def setUp(self):
'''
In this method we create an indexer, create a text file,
index it, then delete the file and the indexer.
Then create an object of SearchEngine()
'''
indexer = ToIndex('database')
self.maxDiff = None
text = open('test_text.txt', 'w')
text.write('Ах, не говорите мне про Австрию! \
Я ничего не понимаю, может быть')
text.close()
another_text = open('another_test_text.txt', 'w')
another_text.write('но Ах Австрия никогда не хотела и не хочет войны.\
Она предает нас')
another_text.close()
text1 = open('test_text1.txt', 'w')
text1.write('ooh la la мама мыла раму123 frf34')
text1.close()
text2 = open('test_text2.txt', 'w')
text2.write('мама мыла окно')
text2.close()
text3 = open('test_text3.txt', 'w')
text3.write('мама мыла еще что-нибудь')
text3.close()
indexer.index_by_line('test_text.txt')
indexer.index_by_line('another_test_text.txt')
del indexer
self.search_eng = SearchEngine('database')
def print_result(self):
self.query = self.entry.get()
My_search_engine = SearchEngine(self.db)
self.top_level = Toplevel(self.tk)
self.top_level.geometry("500x500")
self.top_level.title("The Result")
result = My_search_engine.print_results(self.query)
return_doc = My_search_engine.get_return_doc()
if len(self.query) == 0:
label = Label(self.top_level, text="Please enter your text again")
elif len(result) == 0:
label = Label(self.top_level, text="No result found")
else:
label = Label(self.top_level,
text="These are the top 20 results for query " +
self.query + ": ")
label.pack()
i = 0
for single_result in result:
single_link_and_snippet = Label(
self.top_level,
text=single_result + " Its snippet: " +
self.snippet_dict[return_doc[i]] + "...",
fg="blue",
cursor="hand2")
single_link_and_snippet.pack()
#single_snippet = Label(self.top_level, text="its snippet: "+ self.snippet_dict[return_doc[i]])
i += 1
single_link_and_snippet.bind("<Button-1>", self.show_content)
def __main__(argv):
#%%
logger = logging.getLogger(__name__)
logger.info("VECTOR MODEL INFORMATION RETRIEVAL SYSTEM START")
gli = InvertedIndexGenerator(GLI_CONFIG_FILE)
gli.run()
gli.write_output()
index = Indexer(INDEX_CONFIG_FILE, TfidfVectorizer)
index.run()
index.write_output()
pc = QueryProcessor(PC_CONFIG_FILE)
pc.run()
pc.write_output()
buscador = SearchEngine(BUSCA_CONFIG_FILE, TfidfVectorizer)
buscador.run()
buscador.write_output()
#%%
avaliador = Evaluator(AVAL_CONFIG_FILE)
avaliador.run()
avaliador.write_output()
logger.info("VECTOR MODEL INFORMATION RETRIEVAL SYSTEM DONE")
def test_search_engine():
"""Tests the SearchEngine class"""
engine = SearchEngine('test_search')
assert_equals(None, engine.search('asdf'))
assert_equals(['test_search/file.txt'], engine.search('dog'))
assert_equals(['test_search/file.txt', 'test_search/file1.txt'],
engine.search('test'))
def test_find_pages(self):
page_1 = WebPage("http://tradein.nissan.co.jp/")
page_1.title = "自動車の下取りと売却"
page_1.snippet = "自動車には下取りをする方法がけっこうある。"
page_2 = WebPage("http://www.link-nexus.com/")
page_2.title = "自動車の下取りと販売"
page_2.snippet = "あばばばばば"
page_3 = WebPage("http://toyota.jp/service/tradein/dc/top")
page_3.title = "下取り参考価格情報"
page_3.snippet = "下取りと販売ですよプロデューサーさん"
search_engine = SearchEngine()
search_engine.material_pages = [page_1, page_2, page_3]
search_engine.hint_word = "自動車"
search_engine.action_word = "下取り"
search_engine.find_pages_including_related_words()
self.assertEqual(search_engine.result_pages[0], page_1)
self.assertEqual(search_engine.result_pages[1], page_2)
self.assertEqual(search_engine.result_pages[2], page_3)
search_engine.count_action_words()
self.assertEqual(search_engine.action_words_count, {"販売": 2, "売却": 1})
search_engine.sort_action_words_count()
self.assertEqual(search_engine.sorted_action_words, [{"word": "販売", "count": 2}, {"word": "売却", "count": 1}])
def searchidlist(key, selected=0):
global page
global doc_id
se = SearchEngine('../config.ini', 'utf-8')
flag, id_scores = se.search(key, selected)
# 返回docid列表
doc_id = [i for i, s in id_scores]
# TODO 根据用户等级过滤掉部分数据
global dir_path, db_path, rank
conn = sqlite3.connect(db_path)
c = conn.cursor()
doc_id_rank = []
for id in doc_id:
c.execute("SELECT * FROM news WHERE id=?", (id, ))
fetch = c.fetchone()
if fetch[5] < rank:
doc_id_rank.append(id)
doc_id.clear()
doc_id = doc_id_rank.copy()
print("**rank**: ", rank)
print("**doc_id**: ", doc_id)
page = []
for i in range(1, (len(doc_id) // 10 + 2)):
page.append(i)
return flag, page
def main():
# Instance
generator = SearchEngineGenerator()
search = SearchEngine()
# Generate invert index
ans = input('Do you want to build scores?[type \'y\' to build or pass]: ')
if ans == 'y':
generator()
# generator()
while True:
query = input('2018-26161> ')
if query in '/quit':
print("Okay bye!")
