initial commit

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+images/rd1.png filter=lfs diff=lfs merge=lfs -text
+images/rd2.png filter=lfs diff=lfs merge=lfs -text
+images/sc1.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,14 +1,16 @@
 ---
 title: Graphify
-emoji: 🔥
-colorFrom: red
-colorTo: gray
+emoji: ⚡
+colorFrom: purple
+colorTo: indigo
 sdk: gradio
 sdk_version: 5.33.0
 app_file: app.py
 pinned: false
 license: mit
-short_description: Generate diagrams from JSON super fast and easy!
+short_description: Generate all type of diagrams from JSON 
+tags:
+  - mcp-server-track
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,216 @@
+import gradio as gr
+
+from concept_map_generator import generate_concept_map
+from synoptic_chart_generator import generate_synoptic_chart
+from radial_diagram_generator import generate_radial_diagram
+from process_flow_generator import generate_process_flow_diagram
+from wbs_diagram_generator import generate_wbs_diagram
+
+from sample_data import CONCEPT_MAP_JSON, SYNOPTIC_CHART_JSON, RADIAL_DIAGRAM_JSON, PROCESS_FLOW_JSON, WBS_DIAGRAM_JSON
+
+if __name__ == "__main__":
+    DEFAULT_BASE_COLOR = '#19191a' 
+
+    with gr.Blocks(
+        title="Advanced Graph Generator",
+        css="""
+        .gradio-container {
+            font-family: 'Inter', sans-serif !important;
+        }
+        .gr-tab-item {
+            padding: 10px 20px;
+            font-size: 1.1em;
+            font-weight: bold;
+        }
+        .gr-button {
+            border-radius: 8px;
+            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+            background-color: #FFA500; /* Orange color for buttons */
+            color: white;
+            padding: 10px 20px;
+            font-size: 1.1em;
+        }
+        .gr-button:hover {
+            background-color: #FF8C00; /* Darker Orange on hover */
+        }
+        .gr-textbox {
+            border-radius: 8px;
+            padding: 10px;
+        }
+        /* Dark mode styles, adjust if needed */
+        .gradio-container.dark { 
+            --tw-bg-opacity: 1;
+            background-color: rgb(24 24 27 / var(--tw-bg-opacity));
+            color: #d4d4d8; /* text-zinc-300 */
+        }
+        .gradio-container.dark .gr-textbox {
+            background-color: rgb(39 39 42 / var(--tw-bg-opacity)); 
+            color: #d4d4d8;
+            border-color: #52525b; /* border-zinc-600 */
+        }
+        .gradio-container.dark .gr-tab-item {
+            color: #d4d4d8;
+        }
+        .gradio-container.dark .gr-tab-item.selected {
+            background-color: rgb(39 39 42 / var(--tw-bg-opacity));
+            color: #fff;
+        }
+        """
+    ) as demo:
+        gr.Markdown(
+            """
+            # Graphify: generate diagrams from JSON super fast and easy ⚡!
+            Choose a graph type and provide your JSON data to generate a visual representation.
+            All graphs maintain a consistent, elegant style with rounded boxes,
+            a dark-to-light color gradient, and a clean white background.
+            """
+        )
+
+        with gr.Row(variant="panel"):
+            gr.Markdown("### Output Format")
+            output_format_radio = gr.Radio(
+                choices=["png", "svg"],
+                value="png",
+                label="Select Output File Format",
+                interactive=True,
+                elem_id="output-format-selector"
+            )
+
+        gr.Markdown("<br>") 
+
+        with gr.Tabs():
+            with gr.TabItem("Concept Map"):
+                json_input_cm = gr.Textbox(
+                    value=CONCEPT_MAP_JSON,
+                    placeholder="Paste JSON following the documented format",
+                    label="Structured JSON Input",
+                    lines=25
+                )
+                output_cm = gr.Image(
+                    label="Generated Graph",
+                    type="filepath",
+                    show_download_button=True
+                )
+                submit_btn_cm = gr.Button("Submit")
+                
+                submit_btn_cm.click(
+                    fn=generate_concept_map, 
+                    inputs=[json_input_cm, output_format_radio], 
+                    outputs=output_cm
+                )
+                gr.Markdown("<br>") 
+                gr.Markdown("## Example Concept Maps")
+                with gr.Row():
+                    gr.Image(value="./images/cm1.svg", label="Sample 1", show_label=True, interactive=False, height="auto", width="100%")
+                    gr.Image(value="./images/cm2.svg", label="Sample 2", show_label=True, interactive=False, height="auto", width="100%")
+
+            with gr.TabItem("Synoptic Chart"):
+                json_input_sc = gr.Textbox(
+                    value=SYNOPTIC_CHART_JSON,
+                    placeholder="Paste JSON following the documented format",
+                    label="Structured JSON Input",
