def get_problem_list(request, course_id):
"""
Get all the problems for the given course id
Returns a json dict with the following keys:
success: bool
problem_list: a list containing json dicts with the following keys:
each dict represents a different problem in the course
location: the location of the problem
problem_name: the name of the problem
num_graded: the number of responses that have been graded
num_pending: the number of responses that are sitting in the queue
min_for_ml: the number of responses that need to be graded before
the ml can be run
"""
_check_access(request.user, course_id)
try:
response = staff_grading_service().get_problem_list(
course_id, unique_id_for_user(request.user))
response = json.loads(response)
problem_list = response['problem_list']
valid_problem_list = []
for i in xrange(0, len(problem_list)):
#Needed to ensure that the 'location' key can be accessed
try:
problem_list[i] = json.loads(problem_list[i])
except Exception:
pass
if does_location_exist(course_id, problem_list[i]['location']):
valid_problem_list.append(problem_list[i])
response['problem_list'] = valid_problem_list
response = json.dumps(response)
return HttpResponse(response, mimetype="application/json")
except GradingServiceError:
#This is a dev_facing_error
log.exception("Error from staff grading service in open "
"ended grading. server url: {0}".format(
staff_grading_service().url))
#This is a staff_facing_error
return HttpResponse(
json.dumps({
'success': False,
'error': STAFF_ERROR_MESSAGE
}))
def get_problem_list(request, course_id):
"""
Get all the problems for the given course id
Returns a json dict with the following keys:
success: bool
problem_list: a list containing json dicts with the following keys:
each dict represents a different problem in the course
location: the location of the problem
problem_name: the name of the problem
num_graded: the number of responses that have been graded
num_pending: the number of responses that are sitting in the queue
min_for_ml: the number of responses that need to be graded before
the ml can be run
"""
_check_access(request.user, course_id)
try:
response = staff_grading_service().get_problem_list(course_id, unique_id_for_user(request.user))
response = json.loads(response)
problem_list = response['problem_list']
valid_problem_list = []
for i in xrange(0,len(problem_list)):
#Needed to ensure that the 'location' key can be accessed
try:
problem_list[i] = json.loads(problem_list[i])
except Exception:
pass
if does_location_exist(course_id, problem_list[i]['location']):
valid_problem_list.append(problem_list[i])
response['problem_list'] = valid_problem_list
response = json.dumps(response)
return HttpResponse(response,
mimetype="application/json")
except GradingServiceError:
#This is a dev_facing_error
log.exception(
"Error from staff grading service in open "
"ended grading. server url: {0}".format(staff_grading_service().url)
)
#This is a staff_facing_error
return HttpResponse(json.dumps({'success': False,
'error': STAFF_ERROR_MESSAGE}))
def get_problem_list(request, course_id):
"""
Get all the problems for the given course id
Returns a json dict with the following keys:
success: bool
problem_list: a list containing json dicts with the following keys:
each dict represents a different problem in the course
location: the location of the problem
problem_name: the name of the problem
num_graded: the number of responses that have been graded
num_pending: the number of responses that are sitting in the queue
min_for_ml: the number of responses that need to be graded before
the ml can be run
'error': if success is False, will have an error message with more info.
"""
_check_access(request.user, course_id)
try:
response = staff_grading_service().get_problem_list(course_id, unique_id_for_user(request.user))
response = json.loads(response)
# If 'problem_list' is in the response, then we got a list of problems from the ORA server.
# If it is not, then ORA could not find any problems.
if 'problem_list' in response:
problem_list = response['problem_list']
else:
problem_list = []
# Make an error messages to reflect that we could not find anything to grade.
response['error'] = ("Cannot find any open response problems in this course. "
"Have you submitted answers to any open response assessment questions? "
"If not, please do so and return to this page.")
valid_problem_list = []
for i in xrange(0,len(problem_list)):
# Needed to ensure that the 'location' key can be accessed.
try:
problem_list[i] = json.loads(problem_list[i])
except Exception:
pass
if does_location_exist(course_id, problem_list[i]['location']):
valid_problem_list.append(problem_list[i])
response['problem_list'] = valid_problem_list
response = json.dumps(response)
return HttpResponse(response,
mimetype="application/json")
except GradingServiceError:
#This is a dev_facing_error
log.exception(
"Error from staff grading service in open "
"ended grading. server url: {0}".format(staff_grading_service().url)
)
#This is a staff_facing_error
return HttpResponse(json.dumps({'success': False,
'error': STAFF_ERROR_MESSAGE}))
def get_problem_list(request, course_id):
"""
Get all the problems for the given course id
Returns a json dict with the following keys:
success: bool
problem_list: a list containing json dicts with the following keys:
each dict represents a different problem in the course
location: the location of the problem
problem_name: the name of the problem
num_graded: the number of responses that have been graded
num_pending: the number of responses that are sitting in the queue
min_for_ml: the number of responses that need to be graded before
the ml can be run
'error': if success is False, will have an error message with more info.
"""
_check_access(request.user, course_id)
try:
response = staff_grading_service().get_problem_list(
course_id, unique_id_for_user(request.user))
response = json.loads(response)
# If 'problem_list' is in the response, then we got a list of problems from the ORA server.
# If it is not, then ORA could not find any problems.
if 'problem_list' in response:
problem_list = response['problem_list']
else:
problem_list = []
# Make an error messages to reflect that we could not find anything to grade.
response['error'] = (
"Cannot find any open response problems in this course. "
"Have you submitted answers to any open response assessment questions? "
"If not, please do so and return to this page.")
valid_problem_list = []
for i in xrange(0, len(problem_list)):
# Needed to ensure that the 'location' key can be accessed.
try:
problem_list[i] = json.loads(problem_list[i])
except Exception:
pass
if does_location_exist(course_id, problem_list[i]['location']):
valid_problem_list.append(problem_list[i])
response['problem_list'] = valid_problem_list
response = json.dumps(response)
return HttpResponse(response, mimetype="application/json")
except GradingServiceError:
#This is a dev_facing_error
log.exception("Error from staff grading service in open "
"ended grading. server url: {0}".format(
staff_grading_service().url))
#This is a staff_facing_error
return HttpResponse(
json.dumps({
'success': False,
'error': STAFF_ERROR_MESSAGE
}))
