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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_concept_map(json_input: str, output_format: str) -> str: | |
| """ | |
| Generates a concept map from JSON input. | |
| Args: | |
| json_input (str): A JSON string describing the concept map structure. | |
| It must follow the Expected JSON Format Example below. | |
| Expected JSON Format Example: | |
| { | |
| "central_node": "Artificial Intelligence (AI)", | |
| "nodes": [ | |
| { | |
| "id": "ml_fundamental", | |
| "label": "Machine Learning", | |
| "relationship": "is essential for", | |
| "subnodes": [ | |
| { | |
| "id": "dl_branch", | |
| "label": "Deep Learning", | |
| "relationship": "for example", | |
| "subnodes": [ | |
| { | |
| "id": "cnn_example", | |
| "label": "CNNs", | |
| "relationship": "for example" | |
| }, | |
| { | |
| "id": "rnn_example", | |
| "label": "RNNs", | |
| "relationship": "for example" | |
| } | |
| ] | |
| }, | |
| { | |
| "id": "rl_branch", | |
| "label": "Reinforcement Learning", | |
| "relationship": "for example", | |
| "subnodes": [ | |
| { | |
| "id": "qlearning_example", | |
| "label": "Q-Learning", | |
| "relationship": "example" | |
| }, | |
| { | |
| "id": "pg_example", | |
| "label": "Policy Gradients", | |
| "relationship": "example" | |
| } | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "id": "ai_types", | |
| "label": "Types", | |
| "relationship": "formed by", | |
| "subnodes": [ | |
| { | |
| "id": "agi_type", | |
| "label": "AGI", | |
| "relationship": "this is", | |
| "subnodes": [ | |
| { | |
| "id": "strong_ai", | |
| "label": "Strong AI", | |
| "relationship": "provoked by", | |
| "subnodes": [ | |
| { | |
| "id": "human_intel", | |
| "label": "Human-level Intel.", | |
| "relationship": "of" | |
| } | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "id": "ani_type", | |
| "label": "ANI", | |
| "relationship": "this is", | |
| "subnodes": [ | |
| { | |
| "id": "weak_ai", | |
| "label": "Weak AI", | |
| "relationship": "provoked by", | |
| "subnodes": [ | |
| { | |
| "id": "narrow_tasks", | |
| "label": "Narrow Tasks", | |
| "relationship": "of" | |
| } | |
| ] | |
| } | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "id": "ai_capabilities", | |
| "label": "Capabilities", | |
| "relationship": "change", | |
| "subnodes": [ | |
| { | |
| "id": "data_proc", | |
| "label": "Data Processing", | |
| "relationship": "can", | |
| "subnodes": [ | |
| { | |
| "id": "big_data", | |
| "label": "Big Data", | |
| "relationship": "as", | |
| "subnodes": [ | |
| { | |
| "id": "analysis_example", | |
| "label": "Data Analysis", | |
| "relationship": "example" | |
| }, | |
| { | |
| "id": "prediction_example", | |
| "label": "Prediction", | |
| "relationship": "example" | |
| } | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "id": "decision_making", | |
| "label": "Decision Making", | |
| "relationship": "can be", | |
| "subnodes": [ | |
| { | |
| "id": "automation", | |
| "label": "Automation", | |
| "relationship": "as", | |
| "subnodes": [ | |
| { | |
| "id": "robotics_example", | |
| "label": "Robotics", | |
| "relationship": "Example"}, | |
| { | |
| "id": "autonomous_example", | |
| "label": "Autonomous Vehicles", | |
| "relationship": "of one" | |
| } | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "id": "problem_solving", | |
| "label": "Problem Solving", | |
| "relationship": "can", | |
| "subnodes": [ | |
| { | |
| "id": "optimization", | |
| "label": "Optimization", | |
| "relationship": "as is", | |
| "subnodes": [ | |
| { | |
| "id": "algorithms_example", | |
| "label": "Algorithms", | |
| "relationship": "for example" | |
| } | |
| ] | |
| } | |
| ] | |
| } | |
| ] | |
| } | |
| ] | |
| } | |
| 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='ConceptMap', | |
| format='png', | |
| graph_attr={ | |
| 'rankdir': 'TB', # Top-to-Bottom layout (vertical hierarchy) | |
| 'splines': 'ortho', # Straight lines | |
| 'bgcolor': 'white', # White background | |
| 'pad': '0.5' # Padding around the graph | |
| } | |
| ) | |
| 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)}" | |