questions.py

import nltk
import sys
import os
import string
FILE_MATCHES = 1
SENTENCE_MATCHES = 1
import math
import corpus

FILE_MATCHES = 1
SENTENCE_MATCHES = 1
nltk.download('stopwords')


def main():

    # Check command-line arguments
    

    # Calculate IDF values across files
    files = load_files(corpus)
    file_words = {
        filename: tokenize(files[filename])
        for filename in files
    }
    file_idfs = compute_idfs(file_words)

    # Prompt user for query
    query = set(tokenize(input("Query: ")))

    # Determine top file matches according to TF-IDF
    filenames = top_files(query, file_words, file_idfs, n=FILE_MATCHES)

    # Extract sentences from top files
    sentences = dict()
    for filename in filenames:
        for passage in files[filename].split("\n"):
            for sentence in nltk.sent_tokenize(passage):
                tokens = tokenize(sentence)
                if tokens:
                    sentences[sentence] = tokens

    # Compute IDF values across sentences
    idfs = compute_idfs(sentences)

    # Determine top sentence matches
    matches = top_sentences(query, sentences, idfs, n=SENTENCE_MATCHES)
    for match in matches:
        print(match)


def load_files(directory):
    print("loading data......")

    files = {}
    for filename in corpus:
            files[filename] = content

    return files
    """
    Given a directory name, return a dictionary mapping the filename of each
    `.txt` file inside that directory to the file's contents as a string.
    """
    #raise NotImplementedError


def tokenize(document):
    words_tokens = nltk.word_tokenize(document)

    refined_words_tokens = [word.lower() for word in words_tokens if
                            word not in string.punctuation and word not in nltk.corpus.stopwords.words("english")]

    return refined_words_tokens
    """
    Given a document (represented as a string), return a list of all of the
    words in that document, in order.

    Process document by coverting all words to lowercase, and removing any
    punctuation or English stopwords.
    """
    #raise NotImplementedError


def compute_idfs(documents):
    idf = {}
    total_documents = len(documents)
    for titles in documents:

        for word in documents[titles]:
            if word in idf:
                continue
            word_frequency = 1
            for other_titles in documents:
                if titles == other_titles:
                    continue
                else:
                    if word in documents[other_titles]:
                        word_frequency += 1
            idf[word] = math.log(total_documents / word_frequency)

    return idf
    """
    Given a dictionary of `documents` that maps names of documents to a list
    of words, return a dictionary that maps words to their IDF values.

    Any word that appears in at least one of the documents should be in the
    resulting dictionary.
    """
    #raise NotImplementedError


def top_files(query, files, idfs, n):
    tf_idf_scores = {file_names: 0 for file_names in files}
    for file_name in files:
        for word in query:
            if word in files[file_name]:
                tf = files[file_name].count(word)
                idf = idfs[word]
                tf_idf_scores[file_name] += tf * idf

    return sorted([file for file in files], key=lambda x: tf_idf_scores[x], reverse=True)
    """
    Given a `query` (a set of words), `files` (a dictionary mapping names of
    files to a list of their words), and `idfs` (a dictionary mapping words
    to their IDF values), return a list of the filenames of the the `n` top
    files that match the query, ranked according to tf-idf.
    """
    #raise NotImplementedError


def top_sentences(query, sentences, idfs, n):
    top_sentence_score = {sentence: [0, 0] for sentence in sentences}
    for sentence in sentences:

        for words in query:
            if words in sentences[sentence]:
                top_sentence_score[sentence][0] += idfs[words]
        query_term_density = 0
        for word in sentences[sentence]:
            if word in query:
                query_term_density += 1
        top_sentence_score[sentence][1] = query_term_density / len(sentences[sentence])

    sorted_top_sentence_score = {sentence: values for sentence, values in
                                 sorted(top_sentence_score.items(), key=lambda x: (x[1][0], x[1][1]), reverse=True)}

    return list(sorted_top_sentence_score.keys())[:n]
    """
    Given a `query` (a set of words), `sentences` (a dictionary mapping
    sentences to a list of their words), and `idfs` (a dictionary mapping words
    to their IDF values), return a list of the `n` top sentences that match
    the query, ranked according to idf. If there are ties, preference should
    be given to sentences that have a higher query term density.
    """
    #raise NotImplementedError


if __name__ == "__main__":
    main()