feat: add agent interaction heatmap visualization

- Added a new tab for visualizing agent interactions
- Implemented heatmap generation from swarm logs
- Updated requirements with pandas, seaborn, and matplotlib
- Improved UI with better layout and instructions
- Fixed model name to use latest Claude 3.5 Sonnet

.gitignore

@@ -1,2 +1,5 @@
 # Environment variables
-.env
+.env
+
+# Local tests
+local_tests

app.py

@@ -5,6 +5,9 @@ import anthropic
 import yaml
 import hashlib
 import json
+import io
+import matplotlib.pyplot as plt
+import seaborn as sns
 from dotenv import load_dotenv
 
 load_dotenv()
@@ -121,6 +124,48 @@ def discover_agents_from_registry():
     print(f"Successfully loaded {len(tools)} agents from registry")
     return tools, cards, index
 
+# ---- Heatmap Generator ----
+
+def generate_swarm_heatmap(log_path="swarm_log.jsonl"):
+    from itertools import combinations
+    from collections import defaultdict
+    import pandas as pd
+
+    agent_pairs = defaultdict(int)
+
+    try:
+        with open(log_path, "r", encoding="utf-8") as f:
+            for line in f:
+                entry = json.loads(line)
+                agents = entry.get("agents", [])
+                for a, b in combinations(sorted(agents), 2):
+                    agent_pairs[(a, b)] += 1
+
+        all_agents = sorted({agent for pair in agent_pairs for agent in pair})
+        matrix = pd.DataFrame(0, index=all_agents, columns=all_agents)
+
+        for (a, b), count in agent_pairs.items():
+            matrix.at[a, b] = count
+            matrix.at[b, a] = count
+
+        # Create heatmap figure
+        plt.figure(figsize=(8, 6))
+        sns.heatmap(matrix, annot=True, fmt="d", cmap="YlGnBu", linewidths=0.5)
+        plt.title("Swarm Agent Co-occurrence")
+        plt.xticks(rotation=45, ha="right")
+        plt.yticks(rotation=0)
+        plt.tight_layout()
+
+        buf = io.BytesIO()
+        plt.savefig(buf, format="png")
+        plt.close()
+        buf.seek(0)
+        return buf
+
+    except Exception as e:
+        print(f"Error generating heatmap: {e}")
+        return None
+
 # ---- Claude Orchestrator ----
 
 def match_agents_by_vector(input_text, tools, index):
@@ -374,6 +419,30 @@ if __name__ == "__main__":
                         chatbot
                     )
 
+        # Add Heatmap Tab
+        with gr.Tab("Agent Swarm Heatmap"):
+            with gr.Row():
+                with gr.Column():
+                    heatmap_btn = gr.Button("Show Heatmap")
+                    heatmap_output = gr.Image(type="pil", label="Agent Interaction Heatmap")
+
+                    def show_heatmap():
+                        heatmap = generate_swarm_heatmap()
+                        if heatmap is None:
+                            return None
+                        from PIL import Image
+                        return Image.open(heatmap)
+
+                    heatmap_btn.click(show_heatmap, outputs=heatmap_output)
+                    
+                    gr.Markdown("""
+                    ### How to read this heatmap:
+                    - The heatmap shows how often different agents interact together
+                    - Darker cells indicate more frequent interactions between agents
+                    - The diagonal shows self-interactions (typically not used)
+                    - Use this to understand which agents work together most frequently
+                    """)
+
     mcp_interface = gr.Interface(
         fn=mcp_entry_point,
         inputs=gr.Textbox(label="User Input"),

requirements.txt

@@ -2,4 +2,7 @@ gradio==5.33.1
 requests==2.32.3
 pyyaml==6.0.2
 anthropic==0.53.0
-gradio[mcp]
+gradio[mcp]
+pandas==2.2.3
+seaborn==0.13.2
+matplotlib==3.10.3