radial_diagram_generator.py

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.

        output_format (str): The output format for the generated diagram.
                            Supported formats: "png" or "svg"

    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 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' 
        base_color = '#BEBEBE'
        

        dot.node(
            'central',
            data['central_node'],
            shape='box',            # Rectangular shape
            style='filled,rounded', # Filled and rounded corners
            fillcolor=base_color,   # Darkest color
            fontcolor='black',      # 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)}"