Update sample_data.py

sample_data.py

@@ -1,4 +1,4 @@
-COMPLEX_SAMPLE_JSON = """
+CONCEPT_MAP_JSON = """
 {
   "central_node": "Artificial Intelligence (AI)",
   "nodes": [
@@ -130,8 +130,7 @@ COMPLEX_SAMPLE_JSON = """
                 {
                   "id": "robotics_example",
                   "label": "Robotics",
-                  "relationship": "example"
-                },
+                  "relationship": "Example"},
                 {
                   "id": "autonomous_example",
                   "label": "Autonomous Vehicles",
@@ -164,4 +163,142 @@ COMPLEX_SAMPLE_JSON = """
     }
   ]
 }
-"""
+"""
+
+# 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."}
+      ]
+    }
+  ]
+}
+"""