break
elif query.startswith('-run'):
path = query.split()[1].strip()
if not os.path.exists(path):
print("경로가 올바르지 않습니다.")
continue
# path = 'data/prj2.sched'
simulator = ScheduleSimulator(path)
simulator()
results = search(query)
for doc in results:
print(search.result_formatting(doc))
def enhance(self, num=10):
from search_engine import SearchEngine
se = SearchEngine()
print("*[Data Organizer] Downloading Google Search Results")
results = se.get_data(queries=self.df['cleaned_text'])
results_t = []
d = {}
for r in results.items():
tu = []
for url, text in zip(r[1]['url'], r[1]['text']):
tu.append((url, text))
d = {'source_text': r[0], 'result': tu}
results_t.append(d)
self.df['google-search'] = [i for i in range(len(self.df))]
for d, i in zip(self.df['cleaned_text'], range(len(self.df))):
for r in results_t:
if str(r['source_text']) == str(d):
self.df['google-search'][i] = r['result']
q = []
for gs in self.df['google-search']:
types = dict((el, []) for el in list(self.domains))
for result in gs:
_type = self.in_domain(result[0])
if _type != "": types[_type].append(result)
q.append(types)
self.df['types'] = q
return self.df
def search(query, count):
search_engine = request.forms.get('search_engine')
engine = SearchEngine()
if search_engine == 'google':
pages = engine.google_search(query, count)
elif search_engine == 'bing':
pages = engine.bing_search(query, count)
return pages
def test_search_engine_3():
test = SearchEngine('test_dir3')
# Tests Empty Directory
assert_equals(1, test._num_docs)
assert_equals({}, test._docs)
assert_equals(0, test._calculate_idf('Samsung'))
assert_equals(None, test.search('Samsung'))
def search(query, count):
search_engine = request.forms.get('search_engine')
engine = SearchEngine()
if search_engine == 'google':
pages = engine.google_search(query, count)
elif search_engine == 'bing':
pages = engine.bing_search(query, count)
return pages
def query_db(query, tables, Descriptions):
files = get_codes(tables)
desc_files = get_descriptions(Descriptions)
docs = generate_documents(files, desc_files)
engine = SearchEngine(docs)
search_results = engine.search(query)
return search_results
def start():
searchEngine = SearchEngine("data/")
print("For finish write 'exit'")
request = input("User: "******"exit"):
answer = searchEngine.search(request)
print_result(answer)
request = input("User: ")
def search_by_google_or_bing(request):
query = request.form["query"]
search_engine_name = request.form['search_engine']
search_engine = SearchEngine()
if search_engine_name == 'google':
pages = search_engine.google_search(query, 1)
else:
pages = search_engine.bing_search(query, 1)
return pages
def setUp(self):
self.engine = SearchEngine('database')
self.engine.database.update(database)
test = open("test1.txt", 'w')
test.write(test1)
test.close()
test = open("test2.txt", 'w')
test.write(test2)
test.close()
class TestSearchEngine(unittest.TestCase):
def setUp(self):
'''
Create an indexer, create a text file,
index it, then delete the file and the indexer.
