Delete process_flow_generator.py

process_flow_generator.py

@@ -1,181 +0,0 @@
-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)}"