+                    lines=25
+                )
+                output_sc = gr.Image(
+                    label="Generated Graph",
+                    type="filepath",
+                    show_download_button=True
+                )
+                submit_btn_sc = gr.Button("Submit")
+                
+                submit_btn_sc.click(
+                    fn=generate_synoptic_chart, 
+                    inputs=[json_input_sc, output_format_radio], 
+                    outputs=output_sc
+                )
+                gr.Markdown("<br>")
+                gr.Markdown("## Example Synoptic Charts")
+                with gr.Row():
+                    gr.Image(value="./images/sc1.svg", label="Sample 1", show_label=True, interactive=False, height="auto", width="100%")
+                    gr.Image(value="./images/sc2.svg", label="Sample 2", show_label=True, interactive=False, height="auto", width="100%")
+
+            with gr.TabItem("Radial Diagram"):
+                json_input_rd = gr.Textbox(
+                    value=RADIAL_DIAGRAM_JSON,
+                    placeholder="Paste JSON following the documented format",
+                    label="Structured JSON Input",
+                    lines=25
+                )
+                output_rd = gr.Image(
+                    label="Generated Graph",
+                    type="filepath",
+                    show_download_button=True
+                )
+                submit_btn_rd = gr.Button("Submit")
+                
+                submit_btn_rd.click(
+                    fn=generate_radial_diagram, 
+                    inputs=[json_input_rd, output_format_radio], 
+                    outputs=output_rd
+                )
+                gr.Markdown("<br>")
+                gr.Markdown("## Example Radial Diagrams")
+                with gr.Row():
+                    gr.Image(value="./images/rd1.svg", label="Sample 1", show_label=True, interactive=False, height="auto", width="100%")
+                    gr.Image(value="./images/rd2.svg", label="Sample 2", show_label=True, interactive=False, height="auto", width="100%")
+                with gr.Row():
+                    gr.Image(value="./images/rd3.svg", label="Sample 1", show_label=True, interactive=False, height="auto", width="100%")
+                    gr.Image(value="./images/rd4.svg", label="Sample 2", show_label=True, interactive=False, height="auto", width="100%")
+
+            with gr.TabItem("Process Flow"):
+                json_input_pf = gr.Textbox(
+                    value=PROCESS_FLOW_JSON,
+                    placeholder="Paste JSON following the documented format",
+                    label="Structured JSON Input",
+                    lines=25
+                )
+                output_pf = gr.Image(
+                    label="Generated Graph",
+                    type="filepath",
+                    show_download_button=True
+                )
+                submit_btn_pf = gr.Button("Submit")
+                
+                submit_btn_pf.click(
+                    fn=generate_process_flow_diagram, 
+                    inputs=[json_input_pf, output_format_radio], 
+                    outputs=output_pf
+                )
+                gr.Markdown("<br>")
+                gr.Markdown("## Example Process Flow Diagrams")
+                with gr.Row():
+                    gr.Image(value="./images/pf1.svg", label="Sample 1", show_label=True, interactive=False, height="auto", width="100%")
+                    gr.Image(value="./images/pf2.svg", label="Sample 2", show_label=True, interactive=False, height="auto", width="100%")
+
+            with gr.TabItem("WBS Diagram"):
+                json_input_wbs = gr.Textbox(
+                    value=WBS_DIAGRAM_JSON,
+                    placeholder="Paste JSON following the documented format",
+                    label="Structured JSON Input",
+                    lines=25
+                )
+                output_wbs = gr.Image(
+                    label="Generated Graph",
+                    type="filepath",
+                    show_download_button=True
+                )
+                submit_btn_wbs = gr.Button("Submit")
+                
+                submit_btn_wbs.click(
+                    fn=generate_wbs_diagram, 
+                    inputs=[json_input_wbs, output_format_radio], 
+                    outputs=output_wbs
+                )
+                gr.Markdown("<br>")
+                gr.Markdown("## Example WBS Diagrams")
+                with gr.Row():
+                    gr.Image(value="./images/wd1.svg", label="Sample 1", show_label=True, interactive=False, height="auto", width="100%")
+                    gr.Image(value="./images/wd2.svg", label="Sample 2", show_label=True, interactive=False, height="auto", width="100%")
+    
+    demo.launch(
+        mcp_server=True,
+        share=False,
+        server_port=7860,
+        server_name="0.0.0.0"
+    )
+

