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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', | |
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' | |
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)}" | |