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import graphviz
import json
from tempfile import NamedTemporaryFile
import os
def generate_process_flow_diagram(json_input: str, output_format: str) -> str:
"""
Generates a Process Flow Diagram (Flowchart) from JSON input.
Args:
json_input (str): A JSON string describing the process flow 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:
{
"start_node": "Start Inference Request",
"nodes": [
{
"id": "user_input",
"label": "Receive User Input (Data)",
"type": "io"
},
{
"id": "preprocess_data",
"label": "Preprocess Data",
"type": "process"
},
{
"id": "validate_data",
"label": "Validate Data Format/Type",
"type": "decision"
},
{
"id": "data_valid_yes",
"label": "Data Valid?",
"type": "decision"
},
{
"id": "load_model",
"label": "Load AI Model (if not cached)",
"type": "process"
},
{
"id": "run_inference",
"label": "Run AI Model Inference",
"type": "process"
},
{
"id": "postprocess_output",
"label": "Postprocess Model Output",
"type": "process"
},
{
"id": "send_response",
"label": "Send Response to User",
"type": "io"
},
{
"id": "log_error",
"label": "Log Error & Notify User",
"type": "process"
},
{
"id": "end_inference_process",
"label": "End Inference Process",
"type": "end"
}
],
"connections": [
{"from": "start_node", "to": "user_input", "label": "Request"},
{"from": "user_input", "to": "preprocess_data", "label": "Data Received"},
{"from": "preprocess_data", "to": "validate_data", "label": "Cleaned"},
{"from": "validate_data", "to": "data_valid_yes", "label": "Check"},
{"from": "data_valid_yes", "to": "load_model", "label": "Yes"},
{"from": "data_valid_yes", "to": "log_error", "label": "No"},
{"from": "load_model", "to": "run_inference", "label": "Model Ready"},
{"from": "run_inference", "to": "postprocess_output", "label": "Output Generated"},
{"from": "postprocess_output", "to": "send_response", "label": "Ready"},
{"from": "send_response", "to": "end_inference_process", "label": "Response Sent"},
{"from": "log_error", "to": "end_inference_process", "label": "Error Handled"}
]
}
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 'start_node' not in data or 'nodes' not in data or 'connections' not in data:
raise ValueError("Missing required fields: 'start_node', 'nodes', or 'connections'")
node_shapes = {
"process": "box",
"decision": "diamond",
"start": "oval",
"end": "oval",
"io": "parallelogram",
"document": "note",
"default": "box"
}
node_colors = {
"process": "#BEBEBE",
"decision": "#FFF9C4",
"start": "#A8E6CF",
"end": "#FFB3BA",
"io": "#B8D4F1",
"document": "#F0F8FF",
"default": "#BEBEBE"
}
dot = graphviz.Digraph(
name='ProcessFlowDiagram',
format='png',
graph_attr={
'rankdir': 'TB',
'splines': 'ortho',
'bgcolor': 'white',
'pad': '0.5',
'nodesep': '0.6',
'ranksep': '0.8'
}
)
all_defined_nodes = {node['id']: node for node in data['nodes']}
start_node_id = data['start_node']
dot.node(
start_node_id,
start_node_id,
shape=node_shapes['start'],
style='filled,rounded',
fillcolor=node_colors['start'],
fontcolor='black',
fontsize='14'
)
for node_id, node_info in all_defined_nodes.items():
if node_id == start_node_id:
continue
node_type = node_info.get("type", "default")
shape = node_shapes.get(node_type, "box")
color = node_colors.get(node_type, node_colors["default"])
node_label = node_info['label']
dot.node(
node_id,
node_label,
shape=shape,
style='filled,rounded',
fillcolor=color,
fontcolor='black',
fontsize='14'
)
for connection in data['connections']:
dot.edge(
connection['from'],
connection['to'],
label=connection.get('label', ''),
color='#4a4a4a',
fontcolor='#4a4a4a',
fontsize='10'
)
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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