concept_map_generator.py

@@ -0,0 +1,228 @@
+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' # 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
+        )
+        
+        # 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)}"
+

graph_generator_utils.py

@@ -0,0 +1,83 @@
+import graphviz
+
+def add_nodes_and_edges(dot: graphviz.Digraph, parent_id: str, nodes_list: list, current_depth: int, base_color: str):
+    """
+    Recursively adds nodes and edges to a Graphviz Digraph object,
+    applying a color gradient and consistent styling.
+
+    Args:
+        dot (graphviz.Digraph): The Graphviz Digraph object to modify.
+        parent_id (str): The ID of the parent node for the current set of nodes.
+        nodes_list (list): A list of dictionaries, each representing a node
+                           with 'id', 'label', 'relationship', and optional 'subnodes'.
+        current_depth (int): The current depth in the graph hierarchy (0 for central node).
+        base_color (str): The hexadecimal base color for the deepest nodes.
+    """
+    # Calculate color for current depth, making it lighter
+    # This factor determines how quickly the color lightens per level.
+    lightening_factor = 0.12 
+    
+    # Convert base_color hex to RGB for interpolation
+    # Ensure base_color is a valid hex string before converting
+    if not isinstance(base_color, str) or not base_color.startswith('#') or len(base_color) != 7:
+        base_color = '#19191a' # Fallback to default dark if invalid
+
+    base_r = int(base_color[1:3], 16)
+    base_g = int(base_color[3:5], 16)
+    base_b = int(base_color[5:7], 16)
+
+    # Calculate current node color by blending towards white
+    current_r = base_r + int((255 - base_r) * current_depth * lightening_factor)
+    current_g = base_g + int((255 - base_g) * current_depth * lightening_factor)
+    current_b = base_b + int((255 - base_b) * current_depth * lightening_factor)
+
+    # Clamp values to 255 to stay within valid RGB range
+    current_r = min(255, current_r)
+    current_g = min(255, current_g)
+    current_b = min(255, current_b)
+    
+    node_fill_color = f'#{current_r:02x}{current_g:02x}{current_b:02x}'
+
+    # Font color: white for dark nodes, black for very light nodes for readability
+    font_color = 'white' if current_depth * lightening_factor < 0.6 else 'black'
+    
+    # Edge colors and font sizes
+    edge_color = '#4a4a4a' # Dark gray for lines
+    # Font size adjusts based on depth, ensuring a minimum size
+    font_size = max(9, 14 - (current_depth * 2)) 
+    edge_font_size = max(7, 10 - (current_depth * 1))
+
+    for node in nodes_list:
+        node_id = node.get('id')
+        label = node.get('label')
+        relationship = node.get('relationship')
+        
+        # Basic validation for node data
+        if not all([node_id, label, relationship]):
+            raise ValueError(f"Invalid node: {node}")
+            
+        # Add node with specified style
+        dot.node(
+            node_id,
+            label,
+            shape='box', # All nodes are rectangular
+            style='filled,rounded', # Filled and rounded corners
+            fillcolor=node_fill_color, 
+            fontcolor=font_color,
+            fontsize=str(font_size) 
+        )
+        
+        # Add edge from parent to current node
+        dot.edge(
+            parent_id,
+            node_id,
+            label=relationship,
+            color=edge_color,
+            fontcolor=edge_color, # Edge label color also dark gray
+            fontsize=str(edge_font_size) 
+        )
+        
+        # Recursively call for subnodes
+        if 'subnodes' in node:
+            add_nodes_and_edges(dot, node_id, node['subnodes'], current_depth + 1, base_color)
+

packages.txt

@@ -0,0 +1 @@
+graphviz

process_flow_generator.py

@@ -0,0 +1,192 @@
+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)
+        
+        # Validate required top-level keys for a flowchart
+        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'")
+
+        # Define specific node shapes for flowchart types
+        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' # Hardcoded base color
+
+        fill_color_for_nodes = base_color
+        font_color_for_nodes = 'white' if base_color == '#19191a' or base_color.lower() in ['#000000', '#19191a'] else 'black'
+        
+        # Store all nodes by ID for easy lookup
+        all_defined_nodes = {node['id']: node for node in data['nodes']}
+        
+        # Add start node explicitly
+        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'
+        )
+
+        # Add all other nodes (process, decision, etc.)
+        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)}"
+