Then create an object of SearchEngine()
'''
indexer = ToIndex('database')
self.maxDiff = None
text = open('test_text.txt', 'w')
text.write('mama мыла ramu')
text.close()
indexer.index_by_line('test_text.txt')
del indexer
self.search_eng = SearchEngine('database')
def tearDown(self):
'''
In this method we destroy an object of SearchEngine()
and delete 'database'.
'''
del self.search_eng
files = os.listdir()
for single_file in files:
if single_file == "database":
os.remove(single_file)
else:
if single_file.startswith('database.'):
os.remove(single_file)
os.remove('test_text.txt')
def test_empty_query(self):
'''
Test that ValueError is raised if the query is an empty string.
'''
with self.assertRaises(ValueError):
self.search_eng.search("")
def test_query_is_a_number(self):
"""
If the query is a number raise TypeError.
"""
with self.assertRaises(TypeError):
self.search_eng.search(42)
def test_program_runs_okay(self):
'''
Test that program runs as expected given there is one word in the query
and one file in the database.
'''
search_res = self.search_eng.search('мыла')
ref_dict = {'test_text.txt': [PositionByLine(5, 9, 0)]}
self.assertEqual(ref_dict, search_res)
def find_related_action_words():
search_engine = SearchEngine()
search_engine.action_word = request.form['action_word']
search_engine.hint_word = request.form['hint_word']
search_engine.find_related_action_words()
search_engine.count_action_words()
search_engine.sort_action_words_count()
for elem in search_engine.sorted_action_words:
elem['expanded_query'] = search_engine.action_word + ' ' + search_engine.hint_word + ' ' + elem['word']
return render_template('find_related_action_words.tmpl', items=search_engine.result_pages, sorted_action_words=search_engine.sorted_action_words, found_pages=search_engine.material_pages, query=search_engine.actual_query)
class QASystem(object):
def __init__(self):
if exists('database.dat'):
# deserialize database is much faster.
print('deserialize the QA database...')
self.search_engine = SearchEngine('cc/cppjieba/dict',
'database.dat')
else:
# load database from txt is slower.
print('load from QA database from txt format...')
self.search_engine = SearchEngine('cc/cppjieba/dict')
self.search_engine.loadFromTxt('question_answer.txt')
self.search_engine.save('database.dat')
self.predictor = Predictor()
def query(self, question, count=3):
answer_scores = self.search_engine.query(question, count)
answer_totalscores = dict()
for answer, match in answer_scores.items():
_, relevance = self.predictor.predict(question, answer)
answer_totalscores[answer] = (
log(max(match[0], sys.float_info.min)) * relevance,
match[1],
)
return answer_totalscores
def updateDB(self, file):
assert type(file) is str
self.search_engine.loadFromTxt(file)
self.search_engine.save('database.dat')
class QASystem(object):
def __init__(self):
if exists('database.dat'):
# deserialize database is much faster.
print('deserialize the QA database...')
self.search_engine = SearchEngine('cppjieba/dict', 'database.dat')
else:
# load database from txt is slower.
print('load from QA database from txt format...')
self.search_engine = SearchEngine('cppjieba/dict')
self.search_engine.loadFromTxt('question_answer.txt')
self.search_engine.save('database.dat')
self.predictor = Predictor()
def query(self, question, count=3):
answer_scores = self.search_engine.query(question, count)
answer_totalscores = dict()
for answer, match in answer_scores.items():
_, relevance = self.predictor.predict(question, answer)
answer_totalscores[answer] = exp(match) + exp(relevance)
# sort in descend order of total score
sorted(answer_totalscores, key=operator.itemgetter(1), reverse=True)
return answer_totalscores
def updateDB(self, file):
assert type(file) is str
self.search_engine.loadFromTxt(file)
self.search_engine.save('database.dat')
def searchidlist(key, selected=0):
global page
global doc_id
se = SearchEngine('../config.ini', 'utf-8')
flag, id_scores = se.search(key, selected)
# 返回docid列表
doc_id = [i for i, s in id_scores]
page = []
for i in range(1, (len(doc_id) // 10 + 2)):
page.append(i)
return flag,page
def test_searchengine_search():
'''
Tests the search function.
