def register_device():
patient_id = request.json['patient_id']
spimo_id = request.json['spimo_id']
pedal_id = request.json['pedal_id']
date_time = datetime.datetime.now()
connection = get_connection()
table_name = 'dp_pair'
data = {'device_id': spimo_id}
row = read_data(table_name, data)
#scenario 2: No such device_id
if row is None:
close_connection(connection)
return str(2)
#scenario 1: Successful
elif row.patient_id is None:
data = {'patient_id': patient_id, 'updated_date': date_time}
condition = {'device_id': spimo_id}
update_data(table_name, data, condition)
condition = {'device_id': pedal_id}
update_data(table_name, data, condition)
close_connection(connection)
return str(1)
#scenario 2: duplicated id
else:
close_connection(connection)
return str(0)
def device_start():
patient_id = request.json['patient_id']
device_id = request.json['device_id']
connection = get_connection()
date_time = datetime.datetime.now()
data = {'start_time': date_time}
table_name = 'dp_pair'
condition = {'device_id': device_id, 'patient_id': patient_id}
update_data(table_name, data, condition)
close_connection(connection)
return str(1)
def handle_audio():
api_result = json.loads(request.form.get('data'))
seq = api_result["seq"]
device_id = api_result["device_id"]
session_id = api_result["session_id"]
audiofile = request.files['audio']
filename = werkzeug.utils.secure_filename(audiofile.filename)
audiofile.save(filename)
if filename is not None:
signal, rate = sf.read(filename)
wav_length = len(signal) / rate #second
window_length = 1
# for index in range(sample_per_wav): # take ten windows in an audio file
window_seed = random.uniform(0, abs(wav_length - window_length))
window_range = (window_seed, window_seed + window_length)
window_signal = signal[int(window_range[0] *
rate):int(window_range[1] * rate)]
mfcc_feat = mfcc(window_signal)
mfcc_feat_scale = mfcc_feat / np.linalg.norm(mfcc_feat)
mfcc_feat_scale = mfcc_feat_scale[np.newaxis, :]
print(mfcc_feat_scale.shape)
# result_array=(mfcc_feat-np.min(mfcc_feat))/np.ptp(mfcc_feat)
model = tensorflow.keras.models.load_model('./model')
predictions = model.predict(mfcc_feat_scale)
print(predictions)
into_sad_prob = predictions[0][0]
into_angry_prob = predictions[0][1]
into_happy_prob = predictions[0][2]
into_neutral_prob = predictions[0][3]
table_name = 'transaction_table'
data = {
'session_id': session_id,
'seq': seq,
'into_sad_prob': str(into_sad_prob),
'into_angry_prob': str(into_angry_prob),
'into_happy_prob': str(into_happy_prob),
'into_neutral_prob': str(into_neutral_prob)
}
r_data = {'session_id': session_id, 'seq': seq}
connection = get_connection()
row, number_rows = read_data(table_name, r_data, connection)
if number_rows == 0:
connection = insert_data(table_name, data, connection)
else:
condition = {'session_id': session_id, 'seq': seq}
connection = update_data(table_name, data, condition, connection)
return str(1)
else:
return str(0)
def paddle_count():
patient_id = request.json['patient_id']
device_id = request.json['device_id']
connection = get_connection()
date_time = datetime.datetime.now()
data = {
'patient_id': patient_id,
'device_id': device_id,
'medium_count': 1,
'time': date_time
}
table_name = 'pedal'
create_data(table_name, data, connection)
close_connection(connection)
return str(1)
def device_refresh():
patient_id = request.args.get('patient_id')
print(patient_id)
device_id = request.args.get('device_id')
print(device_id)
connection = get_connection()
table_name = 'dp_pair'
data = {'device_id': device_id, 'patient_id': patient_id}
row = read_data(table_name, data)
start_time = row.start_time
current_time = datetime.datetime.now()
if (device_id[0:3] == 'SPI'):
table_name = 'spimo'
elif (device_id[0:3] == 'PED'):
table_name = 'pedal'
row = read_count(table_name, start_time, current_time, device_id)
result = row.total_count
close_connection(connection)
return str(result)
