Sourcery refactored main branch - #1
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| summary_list = [str(sentence) for sentence in summary] | ||
| result = ' '.join(summary_list) | ||
| return result | ||
| return ' '.join(summary_list) |
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Function sumy_summary refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| estimatedTime = total_words/200.0 | ||
| return estimatedTime | ||
| return total_words/200.0 |
Author
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Function readingTime refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| fetched_text = ' '.join(map(lambda p:p.text,soup.find_all('p'))) | ||
| return fetched_text | ||
| return ' '.join(map(lambda p:p.text,soup.find_all('p'))) |
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Function get_text refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| nouns = list() | ||
| nouns = [] | ||
| for word, tag in blob.tags: | ||
| if tag == 'NN': | ||
| nouns.append(word.lemmatize()) | ||
| len_of_words = len(nouns) | ||
| rand_words = random.sample(nouns,len(nouns)) | ||
| final_word = list() | ||
| for item in rand_words: | ||
| word = Word(item).pluralize() | ||
| final_word.append(word) | ||
| summary = final_word | ||
| end = time.time() | ||
| final_time = end-start | ||
| if tag == 'NN': | ||
| nouns.append(word.lemmatize()) | ||
| len_of_words = len(nouns) | ||
| rand_words = random.sample(nouns,len(nouns)) | ||
| final_word = [] | ||
| for item in rand_words: | ||
| word = Word(item).pluralize() | ||
| final_word.append(word) | ||
| summary = final_word | ||
| end = time.time() | ||
| final_time = end-start |
Author
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Function analyse refactored with the following changes:
- Replace
list()with[][×2] (list-literal)
| summary_list = [str(sentence) for sentence in summary] | ||
| result = ' '.join(summary_list) | ||
| return result | ||
| return ' '.join(summary_list) |
Author
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Function sumy_summary refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| fetched_text = ' '.join(map(lambda p:p.text,soup.find_all('p'))) | ||
| return fetched_text | ||
| return ' '.join(map(lambda p:p.text,soup.find_all('p'))) |
Author
There was a problem hiding this comment.
Function get_text refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| nouns = list() | ||
| nouns = [] | ||
| for word, tag in blob.tags: | ||
| if tag == 'NN': | ||
| nouns.append(word.lemmatize()) | ||
| len_of_words = len(nouns) | ||
| rand_words = random.sample(nouns,len(nouns)) | ||
| final_word = list() | ||
| for item in rand_words: | ||
| word = Word(item).pluralize() | ||
| final_word.append(word) | ||
| summary = final_word | ||
| end = time.time() | ||
| final_time = end-start | ||
| if tag == 'NN': | ||
| nouns.append(word.lemmatize()) | ||
| len_of_words = len(nouns) | ||
| rand_words = random.sample(nouns,len(nouns)) | ||
| final_word = [] | ||
| for item in rand_words: | ||
| word = Word(item).pluralize() | ||
| final_word.append(word) | ||
| summary = final_word | ||
| end = time.time() | ||
| final_time = end-start |
Author
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Function analyse refactored with the following changes:
- Replace
list()with[][×2] (list-literal)
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| word_frequencies = {} | ||
| word_frequencies = {} | ||
| for word in nltk.word_tokenize(raw_text): | ||
| if word not in stopWords: | ||
| if word not in word_frequencies.keys(): | ||
| word_frequencies[word] = 1 | ||
| else: | ||
| word_frequencies[word] += 1 | ||
| if word not in stopWords: | ||
| if word in word_frequencies: | ||
| word_frequencies[word] += 1 | ||
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| else: | ||
| word_frequencies[word] = 1 | ||
| maximum_frequncy = max(word_frequencies.values()) | ||
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| for word in word_frequencies.keys(): | ||
| word_frequencies[word] = (word_frequencies[word]/maximum_frequncy) | ||
| for word in word_frequencies: | ||
| word_frequencies[word] = (word_frequencies[word]/maximum_frequncy) | ||
