def evaluate(reference , answer):
corpus = []
for answers in reference:
text = get_text(answers)
corpus.append(text)
for answers in answer:
text = get_text(answers)
corpus.append(text)
return corpus
def ml_model():
path = r'uploads' # use your path
all_files = glob.glob(path + "/*.pdf")
corpus = []
for file in all_files:
text = get_text(file)
corpus.append(text)
vect = TfidfVectorizer(min_df=1, stop_words="english")
tfidf = vect.fit_transform(corpus)
pairwise_similarity = tfidf * tfidf.T
list1 = pairwise_similarity.toarray()
list = get_percentages(list1)
comparison = assign_comparison(len(list1))
all_files = [x.replace('uploads\\', '') for x in all_files]
final = []
count = 0
for i in range(0, len(list)):
first = comparison[i][0] - 1
second = comparison[i][1] - 1
if list[i] > 0.7:
s = " Your score is {:0.2f}".format(
(list[i] * 100)) + " % between " + str(
all_files[first]) + " and " + str(all_files[second])
final.append(s)
count += 1
return final
def evaluateupload():
app.config['UPLOAD_FOLDER'] = 'answersheets/'
uploaded_files_answers = request.files.getlist("file[]")
marks = request.form['marks']
filenames = []
for file in uploaded_files_answers:
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
filenames.append(filename)
file_path_names = []
for file in filenames:
file_path = "answersheets/" + file
file_path_names.append(file_path)
reference_text = get_text(file_path_names[0])
answer_sheet_text = get_text(file_path_names[1])
#answer_sheet_text = []
#for file in file_path_names[1:]:
# ans = get_text(file)
# answer_sheet_text.append(ans)
reference_dict = get_qno_text(reference_text)
answer_dict = get_qno_text(answer_sheet_text)
#answer_sheet_dict_list = []
#for answers in answer_sheet_text:
# answer_sheet_dict_list.append(get_qno_text(answers))
reference_keys = reference_dict.keys()
answer_keys = answer_dict.keys()
#answer_keys_list = []
#for answer_sheet in answer_sheet_dict_list:
# answer_keys_list.append(answer_sheet.keys())
list1_as_set = set(reference_keys)
intersection = list1_as_set.intersection(answer_keys)
intersection_as_list = list(intersection)
final_question_similarity = []
for questions in intersection_as_list:
reference_value = reference_dict[questions]
answer_value = answer_dict[questions]
corp_value = [reference_value, answer_value]
vect = TfidfVectorizer(min_df=1, stop_words="english")
tfidf = vect.fit_transform(corp_value)
pairwise_similarity = tfidf * tfidf.T
list1 = pairwise_similarity.toarray()
list2 = get_percentages(list1)
comparison1 = assign_comparison(len(list1))
#all_files_questions = [x.replace('answersheets\\', '') for x in all_files_questions]
#final = []
#count = 0
#for i in range(0 , len(list2)):
#first = comparison1[i][0] - 1
#second = comparison1[i][1] - 1
#if list[i] > 0.7:
#s =
#final.append(s)
#count +=1
final_question_similarity.append(list2)
final_question_similarity_list = []
for i in final_question_similarity:
for j in i:
final_question_similarity_list.append(j)
final_question_similarity_list = [
i * 100 for i in final_question_similarity_list
]
final_question_similarity_list = [
round(i, 2) for i in final_question_similarity_list
]
final_similarity_string = []
marks_list = []
total = 0
for i in range(0, len(final_question_similarity_list)):
to_say = "The answers for Q" + str(
intersection_as_list[i]) + " are " + str(
final_question_similarity_list[i]) + " percent satisfactory."
final_similarity_string.append(to_say)
if (final_question_similarity_list[i] > 90):
marks_awarded = 0
elif (final_question_similarity_list[i] > 70
and final_question_similarity_list[i] < 90):
marks_awarded = float(marks) * 0.1
elif (final_question_similarity_list[i] > 30
and final_question_similarity_list[i] < 70):
marks_awarded = float(marks) * .3
elif (final_question_similarity_list[i] > 10
and final_question_similarity_list[i] < 30):
marks_awarded = float(marks) * .18
else:
marks_awarded = 0
marks_awarded += (
final_question_similarity_list[i]) * float(marks) * 0.01
total += marks_awarded
marks_ele = "Marks for Q " + str(
intersection_as_list[i]) + " are " + str(round(
marks_awarded, 2)) + " out of " + str(marks)
marks_list.append(marks_ele)
total_marks = float(marks) * (len(final_question_similarity_list))
for i in reference_keys:
if (i not in intersection_as_list):
total_marks += int(marks)
new_marks_ele = "Marks for Q " + str(i) + " are " + str(0)
marks_list.append(new_marks_ele)
print(marks_list)
path = r'answersheets'
all_files_to_delete = glob.glob(path + "/*.pdf")
all_files_to_delete = [
x.replace('answersheets\\', '') for x in all_files_to_delete
]
for files in all_files_to_delete:
os.remove(os.path.join(app.config['UPLOAD_FOLDER'], files))
return render_template('result.html',
marks_list=marks_list,
total=total,
total_marks=total_marks)