'''
test = SearchEngine(DIRECTORY)
expected = [FILE1]
assert_equals(expected, test.search('super'))
assert_equals(None, test.search('croissant'))
expected = [FILE1, FILE2]
assert_equals(expected, test.search('Apple pie'))
def searchidlist(key, selected=0):
global page
global doc_id
se = SearchEngine('../config.ini', 'utf-8')
flag, id_scores = se.search(key, selected)
# 返回docid列表
doc_id = [i for i, s in id_scores]
page = []
for i in range(1, (len(doc_id) // 10 + 2)):
page.append(i)
return flag, page
def setUp(self):
index = indexer.Indexer('dbase')
f = open('test.txt', 'w')
f.write('this is\ntest')
f.close()
t = open('tst.txt', 'w')
t.write('test')
t.close()
index.indexing_with_lines('test.txt')
index.indexing_with_lines('tst.txt')
del index
self.s = SearchEngine('dbase')
def setUp(self):
index = indexer.Indexer('dbase')
f = open('test.txt', 'w')
f.write('this is a test required for helping students create a test\n')
f.write(' professor required to write a test first')
f.close()
t = open('tst.txt', 'w')
t.write('test is required. On the other hand...')
t.close()
index.indexing_with_lines('test.txt')
index.indexing_with_lines('tst.txt')
del index
self.s = SearchEngine('dbase')
def __init__(self):
if exists('database.dat'):
# deserialize database is much faster.
print('deserialize the QA database...')
self.search_engine = SearchEngine('cppjieba/dict', 'database.dat')
else:
# load database from txt is slower.
print('load from QA database from txt format...')
self.search_engine = SearchEngine('cppjieba/dict')
self.search_engine.loadFromTxt('question_answer.txt')
self.search_engine.save('database.dat')
self.predictor = Predictor()
def test_find_related_action_words_from_clueweb(self):
se = SearchEngine()
se.hint_word = '大学'
se.action_word = '入学'
se.set_solr_query()
se.find_related_action_words_from_clueweb()
self.assertEqual(len(se.result_pages), 1)
def setUp(self):
'''
Create an indexer, create a text file,
index it, then delete the file and the indexer.
Then create an object of SearchEngine()
'''
indexer = ToIndex('database')
self.maxDiff = None
text = open('test_text.txt', 'w')
text.write('mama мыла ramu')
text.close()
indexer.index_by_line('test_text.txt')
del indexer
self.search_eng = SearchEngine('database')
def test_searchengine_fields():
'''
Tests the fields of a search engine after construction.
'''
test = SearchEngine(DIRECTORY)
doc1 = Document(FILE1)
doc2 = Document(FILE2)
doc3 = Document(FILE3)
assert_equals(3, test._total_documents)
expected = {
'i': [doc1, doc2, doc3],
'like': [doc1, doc2],
'apple': [doc1],
'pie': [doc1, doc2],
'is': [doc1, doc3],
'super': [doc1],
'duper': [doc1],
'cool': [doc1, doc3],
'also': [doc2],
'chocolate': [doc2, doc3],
'cake': [doc3],
'guess': [doc3]
}
assert_equals(expected, test._all_terms)
def test_find_related_action_words_from_clueweb(self):
se = SearchEngine()
se.hint_word = "大学"
se.action_word = "入学"
se.set_solr_query()
se.find_related_action_words_from_clueweb()
self.assertEqual(len(se.result_pages), 1)
class TestSearchEngine(unittest.TestCase):
def setUp(self):
self.engine = SearchEngine('database')
self.engine.database.update(database)
test = open("test1.txt", 'w')
test.write(test1)
test.close()
test = open("test2.txt", 'w')
test.write(test2)
test.close()
def test_empty(self):
result = self.engine.single_token_search('')
self.assertEqual(result, {})
def test_search_one(self):
result = self.engine.single_token_search('for')
self.assertEqual(result, {'test2.txt': [(15, 18)]})
def test_search_many_one(self):
result = self.engine.multiple_tokens_search('testing')
self.assertEqual(
result, {
'test1.txt': [Position_with_lines(11, 18, 0)],
'test2.txt': [Position_with_lines(0, 7, 0)]
})
def test_search_many_two(self):
result = self.engine.multiple_tokens_search('testing ground')
self.assertEqual(
result, {
'test1.txt': [
Position_with_lines(11, 18, 0),
Position_with_lines(19, 24, 0)
],
'test2.txt':
[Position_with_lines(0, 7, 0),
Position_with_lines(8, 14, 0)]
})
def tearDown(self):
if 'test1.txt' in os.listdir(os.getcwd()):
os.remove('test1.txt')
if 'test2.txt' in os.listdir(os.getcwd()):
os.remove('test2.txt')
def search(self, key_search):
data = self.select('content')
searcher = SearchEngine(key_search)
result = []
for d in data:
content = d.get('content', ' ')
if len(content.strip()) < 4:
continue
if len(
re.findall('|'.join(key_search.lower().split()),
content.lower())) == 0:
continue
titles = d.get('tieu_de', ' ').split('|')
score = 0
try:
ok = True
for i, title in enumerate(titles):
score_tieu_de, _ = searcher.LCS4Sentence(u'' + title)
score += (i + 1) / len(titles) * score_tieu_de * 2
sentences = content.split('.')
