def myFun(model_answer, answer, out_of):
# for Keywords
if (len(answer.split())) <= 5:
return 0
k = keywordVal.givKeywordsValue(model_answer, answer)
# for Grammer
req = requests.get("https://api.textgears.com/check.php?text=" + answer + "&key=JmcxHCCPZ7jfXLF6")
no_of_errors = len(req.json()['errors'])
if no_of_errors > 5 or k == 6:
g = 0
else:
g = 1
# for Question Specific Tags
q = math.ceil(fuzz.token_set_ratio(model_answer, answer) * 6 / 100)
print("Keywords : ", k)
print("Grammar : ", g)
print("QST : ", q)
predicted = nav_test.predict(k, g, q)
result = predicted * out_of / 10
return result[0]
def givVal(model_answer, keywords, answer, out_of):
# KEYWORDS =>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# TODO : Enhacnce this thing
if (len(answer.split())) <= 5:
return 0
#
# count = 0
# keywords_count = len(keywords)
# for i in range(keywords_count):
# if keywords[i] in answer:
# # print (keywords[i])
# count = count + 1
# k = 0
# if count == keywords_count:
# k = 1
# elif count == (keywords_count - 1):
# k = 2
# elif count == (keywords_count - 2):
# k = 3
# elif count == (keywords_count - 3):
# k = 4
# elif count == (keywords_count - 4):
# k = 5
# elif count == (keywords_count - 5):
# k = 6
k = keywordVal.givKeywordsValue(model_answer, answer)
# print("checkkkkkk", k)
# GRAMMAR =>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
req = requests.get("https://api.textgears.com/check.php?text=" + answer +
"&key=JmcxHCCPZ7jfXLF6")
no_of_errors = len(req.json()['errors'])
if no_of_errors > 5 or k == 6:
g = 0
else:
g = 1
# QST =>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# print("fuzz1 ratio: ", fuzz.ratio(model_answer, answer))
q = math.ceil(fuzz.token_set_ratio(model_answer, answer) * 6 / 100)
print("Keywords : ", k)
print("Grammar : ", g)
print("QST : ", q)
predicted = nav_test.predict(k, g, q)
# Mathematical model->
# predicted / 10
# what? / out_of
result = predicted * out_of / 10
return result[0]
def myFun(model_answer, answer, para):
# KEYWORDS =>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# TODO : Enhance this thing
ka, f = keywordVal.givKeywordsValue(model_answer, answer)
# GRAMMAR =>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
cosine = ka * 100
kval = 0
if cosine > 90:
kval = 1
elif cosine > 80:
kval = 2
elif cosine > 60:
kval = 3
elif cosine > 40:
kval = 4
elif cosine > 20:
kval = 5
else:
kval = 6
if para > 5 or kval == 6:
g = 0
else:
g = 1
# QST =>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
qa = math.ceil(fuzz.token_set_ratio(model_answer, answer))
if qa > 90:
q = 1
elif qa > 80:
q = 2
elif qa > 60:
q = 3
elif qa > 40:
q = 4
elif qa > 20:
q = 5
else:
q = 6
print("Keywords : ", kval)
print("Grammar : ", g)
print("QST : ", q)
predicted = nav_test.predict(kval, g, q)
print(predicted)
return predicted