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| sentence_list = nltk.sent_tokenize(raw_text) | ||
| sentence_scores = {} | ||
| sentence_scores = {} | ||
| for sent in sentence_list: | ||
| for word in nltk.word_tokenize(sent.lower()): | ||
| if word in word_frequencies.keys(): | ||
| if len(sent.split(' ')) < 30: | ||
| if sent not in sentence_scores.keys(): | ||
| sentence_scores[sent] = word_frequencies[word] | ||
| else: | ||
| sentence_scores[sent] += word_frequencies[word] | ||
| for word in nltk.word_tokenize(sent.lower()): | ||
| if word in word_frequencies: | ||
| if len(sent.split(' ')) < 30: | ||
| if sent in sentence_scores: | ||
| sentence_scores[sent] += word_frequencies[word] | ||
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| else: | ||
| sentence_scores[sent] = word_frequencies[word] | ||
| summary_sentences = heapq.nlargest(7, sentence_scores, key=sentence_scores.get) | ||
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| summary = ' '.join(summary_sentences) | ||
| return summary | ||
| return ' '.join(summary_sentences) |
Author
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Function nltk_summarizer refactored with the following changes:
- Remove unnecessary call to keys() [×4] (
remove-dict-keys) - Swap positions of nested conditionals [×12] (
swap-nested-ifs) - Inline variable that is immediately returned (
inline-immediately-returned-variable) - Swap if/else branches [×2] (
swap-if-else-branches)
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| word_frequencies = {} | ||
| word_frequencies = {} | ||
| for word in docx: | ||
| if word.text not in stopwords: | ||
| if word.text not in word_frequencies.keys(): | ||
| word_frequencies[word.text] = 1 | ||
| else: | ||
| if word.text in word_frequencies: | ||
| word_frequencies[word.text] += 1 | ||
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| else: | ||
| word_frequencies[word.text] = 1 | ||
| maximum_frequncy = max(word_frequencies.values()) | ||
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| for word in word_frequencies.keys(): | ||
| for word in word_frequencies: | ||
| word_frequencies[word] = (word_frequencies[word]/maximum_frequncy) | ||
| # Sentence Tokens | ||
| sentence_list = [ sentence for sentence in docx.sents ] | ||
| sentence_list = list(docx.sents) | ||
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| # Sentence Scores | ||
| sentence_scores = {} | ||
| sentence_scores = {} | ||
| for sent in sentence_list: | ||
| for word in sent: | ||
| if word.text.lower() in word_frequencies.keys(): | ||
| if word.text.lower() in word_frequencies: | ||
| if len(sent.text.split(' ')) < 30: | ||
| if sent not in sentence_scores.keys(): | ||
| sentence_scores[sent] = word_frequencies[word.text.lower()] | ||
| else: | ||
| if sent in sentence_scores: | ||
| sentence_scores[sent] += word_frequencies[word.text.lower()] | ||
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| else: | ||
| sentence_scores[sent] = word_frequencies[word.text.lower()] | ||
| summarized_sentences = nlargest(7, sentence_scores, key=sentence_scores.get) | ||
| final_sentences = [ w.text for w in summarized_sentences ] | ||
| summary = ' '.join(final_sentences) | ||
| return summary | ||
| return ' '.join(final_sentences) |
Author
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Function text_summarizer refactored with the following changes:
- Remove unnecessary call to keys() [×4] (
remove-dict-keys) - Replace identity comprehension with call to collection constructor (
identity-comprehension) - Inline variable that is immediately returned (
inline-immediately-returned-variable) - Swap if/else branches [×2] (
swap-if-else-branches)
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| lang = 'english' | ||
| summary_options = {} | ||
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| if request.method == 'POST': | ||
| text = request.form.get('text') | ||
| if text: | ||
| lang = 'english' |
Author
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Function summ refactored with the following changes:
- Move assignments closer to their usage (
move-assign)
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