radial_diagram_generator.py

@@ -0,0 +1,117 @@
+import graphviz
+import json
+from tempfile import NamedTemporaryFile
+import os
+from graph_generator_utils import add_nodes_and_edges
+
+def generate_radial_diagram(json_input: str, output_format: str) -> str:
+    """
+    Generates a radial (center-expanded) diagram from JSON input.
+
+    Args:
+        json_input (str): A JSON string describing the radial diagram structure.
+                          It must follow the Expected JSON Format Example below.
+
+    Expected JSON Format Example:
+    {
+      "central_node": "AI Core Concepts & Domains",
+      "nodes": [
+        {
+          "id": "foundational_ml",
+          "label": "Foundational ML",
+          "relationship": "builds on",
+          "subnodes": [
+            {"id": "supervised_l", "label": "Supervised Learning", "relationship": "e.g."},
+            {"id": "unsupervised_l", "label": "Unsupervised Learning", "relationship": "e.g."}
+          ]
+        },
+        {
+          "id": "dl_architectures",
+          "label": "Deep Learning Arch.",
+          "relationship": "evolved from",
+          "subnodes": [
+            {"id": "cnns_rad", "label": "CNNs", "relationship": "e.g."},
+            {"id": "rnns_rad", "label": "RNNs", "relationship": "e.g."}
+          ]
+        },
+        {
+          "id": "major_applications",
+          "label": "Major AI Applications",
+          "relationship": "applied in",
+          "subnodes": [
+            {"id": "nlp_rad", "label": "Natural Language Processing", "relationship": "e.g."},
+            {"id": "cv_rad", "label": "Computer Vision", "relationship": "e.g."}
+          ]
+        },
+        {
+          "id": "ethical_concerns",
+          "label": "Ethical AI Concerns",
+          "relationship": "addresses",
+          "subnodes": [
+            {"id": "fairness_rad", "label": "Fairness & Bias", "relationship": "e.g."},
+            {"id": "explainability", "label": "Explainability (XAI)", "relationship": "e.g."}
+          ]
+        },
+        {
+          "id": "future_trends",
+          "label": "Future AI Trends",
+          "relationship": "looking at",
+          "subnodes": [
+            {"id": "agi_future", "label": "AGI Development", "relationship": "e.g."},
+            {"id": "quantum_ai", "label": "Quantum AI", "relationship": "e.g."}
+          ]
+        }
+      ]
+    }
+
+    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='RadialDiagram',
+            format='png',
+            engine='neato', # Use 'neato' or 'fdp' for radial/force-directed layout
+            graph_attr={
+                'overlap': 'false', # Prevent node overlap
+                'splines': 'true',  # Smooth splines for edges
+                'bgcolor': 'white', # White background
+                'pad': '0.5',       # Padding around the graph
+                'layout': 'neato'   # Explicitly set layout engine for consistency
+            },
+            node_attr={
+                'fixedsize': 'false' # Allow nodes to resize based on content
+            }
+        )
+        
+        base_color = '#19191a' # Hardcoded base 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
+        )
+        
+        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)}"
+

