import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity

from typing import List, Any, Optional
from collections import defaultdict, deque

def predict_topic_nth_degree(
    new_article_title: str,
    new_article_embedding: List[float],
    edges: List[str],
    G: Any,
    max_depth: int = 1,
    is_weighted: bool = False,
    decay_factor: float = 1.0,
) -> Optional[str]:
    queue = deque()

    visited = set()
    visited.add(new_article_title)

    for ref in edges:
        if ref in G and ref not in visited:
            visited.add(ref)
            queue.append((ref, 1))

    if not queue:
        return None

    topic_scores = defaultdict(float)

    while queue:
        current_node, current_depth = queue.popleft()

        node_data = G.nodes[current_node]
        topic = node_data.get("label")

        if topic:
            if is_weighted:
                neighbor_embedding = node_data["embedding"]
                base_score = cosine_similarity(
                    [new_article_embedding], [neighbor_embedding]
                )[0][0]
            else:
                base_score = 1.0

            weighted_score = base_score * (decay_factor ** (current_depth - 1))

            topic_scores[topic] += weighted_score

        if current_depth < max_depth:
            for neighbor in G.neighbors(current_node):
                if neighbor not in visited:
                    visited.add(neighbor)
                    queue.append((neighbor, current_depth + 1))

    if not topic_scores:
        return None

    columns = ["Class", "Score"]
    result_df = pd.DataFrame(
        [(topic, score) for topic, score in topic_scores.items()], columns=columns
    ).sort_values(by="Score", ascending=False)
    return result_df