try:
url_text_dictionary = pickle.load(open("dump/url_text_dictionary_" + term + ".p", "rb"))
except:
url_text_dictionary = process.get_text_dictionary(urls)
pickle.dump(url_text_dictionary, open("dump/url_text_dictionary_" + term + ".p", "wb"))
#print url_text_dictionary
#now that we have the entire dictionary of url:text mappings, we can analyze instead
wiki_text = process.get_text('http://en.wikipedia.org/wiki/' + term)
#creates dictionaries for the summarability and order scores
ordered_keywords = order.get_ordered_keywords(wiki_text, keywords)
wiki_distribution = process.get_density(wiki_text, keywords)
summarability = {}
orderability = {}
#calculates the summarability score in a dictionary
for url, text in url_text_dictionary.iteritems():
current_distribution = process.get_density(text,keywords)
summarability[url] = order.get_difference(wiki_distribution, current_distribution)
orderability[url] = order.get_order(current_distribution, ordered_keywords)
print summarability
print "\n\n"
print orderability
#gets the urls sorted by summarability as a list of (url, score) tuples
sorted_summarability = sorted(summarability.iteritems(), key=operator.itemgetter(1))
def index(request):
if 'query' in request.GET:
term = request.GET['query']
#replaces spaces with underscores
term = term.replace(" ", "_")
print error("****THE TERM IS " + term)
else:
#defines a term to search for
term = "Manifold"
#gets the list of all words with associated tf-idf scores
words = tfidf.get_tf_idf(term, "wikipedia")
#gets the cleaned list after removing list elements with non-alphanumeric charachters
cleaned = tfidf.parse(words)
cleaned = tfidf.remove_duplicates(cleaned)
#gets the list of top words (keywords)
keywords = tfidf.get_top_words(10, cleaned)
print error(str(keywords))
#gets all combinations of the keywords; this is a list of tuples
combinations = process.get_combinations(keywords)
try:
urls = pickle.load(open("generator/dump/urls_" + term + ".p", "rb"))
except:
#otherwise, creates and searches for the keywords
print "Searching for each keyword set instead, and saving as pickle"
urls = []
for (word1, word2) in combinations:
print "Searching for " + word1 + ", " + word2 + "."
try:
results = google.search(term + " " + word1 + " " + word2,
"com", "en", 1, 0, 3, 2.0)
for i in range(3):
next_result = results.next()
if not next_result in urls:
urls.append(next_result)
except:
print "HTML request overload."
break
pickle.dump(urls, open("generator/dump/urls_" + term + ".p", "wb"))
#gets the cleaned text for each url
try:
url_text_dictionary = pickle.load(
open("generator/dump/url_text_dictionary_" + term + ".p", "rb"))
except:
url_text_dictionary = process.get_text_dictionary(urls)
pickle.dump(
url_text_dictionary,
open("generator/dump/url_text_dictionary_" + term + ".p", "wb"))
urls = url_text_dictionary.keys()
pickle.dump(urls, open("generator/dump/urls_" + term + ".p", "wb"))
#gets the titles for each url
try:
print error("Loading titles.")
url_titles = pickle.load(
open("generator/dump/url_titles_" + term + ".p", "rb"))
print error("Loaded existing titles.")
except:
print error("Getting titles.")
url_titles = process.get_titles(urls)
pickle.dump(url_titles,
open("generator/dump/url_titles_" + term + ".p", "wb"))
#print url_text_dictionary
#now that we have the entire dictionary of url:text mappings, we can analyze instead
wiki_text = process.get_text('http://en.wikipedia.org/wiki/' + term)
#creates dictionaries for the summarability and order scores
ordered_keywords = order.get_ordered_keywords(wiki_text, keywords)
#print error(str(ordered_keywords))
wiki_distribution = process.get_density(wiki_text, keywords)
summarability = {}
orderability = {}
#calculates the summarability score in a dictionary
for url, text in url_text_dictionary.iteritems():
current_distribution = process.get_density(text, keywords)
summarability[url] = order.get_difference(wiki_distribution,
current_distribution)
orderability[url] = order.get_order(current_distribution,
ordered_keywords)
#print summarability
print "\n\n"
#print orderability
#gets the urls sorted by summarability as a list of (url, score) tuples
sorted_summarability = sorted(summarability.iteritems(),
key=operator.itemgetter(1))
sorted_orderability = sorted(orderability.iteritems(),
key=operator.itemgetter(1))
combined = order.combine_summarability_and_orderability(
sorted_summarability, orderability, url_titles)
print "Titles: " + str(url_titles)
with_titles = []
#adds in the title to the tuple
for (url, score) in combined:
with_titles.append((url, score, url_titles[url]))
return render(request, 'generator/index.html',
({
'summarability': json.dumps(sorted_summarability),
'orderability': json.dumps(sorted_orderability),
'combined': json.dumps(combined),
