Create analysis_tools.py

analysis_tools.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import networkx as nx
+from .memory_manager import retrieve_relevant
+
+
+def analyze_sentiment_topics(conversation: list) -> dict:
+    """Return placeholder sentiment and key topics."""
+    sentiments = ["Positive", "Neutral", "Negative"]
+    sentiment = np.random.choice(sentiments, p=[0.4, 0.4, 0.2])
+    topics = ["AI Ethics", "Policy", "Culture", "Technology", "Future"]
+    return {
+        "sentiment": sentiment,
+        "topics": np.random.choice(topics, 3, replace=False).tolist()
+    }
+
+
+def plot_participation(conversation: list, save_path: str) -> str:
+    """Save agent participation bar chart."""
+    agents = [msg['agent'] for msg in conversation]
+    counts = {agent: agents.count(agent) for agent in set(agents)}
+    plt.figure()
+    plt.bar(counts.keys(), counts.values())
+    plt.title("Agent Participation")
+    plt.tight_layout()
+    plt.savefig(save_path)
+    return save_path
+
+
+def generate_knowledge_graph(conversation: list, save_path: str) -> str:
+    """Save a simple directed graph of agent interactions."""
+    G = nx.DiGraph()
+    # Add nodes for each unique agent
+    agents = [msg['agent'] for msg in conversation]
+    for agent in set(agents):
+        G.add_node(agent)
+    # Randomly connect nodes
+    import random
+    nodes = list(G.nodes)
+    for _ in range(len(nodes) * 2):
+        a, b = random.sample(nodes, 2)
+        G.add_edge(a, b)
+    plt.figure()
+    pos = nx.spring_layout(G)
+    nx.draw(G, pos, with_labels=True, node_size=1500)
+    plt.title("Knowledge Graph")
+    plt.savefig(save_path)
+    return save_path