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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.
# 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 PNG 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", # Rectangle for processes
# "decision": "diamond", # Diamond for decisions
# "start": "oval", # Oval for start
# "end": "oval", # Oval for end
# "io": "parallelogram", # Input/Output
# "document": "note", # Document symbol
# "default": "box" # Fallback
# }
# dot = graphviz.Digraph(
# name='ProcessFlowDiagram',
# format='png',
# graph_attr={
# 'rankdir': 'TB', # Top-to-Bottom flow is common for flowcharts
# 'splines': 'ortho', # Straight lines with 90-degree bends
# 'bgcolor': 'white', # White background
# 'pad': '0.5', # Padding around the graph
# 'nodesep': '0.6', # Spacing between nodes on same rank
# 'ranksep': '0.8' # Spacing between ranks
# }
# )
# base_color = '#19191a'
# fill_color_for_nodes = base_color
# font_color_for_nodes = 'white' if base_color == '#19191a' or base_color.lower() in ['#000000', '#19191a'] else 'black'
# 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, # Label is typically the ID itself for start/end
# shape=node_shapes['start'],
# style='filled,rounded',
# fillcolor='#2196F3', # A distinct blue for Start
# fontcolor='white',
# fontsize='14'
# )
# for node_id, node_info in all_defined_nodes.items():
# if node_id == start_node_id: # Skip if it's the start node, already added
# continue
# node_type = node_info.get("type", "default")
# shape = node_shapes.get(node_type, "box")
# node_label = node_info['label']
# # Use distinct color for end node if it exists
# if node_type == 'end':
# dot.node(
# node_id,
# node_label,
# shape=shape,
# style='filled,rounded',
# fillcolor='#F44336', # A distinct red for End
# fontcolor='white',
# fontsize='14'
# )
# else: # Regular process, decision, etc. nodes use the selected base color
# dot.node(
# node_id,
# node_label,
# shape=shape,
# style='filled,rounded',
# fillcolor=fill_color_for_nodes,
# fontcolor=font_color_for_nodes,
# fontsize='14'
# )
# # Add connections (edges)
# for connection in data['connections']:
# dot.edge(
# connection['from'],
# connection['to'],
# label=connection.get('label', ''),
# color='#4a4a4a', # Dark gray for lines
# 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)}"
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.
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 PNG 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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