requirements.txt

@@ -0,0 +1,3 @@
+gradio[mcp]
+graphviz
+pydot

sample_data.py

@@ -0,0 +1,478 @@
+CONCEPT_MAP_JSON = """
+{
+  "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"
+                }
+              ]
+            }
+          ]
+        }
+      ]
+    }
+  ]
+}
+"""
+
+# JSON for Synoptic Chart (horizontal hierarchy) - AI related, 4 levels
+SYNOPTIC_CHART_JSON = """
+{
+  "central_node": "AI Project Lifecycle",
+  "nodes": [
+    {
+      "id": "phase1",
+      "label": "I. Problem Definition & Data Acquisition",
+      "relationship": "Starts with",
+      "subnodes": [
+        {
+          "id": "sub1_1",
+          "label": "1. Problem Formulation",
+          "relationship": "Involves",
+          "subnodes": [
+            {"id": "sub1_1_1", "label": "1.1. Identify Business Need", "relationship": "e.g."},
+            {"id": "sub1_1_2", "label": "1.2. Define KPIs", "relationship": "e.g."}
+          ]
+        },
+        {
+          "id": "sub1_2",
+          "label": "2. Data Collection",
+          "relationship": "Followed by",
+          "subnodes": [
+            {"id": "sub1_2_1", "label": "2.1. Source Data", "relationship": "from"},
+            {"id": "sub1_2_2", "label": "2.2. Data Cleaning", "relationship": "includes"}
+          ]
+        }
+      ]
+    },
+    {
+      "id": "phase2",
+      "label": "II. Model Development",
+      "relationship": "Proceeds to",
+      "subnodes": [
+        {
+          "id": "sub2_1",
+          "label": "1. Feature Engineering",
+          "relationship": "Comprises",
+          "subnodes": [
+            {"id": "sub2_1_1", "label": "1.1. Feature Selection", "relationship": "e.g."},
+            {"id": "sub2_1_2", "label": "1.2. Feature Transformation", "relationship": "e.g."}
+          ]
+        },
+        {
+          "id": "sub2_2",
+          "label": "2. Model Training",
+          "relationship": "Involves",
+          "subnodes": [
+            {"id": "sub2_2_1", "label": "2.1. Algorithm Selection", "relationship": "uses"},
+            {"id": "sub2_2_2", "label": "2.2. Hyperparameter Tuning", "relationship": "optimizes"}
+          ]
+        }
+      ]
+    },
+    {
+      "id": "phase3",
+      "label": "III. Evaluation & Deployment",
+      "relationship": "Culminates in",
+      "subnodes": [
+        {
+          "id": "sub3_1",
+          "label": "1. Model Evaluation",
+          "relationship": "Includes",
+          "subnodes": [
+            {"id": "sub3_1_1", "label": "1.1. Performance Metrics", "relationship": "measures"},
+            {"id": "sub3_1_2", "label": "1.2. Bias & Fairness Audits", "relationship": "ensures"}
+          ]
+        },
+        {
+          "id": "sub3_2",
+          "label": "2. Deployment & Monitoring",
+          "relationship": "Requires",
+          "subnodes": [
+            {"id": "sub3_2_1", "label": "2.1. API/Integration Development", "relationship": "for"},
+            {"id": "sub3_2_2", "label": "2.2. Continuous Monitoring", "relationship": "ensures"}
+          ]
+        }
+      ]
+    }
+  ]
+}
+"""
+
+# JSON for Radial Diagram (central expansion) - AI related, 3 levels with 5->10 structure
+RADIAL_DIAGRAM_JSON = """
+{
+  "central_node": "AI Core Concepts & Domains",
+  "nodes": [
+    {
+      "id": "foundational_ml",
+      "label": "Foundational ML",
+      "relationship": "builds on",
+      "subnodes": [
+        {"id": "supervised_l", "label": "Supervised Learning", "relationship": "e.g."},
+        {"id": "unsupervised_l", "label": "Unsupervised Learning", "relationship": "e.g."}
+      ]
+    },
+    {
+      "id": "dl_architectures",
+      "label": "Deep Learning Arch.",
+      "relationship": "evolved from",
+      "subnodes": [
+        {"id": "cnns_rad", "label": "CNNs", "relationship": "e.g."},
+        {"id": "rnns_rad", "label": "RNNs", "relationship": "e.g."}
+      ]
+    },
+    {