'with_titles': json.dumps(with_titles)
}))
url_text_dictionary = pickle.load(
open("generator/dump/url_text_dictionary_" + term + ".p", "rb"))
except:
url_text_dictionary = process.get_text_dictionary(urls)
pickle.dump(
url_text_dictionary,
open("generator/dump/url_text_dictionary_" + term + ".p", "wb"))
#print url_text_dictionary
#now that we have the entire dictionary of url:text mappings, we can analyze instead
wiki_text = process.get_text('http://en.wikipedia.org/wiki/' + term)
#creates dictionaries for the summarability and order scores
ordered_keywords = order.get_ordered_keywords(wiki_text, keywords)
wiki_distribution = process.get_density(wiki_text, keywords)
summarability = {}
orderability = {}
#calculates the summarability score in a dictionary
for url, text in url_text_dictionary.iteritems():
current_distribution = process.get_density(text, keywords)
summarability[url] = order.get_difference(wiki_distribution,
current_distribution)
orderability[url] = order.get_order(current_distribution, ordered_keywords)
print summarability
print "\n\n"
print orderability
#gets the urls sorted by summarability as a list of (url, score) tuples
def index(request):
if 'query' in request.GET:
term = request.GET['query']
#replaces spaces with underscores
term = term.replace(" ", "_")
print error("****THE TERM IS " + term)
else:
#defines a term to search for
term = "Manifold"
#gets the list of all words with associated tf-idf scores
words = tfidf.get_tf_idf(term, "wikipedia")
#gets the cleaned list after removing list elements with non-alphanumeric charachters
cleaned = tfidf.parse(words)
cleaned = tfidf.remove_duplicates(cleaned)
#gets the list of top words (keywords)
keywords = tfidf.get_top_words(10, cleaned)
print error(str(keywords))
#gets all combinations of the keywords; this is a list of tuples
combinations = process.get_combinations(keywords)
try:
urls = pickle.load(open ("generator/dump/urls_" + term + ".p", "rb"))
except:
#otherwise, creates and searches for the keywords
print "Searching for each keyword set instead, and saving as pickle"
urls = []
for (word1, word2) in combinations:
print "Searching for " + word1 + ", " + word2 + "."
try:
results = google.search(term + " " + word1 + " " + word2, "com", "en", 1, 0, 3, 2.0)
for i in range(3):
next_result = results.next()
if not next_result in urls:
urls.append(next_result)
except:
print "HTML request overload."
break
pickle.dump(urls, open("generator/dump/urls_" + term + ".p", "wb"))
#gets the cleaned text for each url
try:
url_text_dictionary = pickle.load(open("generator/dump/url_text_dictionary_" + term + ".p", "rb"))
except:
url_text_dictionary = process.get_text_dictionary(urls)
pickle.dump(url_text_dictionary, open("generator/dump/url_text_dictionary_" + term + ".p", "wb"))
urls = url_text_dictionary.keys()
pickle.dump(urls, open("generator/dump/urls_" + term + ".p", "wb"))
#gets the titles for each url
try:
print error("Loading titles.")
url_titles = pickle.load(open("generator/dump/url_titles_" + term + ".p", "rb"))
print error("Loaded existing titles.")
except:
print error("Getting titles.")
url_titles = process.get_titles(urls)
pickle.dump(url_titles, open("generator/dump/url_titles_" + term + ".p", "wb"))
#print url_text_dictionary
#now that we have the entire dictionary of url:text mappings, we can analyze instead
wiki_text = process.get_text('http://en.wikipedia.org/wiki/' + term)
#creates dictionaries for the summarability and order scores
ordered_keywords = order.get_ordered_keywords(wiki_text, keywords)
#print error(str(ordered_keywords))
wiki_distribution = process.get_density(wiki_text, keywords)
summarability = {}
orderability = {}
#calculates the summarability score in a dictionary
for url, text in url_text_dictionary.iteritems():
current_distribution = process.get_density(text,keywords)
summarability[url] = order.get_difference(wiki_distribution, current_distribution)
orderability[url] = order.get_order(current_distribution, ordered_keywords)
#print summarability
print "\n\n"
#print orderability
#gets the urls sorted by summarability as a list of (url, score) tuples
sorted_summarability = sorted(summarability.iteritems(), key=operator.itemgetter(1))
sorted_orderability = sorted(orderability.iteritems(), key=operator.itemgetter(1))
combined = order.combine_summarability_and_orderability(sorted_summarability, orderability, url_titles)
print "Titles: " + str(url_titles)
with_titles = []
#adds in the title to the tuple
for (url, score) in combined:
with_titles.append((url, score, url_titles[url]))
return render(request, 'generator/index.html', ({'summarability': json.dumps(sorted_summarability), 'orderability': json.dumps(sorted_orderability), 'combined': json.dumps(combined), 'with_titles': json.dumps(with_titles)}))