import networkx as nx

# Global in-memory graph
academic_graph = nx.DiGraph()

def add_paper_relation(source_paper: str, target_paper: str, relation_type: str = "cites"):
    """
    Menambahkan hubungan antar paper ke Knowledge Graph.
    relation_type: cites, refutes, supports
    """
    academic_graph.add_edge(source_paper, target_paper, relation=relation_type)
    return f"Berhasil memetakan: '{source_paper}' {relation_type} '{target_paper}'."

def query_graph(paper_name: str):
    """
    Mencari informasi paper dan siapa saja yang terkait dengannya di graph.
    """
    if paper_name not in academic_graph:
        return f"Paper '{paper_name}' belum ada di dalam Knowledge Graph."
    
    successors = list(academic_graph.successors(paper_name))
    predecessors = list(academic_graph.predecessors(paper_name))
    
    result = f"Analisis Graph untuk '{paper_name}':\n"
    if successors:
        result += "Mempengaruhi/Mengutip:\n"
        for s in successors:
            rel = academic_graph[paper_name][s]['relation']
            result += f"  - [{rel}] -> {s}\n"
    if predecessors:
        result += "Dipengaruhi/Dikutip oleh:\n"
        for p in predecessors:
            rel = academic_graph[p][paper_name]['relation']
            result += f"  - {p} -> [{rel}]\n"
            
    if not successors and not predecessors:
        result += "Belum ada hubungan yang terpetakan."
        
    return result