+      "id": "major_applications",
+      "label": "Major AI Applications",
+      "relationship": "applied in",
+      "subnodes": [
+        {"id": "nlp_rad", "label": "Natural Language Processing", "relationship": "e.g."},
+        {"id": "cv_rad", "label": "Computer Vision", "relationship": "e.g."}
+      ]
+    },
+    {
+      "id": "ethical_concerns",
+      "label": "Ethical AI Concerns",
+      "relationship": "addresses",
+      "subnodes": [
+        {"id": "fairness_rad", "label": "Fairness & Bias", "relationship": "e.g."},
+        {"id": "explainability", "label": "Explainability (XAI)", "relationship": "e.g."}
+      ]
+    },
+    {
+      "id": "future_trends",
+      "label": "Future AI Trends",
+      "relationship": "looking at",
+      "subnodes": [
+        {"id": "agi_future", "label": "AGI Development", "relationship": "e.g."},
+        {"id": "quantum_ai", "label": "Quantum AI", "relationship": "e.g."}
+      ]
+    }
+  ]
+}
+"""
+
+PROCESS_FLOW_JSON = """
+{
+  "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"}
+  ]
+}
+"""
+
+# New JSON for Work Breakdown Structure (WBS) Diagram - similar to image, but not identical
+WBS_DIAGRAM_JSON = """
+{
+  "project_title": "AI Model Development Project",
+  "phases": [
+    {
+      "id": "phase_prep",
+      "label": "Preparation",
+      "tasks": [
+        {
+          "id": "task_1_1_vision",
+          "label": "Identify Vision",
+          "subtasks": [
+            {
+              "id": "subtask_1_1_1_design_staff",
+              "label": "Design & Staffing",
+              "sub_subtasks": [
+                {
+                  "id": "ss_task_1_1_1_1_env_setup",
+                  "label": "Environment Setup",
+                  "sub_sub_subtasks": [
+                    {
+                      "id": "sss_task_1_1_1_1_1_lib_install",
+                      "label": "Install Libraries",
+                      "final_level_tasks": [
+                        {"id": "ft_1_1_1_1_1_1_data_access", "label": "Grant Data Access"}
+                      ]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        }
+      ]
+    },
+    {
+      "id": "phase_plan",
+      "label": "Planning",
+      "tasks": [
+        {
+          "id": "task_2_1_cost_analysis",
+          "label": "Cost Analysis",
+          "subtasks": [
+            {
+              "id": "subtask_2_1_1_benefit_analysis",
+              "label": "Benefit Analysis",
+              "sub_subtasks": [
+                {
+                  "id": "ss_task_2_1_1_1_risk_assess",
+                  "label": "AI Risk Assessment",
+                  "sub_sub_subtasks": [
+                    {
+                      "id": "sss_task_2_1_1_1_1_model_selection",
+                      "label": "Model Selection",
+                      "final_level_tasks": [
+                        {"id": "ft_2_1_1_1_1_1_data_strategy", "label": "Data Strategy"}
+                      ]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        }
+      ]
+    },
+    {
+      "id": "phase_dev",
+      "label": "Development",
+      "tasks": [
+        {
+          "id": "task_3_1_change_mgmt",
+          "label": "Data Preprocessing",
+          "subtasks": [
+            {
+              "id": "subtask_3_1_1_implementation",
+              "label": "Feature Engineering",
+              "sub_subtasks": [
+                {
+                  "id": "ss_task_3_1_1_1_beta_testing",
+                  "label": "Model Training",
+                  "sub_sub_subtasks": [
+                    {
+                      "id": "sss_task_3_1_1_1_1_other_task",
+                      "label": "Model Evaluation",
+                      "final_level_tasks": [
+                        {"id": "ft_3_1_1_1_1_1_hyperparam_tune", "label": "Hyperparameter Tuning"}
+                      ]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        }
+      ]
+    }
+  ]
+}
+
+"""

