def query(query):
returns = search(query, 10)
if len(returns) > 0:
responses = list()
print(len(returns))
return render_template('search_results.html', results=returns)
else:
return "No Sites found!"
def get_search():
query = request.query.query.lower() or ""
page = int(request.query.page)
sort = request.query.sort
results, max_pages, total_results = searching.search(query, page, sort)
return template("results.tpl",
query=query,
results=results,
queries=None,
sort=sort,
index='code',
max_pages=max_pages,
page=page,
total_results=total_results)
def main():
if request.args.get("search") is None:
return render_template("index.html")
if request.method == "GET":
search_arg = request.args.get("search")
print(search_arg)
returns = search(search_arg, 10, "de")
if len(returns) > 0:
responses = list()
print(len(returns))
return render_template('search_results.html',
results=returns,
search=search_arg)
else:
return "No Sites found!"
def findJobsButton():
search_for = request.form['search-for']
avoiding = request.form['avoiding']
location = request.form['location']
fulltime = request.form.get("fulltime") != None
parttime = request.form.get("parttime") != None
casual = request.form.get("casual") != None
gumtree = request.form.get("gumtree") != None
indeed = request.form.get("indeed") != None
jora = request.form.get("seek") != None
# find User-Agent of current browser for Beautiful Soup
# user_agent = request.user_agent.string
# HEADERS = {"User-Agent":f"{user_agent}"}
# create a words array from words in text field that were separated by commas
words_to_search = format_text_field(search_for)
words_to_avoid = format_text_field(avoiding)
positions_to_render = searching.search(gumtree, jora, indeed,
words_to_search, words_to_avoid,
location, fulltime, parttime,
casual)
return render_template('results.html', positions=positions_to_render)\
def my_form_post():
arrayInput = request.form['array']
item = request.form['item']
array = data[arrayInput]
return render_template("dataPreview.html", year = year, minsAndMax = minsAndMax, df = df, sortedTL = sortedTeamsList , sortedWL = sortedWinList, sortedWLPCT = sortedWinLossPCT, search = search("ALL", array , item))
print(len(wordkey))
print("loading complete!")
print("------------Search Start------------")
while True:
print("Please input the query statement:")
#获取输入
statement = input()
#分词，并标记词性
tokens_tag = pos_tag(nltk.word_tokenize(statement))
#输入单词预处理
tokens = stemming.stemmer_lemma(tokens_tag)
tokens = [word.lower() for word in tokens if word.isalpha()]
print(tokens)
#查询相关文件集合
docset = searching.search(wordindex, tokens)
#获取文件对应的分数（最高分的k个）
scorelist = getscore.topK(10, wordindex, fileNum, tokens, docset)
#无相关返回
if len(scorelist) == 0:
print("无相关结果")
#打印结果
for doc, score in scorelist:
doc = doc.rstrip()
time, url, context = loading.getfile(path, doc, tokens)
print("------------------------------------")
print("文章名： " + doc)
print("分数为： " + "%.3f" % score)
print("时间为： " + time)
print("地址为： " + url)
print("相关内容为：")