synoptic_chart_generator.py

import graphviz
import json
from tempfile import NamedTemporaryFile
import os
from graph_generator_utils import add_nodes_and_edges

def generate_synoptic_chart(json_input: str, output_format: str) -> str:
    """
    Generates a synoptic chart (horizontal flowchart) from JSON input.

    Args:
        json_input (str): A JSON string describing the synoptic chart structure.
                          It must follow the Expected JSON Format Example below.

    Expected JSON Format Example:
    {
      "central_node": "AI Project Lifecycle",
      "nodes": [
        {
          "id": "phase1",
          "label": "I. Problem Definition & Data Acquisition",
          "relationship": "Starts with",
          "subnodes": [
            {
              "id": "sub1_1",
              "label": "1. Problem Formulation",
              "relationship": "Involves",
              "subnodes": [
                {"id": "sub1_1_1", "label": "1.1. Identify Business Need", "relationship": "e.g."},
                {"id": "sub1_1_2", "label": "1.2. Define KPIs", "relationship": "e.g."}
              ]
            },
            {
              "id": "sub1_2",
              "label": "2. Data Collection",
              "relationship": "Followed by",
              "subnodes": [
                {"id": "sub1_2_1", "label": "2.1. Source Data", "relationship": "from"},
                {"id": "sub1_2_2", "label": "2.2. Data Cleaning", "relationship": "includes"}
              ]
            }
          ]
        },
        {
          "id": "phase2",
          "label": "II. Model Development",
          "relationship": "Proceeds to",
          "subnodes": [
            {
              "id": "sub2_1",
              "label": "1. Feature Engineering",
              "relationship": "Comprises",
              "subnodes": [
                {"id": "sub2_1_1", "label": "1.1. Feature Selection", "relationship": "e.g."},
                {"id": "sub2_1_2", "label": "1.2. Feature Transformation", "relationship": "e.g."}
              ]
            },
            {
              "id": "sub2_2",
              "label": "2. Model Training",
              "relationship": "Involves",
              "subnodes": [
                {"id": "sub2_2_1", "label": "2.1. Algorithm Selection", "relationship": "uses"},
                {"id": "sub2_2_2", "label": "2.2. Hyperparameter Tuning", "relationship": "optimizes"}
              ]
            }
          ]
        },
        {
          "id": "phase3",
          "label": "III. Evaluation & Deployment",
          "relationship": "Culminates in",
          "subnodes": [
            {
              "id": "sub3_1",
              "label": "1. Model Evaluation",
              "relationship": "Includes",
              "subnodes": [
                {"id": "sub3_1_1", "label": "1.1. Performance Metrics", "relationship": "measures"},
                {"id": "sub3_1_2", "label": "1.2. Bias & Fairness Audits", "relationship": "ensures"}
              ]
            },
            {
              "id": "sub3_2",
              "label": "2. Deployment & Monitoring",
              "relationship": "Requires",
              "subnodes": [
                {"id": "sub3_2_1", "label": "2.1. API/Integration Development", "relationship": "for"},
                {"id": "sub3_2_2", "label": "2.2. Continuous Monitoring", "relationship": "ensures"}
              ]
            }
          ]
        }
      ]
    }

    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='SynopticChart',
            format='png',
            graph_attr={
                'rankdir': 'LR',        # Left-to-Right layout (horizontal hierarchy)
                'splines': 'ortho',     # Straight lines
                'bgcolor': 'white',     # White background
                'pad': '0.5',           # Padding around the graph
                'ranksep': '0.7',       # Reduced horizontal separation between ranks (columns)
                'nodesep': '0.3'        # Adjusted vertical separation between nodes in the same rank
            }
        )
        
        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)}"