synoptic_chart_generator.py

@@ -0,0 +1,144 @@
+import graphviz
+import json
+from tempfile import NamedTemporaryFile
+import os
+from graph_generator_utils import add_nodes_and_edges
+
+def generate_synoptic_chart(json_input: str, output_format: str) -> str:
+    """
+    Generates a synoptic chart (horizontal flowchart) from JSON input.
+
+    Args:
+        json_input (str): A JSON string describing the synoptic chart structure.
+                          It must follow the Expected JSON Format Example below.
+
+    Expected JSON Format Example:
+    {
+      "central_node": "AI Project Lifecycle",
+      "nodes": [
+        {
+          "id": "phase1",
+          "label": "I. Problem Definition & Data Acquisition",
+          "relationship": "Starts with",
+          "subnodes": [
+            {
+              "id": "sub1_1",
+              "label": "1. Problem Formulation",
+              "relationship": "Involves",
+              "subnodes": [
+                {"id": "sub1_1_1", "label": "1.1. Identify Business Need", "relationship": "e.g."},
+                {"id": "sub1_1_2", "label": "1.2. Define KPIs", "relationship": "e.g."}
+              ]
+            },
+            {
+              "id": "sub1_2",
+              "label": "2. Data Collection",
+              "relationship": "Followed by",
+              "subnodes": [
+                {"id": "sub1_2_1", "label": "2.1. Source Data", "relationship": "from"},
+                {"id": "sub1_2_2", "label": "2.2. Data Cleaning", "relationship": "includes"}
+              ]
+            }
+          ]
+        },
+        {
+          "id": "phase2",
+          "label": "II. Model Development",
+          "relationship": "Proceeds to",
+          "subnodes": [
+            {
+              "id": "sub2_1",
+              "label": "1. Feature Engineering",
+              "relationship": "Comprises",
+              "subnodes": [
+                {"id": "sub2_1_1", "label": "1.1. Feature Selection", "relationship": "e.g."},
+                {"id": "sub2_1_2", "label": "1.2. Feature Transformation", "relationship": "e.g."}
+              ]
+            },
+            {
+              "id": "sub2_2",
+              "label": "2. Model Training",
+              "relationship": "Involves",
+              "subnodes": [
+                {"id": "sub2_2_1", "label": "2.1. Algorithm Selection", "relationship": "uses"},
+                {"id": "sub2_2_2", "label": "2.2. Hyperparameter Tuning", "relationship": "optimizes"}
+              ]
+            }
+          ]
+        },
+        {
+          "id": "phase3",
+          "label": "III. Evaluation & Deployment",
+          "relationship": "Culminates in",
+          "subnodes": [
+            {
+              "id": "sub3_1",
+              "label": "1. Model Evaluation",
+              "relationship": "Includes",
+              "subnodes": [
+                {"id": "sub3_1_1", "label": "1.1. Performance Metrics", "relationship": "measures"},
+                {"id": "sub3_1_2", "label": "1.2. Bias & Fairness Audits", "relationship": "ensures"}
+              ]
+            },
+            {
+              "id": "sub3_2",
+              "label": "2. Deployment & Monitoring",
+              "relationship": "Requires",
+              "subnodes": [
+                {"id": "sub3_2_1", "label": "2.1. API/Integration Development", "relationship": "for"},
+                {"id": "sub3_2_2", "label": "2.2. Continuous Monitoring", "relationship": "ensures"}
+              ]
+            }
+          ]
+        }
+      ]
+    }
+
+    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='SynopticChart',
+            format='png',
+            graph_attr={
+                'rankdir': 'LR',        # Left-to-Right layout (horizontal hierarchy)
+                'splines': 'ortho',     # Straight lines
+                'bgcolor': 'white',     # White background
+                'pad': '0.5',           # Padding around the graph
+                'ranksep': '0.7',       # Reduced horizontal separation between ranks (columns)
+                'nodesep': '0.3'        # Adjusted vertical separation between nodes in the same rank
+            }
+        )
+        
+        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)}"
+

