File size: 7,926 Bytes
8e56712 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 |
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")
# ํ๊ธ ํฐํธ ์ค์
# ํ๊ฒฝ ๋ณ์์์ ํฐํธ ๊ฒฝ๋ก ๊ฐ์ ธ์ค๊ธฐ
font_path = os.environ.get('KOREAN_FONT_PATH', '')
# Graphviz๋ ์์คํ
ํฐํธ๋ฅผ ์ฌ์ฉํ๋ฏ๋ก ํฐํธ ์ด๋ฆ์ผ๋ก ์ง์
# NanumGothic์ด ์์คํ
์ ์ค์น๋์ด ์์ด์ผ ํจ
korean_font = 'NanumGothic'
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
'fontname': korean_font, # ๊ทธ๋ํ ์ ์ฒด ํฐํธ ์ค์
'charset': 'UTF-8' # UTF-8 ์ธ์ฝ๋ฉ
},
node_attr={
'fontname': korean_font # ๋ชจ๋ ๋
ธ๋์ ๊ธฐ๋ณธ ํฐํธ
},
edge_attr={
'fontname': korean_font # ๋ชจ๋ ์ฃ์ง์ ๊ธฐ๋ณธ ํฐํธ
}
)
base_color = '#19191a' # Hardcoded base color
# Central node styling (rounded box, dark color)
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
fontname=korean_font # ํ๊ธ ํฐํธ ๋ช
์์ ์ง์
)
# Add child nodes and edges recursively starting from depth 1
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)}" |