score_content = 0
n_content = 0
for sentence in sentences:
s_content, index = searcher.LCS4Sentence(u'' + sentence)
# score_content += s_content
# if s_content > 0:
# n_content += 1
if s_content > score_content:
score_content = s_content
score_content = 2 * score_content / (n_content + 1)
_, index = searcher.LCS4Sentence(u'' + content)
score += score_content
if score < 0.3:
continue
index = ';'.join(['{0}-{1}'.format(s, e) for s, e in index])
reference = d.get('reference') # d['stt']
result.append({
'reference': reference,
'title': title.split('|')[-1],
'content': content,
'score': score,
'index': index
})
except Exception as e:
print(e)
ok = False
# from text_mining.search_engine.search_engine import SearchEngine
searcher = SearchEngine(key_search)
if not ok:
print('error')
# print(searcher.LCS4Sentence('haha'))
result = [r for r in result if r['score'] > 0.05]
result = sorted(result, key=lambda r: r['score'], reverse=True)
result = result[:20]
return result
def test_clueweb_search(self):
se = SearchEngine()
se.hint_word = '大学'
se.action_word = '入学'
se.set_solr_query()
texts = se.clue_web_search(se.solr_query)
self.assertEqual(len(texts), 50)
self.assertEqual('大学' and '入学' in texts[0], True)
def get_all_search_results(self, request, **values):
if not request.args.has_key('q'):
raise BadRequest('please provide a search query \'q\'')
engine = SearchEngine()
try:
query = request.args['q']
limit = int(request.args['limit']) if request.args.has_key('limit') else 10
service = request.args['service'] if request.args.has_key('service') else 'all'
except:
return BadRequest('error parsing request. make sure limit is an integer')
sc_res = []
yt_res = []
# query each of the services provided by the engine
if service == 'all' or service == 'soundcloud':
sc_res = engine.soundcloud_query(query, limit)
if service == 'all' or service == 'youtube':
yt_res = engine.youtube_query(query, limit)
return self.render_template('results.txt', results=sc_res + yt_res)
def main():
print("Building SearchEngine")
engine = SearchEngine()
answer = 'y'
while answer == 'y':
term = input('Enter Search Term:')
ranking = engine.search(term)
print("Displaying results for " + "'" + term + "':")
if ranking is None:
print("No results")
rank = 1
for doc in ranking:
print(' ' + str(rank) + '. ' + doc)
rank += 1
print()
answer = ''
while not (answer == 'y' or answer == 'n'):
answer = input('Would you like to search another term (y/n) ')
def main():
test1 = Document('test_docs/test1.txt')
test2 = Document('test_docs/test2.txt')
test3 = Document('test_docs/test3.txt')
test4 = Document('test_docs/test4.txt')
test_search1 = SearchEngine('test_docs')
test_document(test1, test2, test3, test4)
test_single(test_search1)
test_mulit(test_search1)
def search_service(request):
"""
Parses http json request and returns list of articles and their
scores depending on the input query
Args:
request: http POST body request as json
Returns:
list of articles with their respective scores
"""
json = request.get_json()
logging.info(json)
if "query" not in json:
message = "ValueError: Expected 'query' field in json body missing"
error = {"error": {"message": message}}
logging.error(message)
return error
query = json["query"]
search = SearchEngine(keywords_weight=constants.KEYWORDS_WEIGHT)
try:
score_per_article = search.query(query)
except Exception as e:
error = {
"error": {
"message": getattr(e, 'message', str(e)),
"trace": traceback.format_exc()
}
}
logging.error(error['error'])
return error
# sorts dictionary by value in DESC order
articles_sorted = [
k for k, v in sorted(
score_per_article.items(), key=lambda item: item[1], reverse=True)
]
response = {"articles": articles_sorted}
logging.info(response)
return response
def test_clueweb_search(self):
se = SearchEngine()
se.hint_word = "大学"
se.action_word = "入学"
se.set_solr_query()
