def analyze():
query = request.form['query']
a, b, c = query_module.query_structure(query)
clean_query_root = a
query_synonym_root = b
query_suggestion_root = c
query_analyse = query
result_tm = ranking_text_mining.ranking(a, b, c)
result_nlp, ngram_considered = ranking_nlp.rank_ngram(query)
result, result_score = combined_ranking.ranking(result_tm, result_nlp)
result_analyse = result
result_score_analyse = result_score
score=[]
for key, value in result_score.items():
score.append(value)
return render_template("analysis.html",
query=query_analyse,
query_root=clean_query_root,
synonym=query_synonym_root,
suggestion=query_suggestion_root,
result_title=result_analyse,
result_score=result_score_analyse,
score=score,
ngram=ngram_considered)
def index():
query = request.form['query']
a, b, c=query_module.query_structure(query)
result_tm=ranking_text_mining.ranking(a, b, c)
result_nlp, ngram_considered=ranking_nlp.rank_ngram(query)
result, result_score=combined_ranking.ranking(result_tm,result_nlp)
return render_template("result.html",result=result)
list_final_query = []
for i in range(10):
temp = []
temp.append(list_combined_query_str[i])
temp.append(list_random_gram2[i])
temp.append(list_random_gram3[i])
temp = ' '.join(temp)
list_final_query.append(temp)
#print(list_final_query)
#print(list_random_url_50)
count = 0
for iter, temp_query in enumerate(list_final_query):
query = temp_query
a, b, c = query_module.query_structure(query)
result_tm = ranking_text_mining.ranking(a, b, c)
result_nlp, ngram_considered = ranking_nlp.rank_ngram(query)
result, result_score = combined_ranking.ranking(result_tm, result_nlp)
#result=result[:3]
print(result)
print(list_random_url_50[iter])
if list_random_url_50[iter] in result:
count = count + 1
print("Count=", count)
print("Count=", count)