."} | |
| ] | |
| }, | |
| { | |
| "id": "future_trends", | |
| "label": "Future AI Trends", | |
| "relationship": "looking at", | |
| "subnodes": [ | |
| {"id": "agi_future", "label": "AGI Development", "relationship": "e.g."}, | |
| {"id": "quantum_ai", "label": "Quantum AI", "relationship": "e.g."} | |
| ] | |
| } | |
| ] | |
| } | |
| Returns: | |
| str: The filepath to the generated PNG image file. | |
| """ | |
| try: | |
| if not json_input.strip(): | |
| return "Error: Empty input" | |
| data = json.loads(json_input) | |
| if 'central_node' not in data or 'nodes' not in data: | |
| raise ValueError("Missing required fields: central_node or nodes") | |
| dot = graphviz.Digraph( | |
| name='RadialDiagram', | |
| format='png', | |
| engine='neato', # Use 'neato' or 'fdp' for radial/force-directed layout | |
| graph_attr={ | |
| 'overlap': 'false', # Prevent node overlap | |
| 'splines': 'true', # Smooth splines for edges | |
| 'bgcolor': 'white', # White background | |
| 'pad': '0.5', # Padding around the graph | |
| 'layout': 'neato' # Explicitly set layout engine for consistency | |
| }, | |
| node_attr={ | |
| 'fixedsize': 'false' # Allow nodes to resize based on content | |
| } | |
| ) | |
| base_color = '#19191a' # Hardcoded base color | |
| dot.node( | |
| 'central', | |
| data['central_node'], | |
| shape='box', # Rectangular shape | |
| style='filled,rounded', # Filled and rounded corners | |
| fillcolor=base_color, # Darkest color | |
| fontcolor='white', # White text for dark background | |
| fontsize='16' # Larger font for central node | |
| ) | |
| add_nodes_and_edges(dot, 'central', data.get('nodes', []), current_depth=1, base_color=base_color) | |
| with NamedTemporaryFile(delete=False, suffix=f'.{output_format}') as tmp: | |
| dot.render(tmp.name, format=output_format, cleanup=True) | |
| return f"{tmp.name}.{output_format}" | |
| except json.JSONDecodeError: | |
| return "Error: Invalid JSON format" | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |