def ws_message(message):
print "Message recieved from client side and the content is ", message.content['text']
# prefix, label = message['path'].strip('/').split('/')
socketid = message.content['text']
Group(socketid).add(message.reply_channel)
log_to_terminal(socketid, {"info": "User added to the Channel Group"})
def callback(ch, method, properties, body):
try:
print(" [x] Received %r" % body)
body = yaml.safe_load(body) # using yaml instead of json.loads since that unicodes the string in value
result = ClassificationTorchModel.predict(body['image_path'], body['label'], body['output_dir'])
ClassificationJob.objects.create(job_id=body['socketid'], input_label=body['label'], image=str(result['input_image']).replace(settings.BASE_DIR, '')[1:], predicted_label = result['pred_label'], gcam_image=str(result['classify_gcam']).replace(settings.BASE_DIR, '')[1:])
# Close the database connection in order to make sure that MYSQL Timeout doesn't occur
django.db.close_old_connections()
result['input_image'] = str(result['input_image']).replace(settings.BASE_DIR, '')
result['classify_gcam'] = str(result['classify_gcam']).replace(settings.BASE_DIR, '')
result['classify_gcam_raw'] = str(result['classify_gcam_raw']).replace(settings.BASE_DIR, '')
result['classify_gb'] = str(result['classify_gb']).replace(settings.BASE_DIR, '')
result['classify_gb_gcam'] = str(result['classify_gb_gcam']).replace(settings.BASE_DIR, '')
print result
log_to_terminal("Hello", {"terminal": "Completed the Classification Task"})
log_to_terminal(body['socketid'], {"terminal": json.dumps(result)})
log_to_terminal(body['socketid'], {"result": json.dumps(result)})
log_to_terminal(body['socketid'], {"terminal": "Completed the Classification Task"})
ch.basic_ack(delivery_tag = method.delivery_tag)
except Exception, err:
log_to_terminal(body['socketid'], {"terminal": json.dumps({"Traceback": str(traceback.print_exc())})})
def ws_message(message):
print("Message recieved from client side and the content is ",
message.content['text'])
# prefix, label = message['path'].strip('/').split('/')
socketid = message.content['text']
Group(socketid).add(message.reply_channel)
log_to_terminal(socketid, {"info": "User added to the Channel Group"})
def fever(request, template_name="fever/fever.html"):
socketid = uuid.uuid4()
if request.method == "POST":
try:
# from IPython import embed; embed(); import os; os._exit(1)
socketid = request.POST.get('socketid')
claim_text = request.POST.get('src')
log_to_terminal(socketid, {"terminal": "Starting FEVER job..."})
response = grad_cam_fever(str(claim_text), socketid)
except Exception, err:
log_to_terminal(socketid, {"terminal": traceback.print_exc()})
def captioning_api(request):
if request.method == "POST":
try:
image = request.FILES['image']
caption = request.POST.get('caption', '')
abs_image_path = os.path.join(settings.BASE_DIR, str(img_path[1:]))
out_dir = os.path.dirname(abs_image_path)
# Run the captioning wrapper
log_to_terminal(socketid, {"terminal": "Starting Captioning job..."})
response = grad_cam_captioning(str(abs_image_path), str(caption), str(out_dir+"/"), socketid)
except Exception, err:
log_to_terminal(socketid, {"terminal": traceback.print_exc()})
def callback(ch, method, properties, body):
try:
print(" [x] Received %r" % body)
body = yaml.safe_load(body) # using yaml instead of json.loads since that unicodes the string in value
result = VqaTorchModel.predict(body['image_path'], constants.VQA_CONFIG['input_sz'], constants.VQA_CONFIG['input_sz'], body['input_question'], body['input_answer'], body['output_dir'])
VqaJob.objects.create(job_id=body['socketid'], question=body['input_question'], input_answer=body['input_answer'], image=str(result['input_image']).replace(settings.BASE_DIR, '')[1:], predicted_answer = result['answer'], gcam_image=str(result['vqa_gcam']).replace(settings.BASE_DIR, '')[1:])
# Close the database connection in order to make sure that MYSQL Timeout doesn't occur
django.db.close_old_connections()
result['input_image'] = str(result['input_image']).replace(settings.BASE_DIR, '')
result['vqa_gcam'] = str(result['vqa_gcam']).replace(settings.BASE_DIR, '')
result['vqa_gcam_raw'] = str(result['vqa_gcam_raw']).replace(settings.BASE_DIR, '')
result['vqa_gb'] = str(result['vqa_gb']).replace(settings.BASE_DIR, '')
result['vqa_gb_gcam'] = str(result['vqa_gb_gcam']).replace(settings.BASE_DIR, '')
log_to_terminal(body['socketid'], {"terminal": json.dumps(result)})
log_to_terminal(body['socketid'], {"result": json.dumps(result)})
log_to_terminal(body['socketid'], {"terminal": "Completed the Grad-CAM VQA task"})
ch.basic_ack(delivery_tag = method.delivery_tag)
except Exception, err:
log_to_terminal(body['socketid'], {"terminal": json.dumps({"Traceback": str(traceback.print_exc())})})
def callback(ch, method, properties, body):
try:
print(" [x] Received %r" % body)
body = yaml.safe_load(
body
) # using yaml instead of json.loads since that unicodes the string in value
result = ClassificationTorchModel.predict(body['image_path'],
body['label'],
body['output_dir'])
ClassificationJob.objects.create(
job_id=body['socketid'],
input_label=body['label'],
image=str(result['input_image']).replace(settings.BASE_DIR,
'')[1:],
predicted_label=result['pred_label'],
gcam_image=str(result['classify_gcam']).replace(
settings.BASE_DIR, '')[1:])
# Close the database connection in order to make sure that MYSQL Timeout doesn't occur
django.db.close_old_connections()
result['input_image'] = str(result['input_image']).replace(
settings.BASE_DIR, '')
result['classify_gcam'] = str(result['classify_gcam']).replace(
settings.BASE_DIR, '')
result['classify_gcam_raw'] = str(result['classify_gcam_raw']).replace(
settings.BASE_DIR, '')
result['classify_gb'] = str(result['classify_gb']).replace(
settings.BASE_DIR, '')
result['classify_gb_gcam'] = str(result['classify_gb_gcam']).replace(
settings.BASE_DIR, '')
print result
log_to_terminal("Hello",
{"terminal": "Completed the Classification Task"})
log_to_terminal(body['socketid'], {"terminal": json.dumps(result)})
log_to_terminal(body['socketid'], {"result": json.dumps(result)})
log_to_terminal(body['socketid'],
{"terminal": "Completed the Classification Task"})
ch.basic_ack(delivery_tag=method.delivery_tag)
except Exception, err:
log_to_terminal(body['socketid'], {
"terminal":
json.dumps({"Traceback": str(traceback.print_exc())})
})
def captioning_api(request):
if request.method == "POST":
try:
image = request.FILES['image']
caption = request.POST.get('caption', '')
abs_image_path = os.path.join(settings.BASE_DIR, str(img_path[1:]))
out_dir = os.path.dirname(abs_image_path)
# Run the captioning wrapper
log_to_terminal(socketid,
{"terminal": "Starting Captioning job..."})
response = grad_cam_captioning(str(abs_image_path), str(caption),
str(out_dir + "/"), socketid)
except Exception, err:
log_to_terminal(socketid, {"terminal": traceback.print_exc()})
def captioning(request, template_name="captioning/captioning.html"):
socketid = uuid.uuid4()
if request.method == "POST":
try:
img_path = request.POST.get('img_path')
img_path = urllib2.unquote(img_path)
caption = request.POST.get('caption', '')
socketid = request.POST.get('socketid')
abs_image_path = os.path.join(settings.BASE_DIR, str(img_path[1:]))
out_dir = os.path.dirname(abs_image_path)
# Run the captioning wrapper
log_to_terminal(socketid, {"terminal": "Starting Captioning job..."})
response = grad_cam_captioning(str(abs_image_path), str(caption), str(out_dir+"/"), socketid)
except Exception, err:
log_to_terminal(socketid, {"terminal": traceback.print_exc()})
def classification(request, template_name="classification/classification.html"):
socketid = uuid.uuid4()
if request.method == "POST":
try:
img_path = request.POST.get('img_path')
img_path = urllib2.unquote(img_path)
label = request.POST.get('label')
socketid = request.POST.get('socketid')
abs_image_path = os.path.join(settings.BASE_DIR, str(img_path[1:]))
out_dir = os.path.dirname(abs_image_path)
# Run the classification wrapper
log_to_terminal(socketid, {"terminal": "Starting classification job on VGG_ILSVRC_16_layers.caffemodel"})
response = grad_cam_classification(str(abs_image_path), int(label), str(out_dir+"/"), socketid)
except Exception, err:
log_to_terminal(socketid, {"terminal": traceback.print_exc()})
def callback(ch, method, properties, body):
try:
print(" [x] Received %r" % body)
body = yaml.safe_load(
body
) # using yaml instead of json.loads since that unicodes the string in value
log_to_terminal(body['socketid'],
{"terminal": "Running... Please wait."})
result = fever_predictor.predict(body['claim_text'])
# from IPython import embed; embed(); import os; os._exit(1)
FEVERJob.objects.create(
job_id=body['socketid'],
claim_text=body['claim_text'],
docids=str(result['docids']),
predicted_evidence=str(
result['predicted_evidence'].encode('utf-8')),
predicted_label=result['predicted_label'])
# Close the database connection in order to make sure that MYSQL Timeout doesn't occur
django.db.close_old_connections()
result['predicted_evidence'] = result['predicted_evidence'].split(
'@_@')
result['docids'] = result['docids'].split('@_@')
# assert len(result['predicted_evidence']) == len(result['docids'])
result['docids'] = [
'https://en.wikipedia.org/wiki/{}'.format(d)
for d in result['docids']
]
log_to_terminal(body['socketid'], {"result": json.dumps(result)})
log_to_terminal(body['socketid'], {"terminal": json.dumps(result)})
log_to_terminal(body['socketid'],
{"terminal": "Completed the FEVER job"})
ch.basic_ack(delivery_tag=method.delivery_tag)
except Exception, err:
log_to_terminal(body['socketid'], {
"terminal":
json.dumps({"Traceback": str(traceback.print_exc())})
})
def captioning(request, template_name="captioning/captioning.html"):
socketid = uuid.uuid4()
if request.method == "POST":
try:
img_path = request.POST.get('img_path')
img_path = urllib2.unquote(img_path)
caption = request.POST.get('caption', '')
socketid = request.POST.get('socketid')
abs_image_path = os.path.join(settings.BASE_DIR, str(img_path[1:]))
out_dir = os.path.dirname(abs_image_path)
# Run the captioning wrapper
log_to_terminal(socketid,
{"terminal": "Starting Captioning job..."})
response = grad_cam_captioning(str(abs_image_path), str(caption),
str(out_dir + "/"), socketid)
except Exception, err:
log_to_terminal(socketid, {"terminal": traceback.print_exc()})
def vqa(request, template_name="vqa/vqa.html"):
socketid = uuid.uuid4()
if request.method == "POST":
# get the parameters from client side
try:
socketid = request.POST.get('socketid')
input_question = request.POST.get('question', '')
input_answer = request.POST.get('answer', None)
img_path = request.POST.get('img_path')
img_path = urllib2.unquote(img_path)
abs_image_path = settings.BASE_DIR + str(img_path)
# abs_image_path = os.path.join(settings.BASE_DIR, str(img_path[1:]))
out_dir = os.path.dirname(abs_image_path)
# Run the VQA wrapper
log_to_terminal(socketid, {"terminal": "Starting Visual Question Answering job..."})
response = grad_cam_vqa(str(input_question), str(input_answer), str(abs_image_path), str(out_dir+"/"), socketid)
except Exception, err:
log_to_terminal(socketid, {"terminal": traceback.print_exc()})
def grad_cam_fever(claim_text, socketid):
connection = pika.BlockingConnection(pika.ConnectionParameters(
host='localhost'))
channel = connection.channel()
channel.queue_declare('fever_task_queue', durable=True)
message = {
'claim_text': claim_text,
'socketid': socketid,
}
# from IPython import embed; embed(); import os; os._exit(1)
log_to_terminal(socketid, {"terminal": "Publishing job to FEVER Queue"})
channel.basic_publish(exchange='',
routing_key='fever_task_queue',
body=json.dumps(message),
properties=pika.BasicProperties(
delivery_mode=2, # make message persistent
))
print(" [x] Sent %r" % message)
log_to_terminal(socketid, {"terminal": "Job published successfully"})
connection.close()
def callback(ch, method, properties, body):
try:
print(" [x] Received %r" % body)
body = yaml.safe_load(
body
) # using yaml instead of json.loads since that unicodes the string in value
result = VqaTorchModel.predict(body['image_path'],
constants.VQA_CONFIG['input_sz'],
constants.VQA_CONFIG['input_sz'],
body['input_question'],
body['input_answer'],
body['output_dir'])
VqaJob.objects.create(
job_id=body['socketid'],
question=body['input_question'],
input_answer=body['input_answer'],
image=str(result['input_image']).replace(settings.BASE_DIR,
'')[1:],
predicted_answer=result['answer'],
gcam_image=str(result['vqa_gcam']).replace(settings.BASE_DIR,
'')[1:])
# Close the database connection in order to make sure that MYSQL Timeout doesn't occur
django.db.close_old_connections()
result['input_image'] = str(result['input_image']).replace(
settings.BASE_DIR, '')
result['vqa_gcam'] = str(result['vqa_gcam']).replace(
settings.BASE_DIR, '')
result['vqa_gcam_raw'] = str(result['vqa_gcam_raw']).replace(
settings.BASE_DIR, '')
result['vqa_gb'] = str(result['vqa_gb']).replace(settings.BASE_DIR, '')
result['vqa_gb_gcam'] = str(result['vqa_gb_gcam']).replace(
settings.BASE_DIR, '')
log_to_terminal(body['socketid'], {"terminal": json.dumps(result)})
log_to_terminal(body['socketid'], {"result": json.dumps(result)})
log_to_terminal(body['socketid'],
{"terminal": "Completed the Grad-CAM VQA task"})
ch.basic_ack(delivery_tag=method.delivery_tag)
except Exception, err:
log_to_terminal(body['socketid'], {
"terminal":
json.dumps({"Traceback": str(traceback.print_exc())})
})
def classification(request,
template_name="classification/classification.html"):
socketid = uuid.uuid4()
if request.method == "POST":
try:
img_path = request.POST.get('img_path')
img_path = urllib2.unquote(img_path)
label = request.POST.get('label')
socketid = request.POST.get('socketid')
abs_image_path = os.path.join(settings.BASE_DIR, str(img_path[1:]))
out_dir = os.path.dirname(abs_image_path)
# Run the classification wrapper
log_to_terminal(
socketid, {
"terminal":
"Starting classification job on VGG_ILSVRC_16_layers.caffemodel"
})
response = grad_cam_classification(str(abs_image_path), int(label),
str(out_dir + "/"), socketid)
except Exception, err:
log_to_terminal(socketid, {"terminal": traceback.print_exc()})
def vqa(request, template_name="vqa/vqa.html"):
socketid = uuid.uuid4()
if request.method == "POST":
# get the parameters from client side
try:
socketid = request.POST.get('socketid')
input_question = request.POST.get('question', '')
input_answer = request.POST.get('answer', None)
img_path = request.POST.get('img_path')
img_path = urllib2.unquote(img_path)
abs_image_path = settings.BASE_DIR + str(img_path)
# abs_image_path = os.path.join(settings.BASE_DIR, str(img_path[1:]))
out_dir = os.path.dirname(abs_image_path)
# Run the VQA wrapper
log_to_terminal(
socketid,
{"terminal": "Starting Visual Question Answering job..."})
response = grad_cam_vqa(str(input_question), str(input_answer),
str(abs_image_path), str(out_dir + "/"),
socketid)
except Exception, err:
log_to_terminal(socketid, {"terminal": traceback.print_exc()})
def grad_cam_captioning(image_path, caption, out_dir, socketid):
connection = pika.BlockingConnection(pika.ConnectionParameters(
host='localhost'))
channel = connection.channel()
channel.queue_declare(queue='captioning_task_queue', durable=True)
message = {
'image_path': image_path,
'caption': caption,
'output_dir': out_dir,
'socketid': socketid,
}
log_to_terminal(socketid, {"terminal": "Publishing job to Captioning Queue"})
channel.basic_publish(exchange='',
routing_key='captioning_task_queue',
body=json.dumps(message),
properties=pika.BasicProperties(
delivery_mode = 2, # make message persistent
))
print(" [x] Sent %r" % message)
log_to_terminal(socketid, {"terminal": "Job published successfully"})
connection.close()
def callback(ch, method, properties, body):
try:
print(" [x] Received %r" % body)
body = yaml.safe_load(
body
) # using yaml instead of json.loads since that unicodes the string in value
result = ClassificationTorchModel.predict(body['image_path'],
body['label'],
body['output_dir'])
result['input_image'] = str(result['input_image']).replace(
settings.BASE_DIR, '')
result['classify_gcam'] = str(result['classify_gcam']).replace(
settings.BASE_DIR, '')
result['classify_gcam_raw'] = str(result['classify_gcam_raw']).replace(
settings.BASE_DIR, '')
result['classify_gb'] = str(result['classify_gb']).replace(
settings.BASE_DIR, '')
result['classify_gb_gcam'] = str(result['classify_gb_gcam']).replace(
settings.BASE_DIR, '')
print result
log_to_terminal("Hello",
{"terminal": "Completed the Classification Task"})
log_to_terminal(body['socketid'], {"terminal": json.dumps(result)})
log_to_terminal(body['socketid'], {"result": json.dumps(result)})
log_to_terminal(body['socketid'],
{"terminal": "Completed the Classification Task"})
ch.basic_ack(delivery_tag=method.delivery_tag)
except Exception, err:
log_to_terminal(body['socketid'], {
"terminal":
json.dumps({"Traceback": str(traceback.print_exc())})
})
def callback(ch, method, properties, body):
try:
print(" [x] Received %r" % body)
body = yaml.safe_load(body) # using yaml instead of json.loads since that unicodes the string in value
result = VqaTorchModel.predict(body['image_path'], constants.VQA_CONFIG['input_sz'], constants.VQA_CONFIG['input_sz'], body['input_question'], body['input_answer'], body['output_dir'])
result['input_image'] = str(result['input_image']).replace(settings.BASE_DIR, '')
result['vqa_gcam'] = str(result['vqa_gcam']).replace(settings.BASE_DIR, '')
result['vqa_gcam_raw'] = str(result['vqa_gcam_raw']).replace(settings.BASE_DIR, '')
result['vqa_gb'] = str(result['vqa_gb']).replace(settings.BASE_DIR, '')
result['vqa_gb_gcam'] = str(result['vqa_gb_gcam']).replace(settings.BASE_DIR, '')
log_to_terminal(body['socketid'], {"terminal": json.dumps(result)})
log_to_terminal(body['socketid'], {"result": json.dumps(result)})
log_to_terminal(body['socketid'], {"terminal": "Completed the Grad-CAM VQA task"})
ch.basic_ack(delivery_tag = method.delivery_tag)
except Exception, err:
log_to_terminal(body['socketid'], {"terminal": json.dumps({"Traceback": str(traceback.print_exc())})})
