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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)}"
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