import re
import tempfile
import os
from pyvis.network import Network
from retrieval import collection  # Import the collection from retrieval.py

def extract_key_terms(text: str):
    """
    Naive keyword extraction: finds capitalized words.
    For production, consider using a dedicated medical NER.
    """
    return re.findall(r"\b[A-Z][a-zA-Z]+\b", text)

def create_medical_graph(query: str, docs: list) -> str:
    """
    Builds a Pyvis network:
      - A central 'QUERY' node.
      - One node per retrieved abstract.
      - Key terms from abstracts as subnodes.
    Returns the HTML 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)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".html") as tmp:
        temp_filename = tmp.name
    net.write_html(temp_filename, open_browser=False, notebook=False)
    with open(temp_filename, "r", encoding="utf-8") as f:
        html_content = f.read()
    os.remove(temp_filename)
    return html_content