Update sample_data.py

sample_data.py

@@ -474,45 +474,585 @@ WBS_DIAGRAM_JSON = """
 
 """
 
+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"}
+                      ]
+                    }
+                  ]
+                }
+              ]
+            }
+          ]
+        }
+      ]
+    }
+  ]
+}
+"""
+
+# JSON for Timeline Diagram
 TIMELINE_JSON = """
 {
-  "title": "AI Development Timeline",
+  "title": "Complete History of Artificial Intelligence",
+  "events_per_row": 4,
   "events": [
     {
       "id": "event_1",
-      "label": "Machine Learning Foundations",
-      "date": "1950-1960",
-      "description": "Early neural networks and perceptrons"
+      "label": "AI Concept Birth",
+      "date": "1943",
+      "description": "McCulloch & Pitts neural network model"
     },
     {
       "id": "event_2",
-      "label": "Expert Systems Era",
-      "date": "1970-1980",
-      "description": "Rule-based AI systems"
+      "label": "Turing Test",
+      "date": "1950",
+      "description": "Alan Turing proposes machine intelligence test"
     },
     {
       "id": "event_3",
-      "label": "Neural Network Revival",
-      "date": "1980-1990",
-      "description": "Backpropagation algorithm"
+      "label": "Dartmouth Conference",
+      "date": "1956",
+      "description": "Term 'Artificial Intelligence' coined"
     },
     {
       "id": "event_4",
-      "label": "Machine Learning Boom",
-      "date": "1990-2000",
-      "description": "Support Vector Machines, Random Forests"
+      "label": "First AI Program",
+      "date": "1957",
+      "description": "General Problem Solver (GPS) created"
     },
     {
       "id": "event_5",
-      "label": "Deep Learning Revolution",
-      "date": "2010-2020",
-      "description": "CNNs, RNNs, and breakthrough applications"
+      "label": "Perceptron Algorithm",
+      "date": "1958",
+      "description": "Frank Rosenblatt develops perceptron"
     },
     {
       "id": "event_6",
-      "label": "Transformer Era",
-      "date": "2020-Present",
-      "description": "GPT, BERT, and Large Language Models"
+      "label": "LISP Programming",
+      "date": "1959",
+      "description": "John McCarthy creates LISP for AI"
+    },
+    {
+      "id": "event_7",
+      "label": "Expert Systems",
+      "date": "1965",
+      "description": "DENDRAL - first expert system"
+    },
+    {
+      "id": "event_8",
+      "label": "AI Winter Begins",
+      "date": "1974",
+      "description": "Funding cuts due to unmet expectations"
+    },
+    {
+      "id": "event_9",
+      "label": "Backpropagation",
+      "date": "1986",
+      "description": "Algorithm for training neural networks"
+    },
+    {
+      "id": "event_10",
+      "label": "Deep Blue Victory",
+      "date": "1997",
+      "description": "IBM computer defeats chess champion"
+    },
+    {
+      "id": "event_11",
+      "label": "Machine Learning Boom",
+      "date": "2000s",
+      "description": "Support Vector Machines, Random Forests"
+    },
+    {
+      "id": "event_12",
+      "label": "Deep Learning Revival",
+      "date": "2006",
+      "description": "Geoffrey Hinton's deep belief networks"
+    },
+    {
+      "id": "event_13",
+      "label": "ImageNet Challenge",
+      "date": "2012",
+      "description": "AlexNet wins with deep CNN"
+    },
+    {
+      "id": "event_14",
+      "label": "AlphaGo Triumph",
+      "date": "2016",
+      "description": "DeepMind defeats Go world champion"
+    },
+    {
+      "id": "event_15",
+      "label": "Transformer Architecture",
+      "date": "2017",
+      "description": "Attention Is All You Need paper"
+    },
+    {
+      "id": "event_16",
+      "label": "GPT Era Begins",
+      "date": "2018-2023",
+      "description": "Large Language Models revolution"
     }
   ]
 }