wbs_diagram_generator.py

@@ -0,0 +1,243 @@
+import graphviz
+import json
+from tempfile import NamedTemporaryFile
+import os
+from graph_generator_utils import add_nodes_and_edges 
+
+def generate_wbs_diagram(json_input: str, output_format: str) -> str:
+    """
+    Generates a Work Breakdown Structure (WBS) Diagram from JSON input.
+
+    Args:
+        json_input (str): A JSON string describing the WBS structure.
+                          It must follow the Expected JSON Format Example below.
+
+    Expected JSON Format Example:
+    {
+      "project_title": "AI Model Development Project",
+      "phases": [
+        {
+          "id": "phase_prep",
+          "label": "Preparation",
+          "tasks": [
+            {
+              "id": "task_1_1_vision",
+              "label": "Identify Vision",
+              "subtasks": [
+                {
+                  "id": "subtask_1_1_1_design_staff",
+                  "label": "Design & Staffing",
+                  "sub_subtasks": [
+                    {
+                      "id": "ss_task_1_1_1_1_env_setup",
+                      "label": "Environment Setup",
+                      "sub_sub_subtasks": [
+                        {
+                          "id": "sss_task_1_1_1_1_1_lib_install",
+                          "label": "Install Libraries",
+                          "final_level_tasks": [
+                            {"id": "ft_1_1_1_1_1_1_data_access", "label": "Grant Data Access"}
+                          ]
+                        }
+                      ]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        {
+          "id": "phase_plan",
+          "label": "Planning",
+          "tasks": [
+            {
+              "id": "task_2_1_cost_analysis",
+              "label": "Cost Analysis",
+              "subtasks": [
+                {
+                  "id": "subtask_2_1_1_benefit_analysis",
+                  "label": "Benefit Analysis",
+                  "sub_subtasks": [
+                    {
+                      "id": "ss_task_2_1_1_1_risk_assess",
+                      "label": "AI Risk Assessment",
+                      "sub_sub_subtasks": [
+                        {
+                          "id": "sss_task_2_1_1_1_1_model_selection",
+                          "label": "Model Selection",
+                          "final_level_tasks": [
+                            {"id": "ft_2_1_1_1_1_1_data_strategy", "label": "Data Strategy"}
+                          ]
+                        }
+                      ]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        },
+        {
+          "id": "phase_dev",
+          "label": "Development",
+          "tasks": [
+            {
+              "id": "task_3_1_change_mgmt",
+              "label": "Data Preprocessing",
+              "subtasks": [
+                {
+                  "id": "subtask_3_1_1_implementation",
+                  "label": "Feature Engineering",
+                  "sub_subtasks": [
+                    {
+                      "id": "ss_task_3_1_1_1_beta_testing",
+                      "label": "Model Training",
+                      "sub_sub_subtasks": [
+                        {
+                          "id": "sss_task_3_1_1_1_1_other_task",
+                          "label": "Model Evaluation",
+                          "final_level_tasks": [
+                            {"id": "ft_3_1_1_1_1_1_hyperparam_tune", "label": "Hyperparameter Tuning"}
+                          ]
+                        }
+                      ]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        }
+      ]
+    }
+
+    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 'project_title' not in data or 'phases' not in data:
+            raise ValueError("Missing required fields: project_title or phases")
+
+        dot = graphviz.Digraph(
+            name='WBSDiagram',
+            format='png',
+            graph_attr={
+                'rankdir': 'TB',        # Top-to-Bottom hierarchy
+                'splines': 'ortho',     # Straight lines
+                'bgcolor': 'white',     # White background
+                'pad': '0.5',           # Padding
+                'ranksep': '0.6',       # Adjust vertical separation between ranks
+                'nodesep': '0.5'        # Adjust horizontal separation between nodes
+            }
+        )
+        
+        base_color = '#19191a' # Hardcoded base color
+
+        # Project Title node (main node)
+        dot.node(
+            'project_root',
+            data['project_title'],
+            shape='box',
+            style='filled,rounded',
+            fillcolor=base_color,
+            fontcolor='white',
+            fontsize='18'
+        )
+
+        # Helper for color and font based on depth for WBS
+        def get_gradient_color(depth, base_hex_color, lightening_factor=0.12):
+            base_r = int(base_hex_color[1:3], 16)
+            base_g = int(base_hex_color[3:5], 16)
+            base_b = int(base_hex_color[5:7], 16)
+
+            current_r = base_r + int((255 - base_r) * depth * lightening_factor)
+            current_g = base_g + int((255 - base_g) * depth * lightening_factor)
+            current_b = base_b + int((255 - base_b) * depth * lightening_factor)
+            
+            return f'#{min(255, current_r):02x}{min(255, current_g):02x}{min(255, current_b):02x}'
+
+        def get_font_color_for_background(depth, base_hex_color, lightening_factor=0.12):
+            base_r = int(base_hex_color[1:3], 16)
+            base_g = int(base_hex_color[3:5], 16)
+            base_b = int(base_hex_color[5:7], 16)
+            current_r = base_r + (255 - base_r) * depth * lightening_factor
+            current_g = base_g + (255 - base_g) * depth * lightening_factor
+            current_b = base_b + (255 - base_b) * depth * lightening_factor
+            
+            luminance = (0.2126 * current_r + 0.7152 * current_g + 0.0722 * current_b) / 255
+            return 'white' if luminance < 0.5 else 'black'
+
+        def _add_wbs_nodes_recursive(parent_id, current_level_tasks, current_depth):
+            for task_data in current_level_tasks:
+                task_id = task_data.get('id')
+                task_label = task_data.get('label')
+                
+                if not all([task_id, task_label]):
+                    raise ValueError(f"Invalid task data at depth {current_depth}: {task_data}")
+
+                node_fill_color = get_gradient_color(current_depth, base_color)
+                node_font_color = get_font_color_for_background(current_depth, base_color)
+                font_size = max(9, 14 - (current_depth * 2))
+
+                dot.node(
+                    task_id,
+                    task_label,
+                    shape='box',
+                    style='filled,rounded',
+                    fillcolor=node_fill_color,
+                    fontcolor=node_font_color,
+                    fontsize=str(font_size)
+                )
+                dot.edge(parent_id, task_id, color='#4a4a4a', arrowhead='none')
+
+                # Recursively call for next level of tasks (subtasks, sub_subtasks, etc.)
+                # This handles arbitrary nested keys like 'subtasks', 'sub_subtasks', 'final_level_tasks'
+                next_level_keys = ['tasks', 'subtasks', 'sub_subtasks', 'sub_sub_subtasks', 'final_level_tasks']
+                for key_idx, key in enumerate(next_level_keys):
+                    if key in task_data and isinstance(task_data[key], list):
+                        _add_wbs_nodes_recursive(task_id, task_data[key], current_depth + 1)
+                        break # Only process the first found sub-level key
+
+        # Process phases (level 1 from project_root)
+        phase_depth = 1
+        for phase in data['phases']:
+            phase_id = phase.get('id')
+            phase_label = phase.get('label')
+            
+            if not all([phase_id, phase_label]):
+                raise ValueError(f"Invalid phase data: {phase}")
+
+            phase_fill_color = get_gradient_color(phase_depth, base_color)
+            phase_font_color = get_font_color_for_background(phase_depth, base_color)
+            font_size_phase = max(9, 14 - (phase_depth * 2))
+
+            dot.node(
+                phase_id,
+                phase_label,
+                shape='box',
+                style='filled,rounded',
+                fillcolor=phase_fill_color,
+                fontcolor=phase_font_color,
+                fontsize=str(font_size_phase)
+            )
+            dot.edge('project_root', phase_id, color='#4a4a4a', arrowhead='none')
+
+            # Start recursion for tasks under this phase
+            if 'tasks' in phase and isinstance(phase['tasks'], list):
+                _add_wbs_nodes_recursive(phase_id, phase['tasks'], phase_depth + 1)
+
+        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)}"
+