texts = se.clue_web_search(se.solr_query)
self.assertEqual(len(texts), 50)
self.assertEqual("大学" and "入学" in texts[0], True)
def search_and_fetch_30_pages():
search_engine = SearchEngine()
pages = search_engine.google_search(QUERY, 3)
[page.fetch_html() for page in pages]
return pages
from search_engine import SearchEngine
if __name__ == '__main__':
query = '花粉症対策'
engine = SearchEngine()
pages = engine.google_search('"' + 'で' + query + '"', 3)
for page in pages:
try:
i = page.title.index('で' + query)
print(page.title[(i - 10):])
except ValueError:
try:
i = page.snippet.index('で' + query)
print(page.snippet[(i - 10):])
except:
pass
from search_engine import SearchEngine
search = SearchEngine()
welcome = "Seach by \"Title\", \"Call Number\", \"Subjects\", \"Other\" or \"Quit\": "
while True:
input = raw_input(welcome)
if input.upper().strip() == "QUIT":
break
search_string = raw_input("Please enter a search string: ")
if input.upper().strip() == "TITLE":
search.search_by_title(search_string)
elif (input.upper().strip() == "CALL NUMBER"
or input.upper().strip() == "CALLNUMBER"):
search.search_by_call_number(search_string)
elif (input.upper().strip() == "SUBJECTS"
or input.upper().strip() == "SUBJECT"):
search.search_by_subjects(search_string)
elif input.upper().strip() == "OTHER":
search.search_by_other(search_string)
def search_in_clueweb_with_expanded_query():
search_engine = SearchEngine()
search_engine.action_word = request.form['action_word']
search_engine.hint_word = request.form['hint_word']
search_engine.find_related_action_words_with_google()
search_engine.count_action_words()
search_engine.sort_action_words_count()
search_engine.pick_sorted_action_words_more_than_1_count()
results = []
for elem in search_engine.sorted_action_words_more_than_1_count:
elem['expanded_query'] = search_engine.action_word + ' ' + search_engine.hint_word + ' ' + elem['word']
url = 'http://karen.dl.local:8983/solr/ClueWeb09ja/select?q=' + elem['expanded_query'] + '&wt=xml'
web_page = WebPage(url)
web_page.fetch_xml()
web_page.pick_texts_to_result_pages()
# クエリ1つごとに結果xmlページがある
# 結果xmlページの内容を1ページずつWebPageオブジェクトにしてresult_pagesとして1クエリに対応する結果ページに持たせる
for result_page in web_page.result_pages:
# result_page.text_body
result_page.set_lines_from_texts()
result_page.set_line_nums_with_word(search_engine.action_word)
result_page.set_line_nums_around_action_word()
result_page.set_line_clusters_around_action_word()
# web_page.result_pages[0].line_clusters_around_action_word
results.append({'pages': web_page.result_pages, 'expanded_query': elem['expanded_query']})
return render_template('search_in_clueweb_with_expanded_query.tmpl',
results=results)
def search():
keyword = request.args.get('keyword', '')
engine = SearchEngine()
pages_searched = engine.search(keyword)
return render_template(
'results.html', keyword=keyword, pages_searched=pages_searched)
def google_search(self):
engine = SearchEngine()
pages = engine.google_search(self.query, self.search_num)
return pages
""" fetchnetapps = fetchNetApps(applications) fetchnetapps.download_apps() fetchnetapps.decompress_apps() dbhandle = dbHandle(constants, functions, structures, applications) apps_in_analysis_db = dbhandle.apps_analysis_is_done() search_engine = SearchEngine(all_socket_api) #count all socket APIs under applications directory apps_dir = os.path.join(os.environ['PWD'],'applications') #buggy Here #for name in os.listdir(apps_dir): for conf_name in applications: #check whether it is the right application download based on configuration file # FIX ME: not try to get #for conf_name in applications: for name in os.listdir(apps_dir): path = os.path.join(apps_dir, name) #print name #print conf_name
def do_something():
query = request.args.get('query', '')
se = SearchEngine()
results = se.make_query(query)
return render_template('result.html', data=results)
