from .pagerank_weighted import pagerank_weighted_scipy as _pagerank
from .preprocessing.textcleaner import clean_text_by_sentences as _clean_text_by_sentences
from .commons import build_graph as _build_graph
from .commons import remove_unreachable_nodes as _remove_unreachable_nodes
from .summarizer import _set_graph_edge_weights
from .summarizer import _add_scores_to_sentences


def scored_sentences(text, language="english", split=False, additional_stopwords=None):
    if not isinstance(text, str):
        raise ValueError("Text parameter must be a Unicode object (str)!")

    # Gets a list of processed sentences.
    sentences = _clean_text_by_sentences(text, language, additional_stopwords)

    # Creates the graph and calculates the similarity coefficient for every pair of nodes.
    graph = _build_graph([sentence.token for sentence in sentences])
    _set_graph_edge_weights(graph)

    # Remove all nodes with all edges weights equal to zero.
    _remove_unreachable_nodes(graph)

    # PageRank cannot be run in an empty graph.
    if len(graph.nodes()) == 0:
        return [] if split else ""

    # Ranks the tokens using the PageRank algorithm. Returns dict of sentence -> score
    pagerank_scores = _pagerank(graph)

    # Adds the summa scores to the sentence objects.
    _add_scores_to_sentences(sentences, pagerank_scores)

    return sentences