import re
from pyvis.network import Network

def extract_key_terms(text: str):
    """
    A naive approach to extract key terms by matching capitalized words.
    """
    return re.findall(r"\b[A-Z][a-zA-Z]+\b", text)

def create_medical_graph(query: str, docs: list) -> str:
    """
    Builds an interactive PyVis network:
      - A central "QUERY" node.
      - One node per retrieved document.
      - Sub-nodes for extracted key terms.
    Returns the HTML content of the generated graph.
    """
    net = Network(height="600px", width="100%", directed=False)
    net.add_node("QUERY", label=f"Query: {query}", color="red", shape="star")

    for i, doc in enumerate(docs):
        doc_id = f"Doc_{i}"
        net.add_node(doc_id, label=f"Abstract {i+1}", color="blue")
        net.add_edge("QUERY", doc_id)

        terms = extract_key_terms(doc)
        for term in set(terms):
            term_id = f"{doc_id}_{term}"
            net.add_node(term_id, label=term, color="green")
            net.add_edge(doc_id, term_id)

    # Generate the HTML content directly without unsupported keyword arguments.
    html_content = net.generate_html(notebook=False)
    return html_content