def handle_senti_score_request():
session_id = request.json['session_id']
seq = request.json['seq']
text_senti_score = request.json['sentiment_score']
again = request.json['again']
# upadte if record exists
table_name = 'transaction_table'
data = {
'session_id': session_id,
'seq': seq,
'text_senti_score': text_senti_score
}
connection = get_connection()
if again == '1':
condition = {'session_id': session_id, 'seq': seq}
connection = update_data(table_name, data, condition, connection)
elif again == '0':
connection = insert_data(table_name, data, connection)
close_connection(connection)
return str(1)
def handle_ml():
session_id = request.json['session_id']
seq = request.json['seq']
table_name = 'transaction_table'
connection = get_connection()
text_senti_avg, text_senti_std, text_senti_min, text_senti_max = calculate_features(
table_name, session_id, seq, connection)
condition = {'session_id': session_id, 'seq': seq}
features_data = {
'text_senti_avg': text_senti_avg,
'text_senti_std': text_senti_std,
'text_senti_min': text_senti_min,
'text_senti_max': text_senti_max
}
update_data(table_name, features_data, condition, connection)
r_data = {'session_id': session_id, 'seq': seq}
rows, number_rows = read_data(table_name, r_data, connection)
emotion_label = send_request_to_ml(rows)
features_data = {'result': emotion_label}
update_data(table_name, features_data, condition, connection)
close_connection(connection)
return emotion_label
def return_device():
patient_id = request.json['patient_id']
spimo_id = request.json['spimo_id']
pedal_id = request.json['pedal_id']
date_time = datetime.datetime.now()
connection = get_connection()
table_name = 'dp_pair'
data = {'device_id': spimo_id}
row = read_data(table_name, data)
# device has been returned
if row.patient_id is None:
close_connection(connection)
return str(0)
else:
data = {'patient_id': None, 'updated_date': None}
condition = {'device_id': spimo_id}
update_data(table_name, data, condition)
condition = {'device_id': pedal_id}
update_data(table_name, data, condition)
close_connection(connection)
return str(1)
def handle_request():
api_result = json.loads(request.form.get('data'))
seq = api_result["seq"]
device_id = api_result["device_id"]
session_id = api_result["session_id"]
imagefile = request.files['image']
filename = werkzeug.utils.secure_filename(imagefile.filename)
imagefile.save(filename)
with open(filename, 'rb') as f:
data = f.read()
_url = 'https://westus2.api.cognitive.microsoft.com/face/v1.0/detect'
_key = 'bc027dc227484433a77d7b613807d230' #Here you have to paste your primary key
headers = dict()
headers['Ocp-Apim-Subscription-Key'] = _key
headers['Content-Type'] = 'application/octet-stream'
json_ = None
params = {
'returnFaceId':
'true',
'returnFaceLandmarks':
'false',
'returnFaceAttributes':
'age,gender,headPose,smile,facialHair,glasses,emotion,hair,makeup,occlusion,accessories,blur,exposure,noise',
}
result = processRequest(json_, data, headers, params, _url)
connection = get_connection()
if result == []:
date_time = datetime.datetime.now()
data = {
'date': date_time,
'session_id': session_id,
'seq': seq,
'device_id': device_id,
'face_smile': 0,
'face_anger': 0,
'face_contempt': 0,
'face_disgust': 0,
'face_fear': 0,
'face_happiness': 0,
'face_neutral': 0,
'face_sadness': 0,
'face_surprise': 0
}
table_name = 'transaction_table'
condition = {'session_id': session_id, 'seq': seq}
connection = update_data(table_name, data, condition, connection)
close_connection(connection)
return str(4)
elif result is None:
date_time = datetime.datetime.now()
data = {
'date': date_time,
'session_id': session_id,
'seq': seq,
'device_id': device_id,
'face_smile': 0,
'face_anger': 0,
'face_contempt': 0,
'face_disgust': 0,
'face_fear': 0,
'face_happiness': 0,
'face_neutral': 0,
'face_sadness': 0,
'face_surprise': 0
}
table_name = 'transaction_table'
condition = {'session_id': session_id, 'seq': seq}
connection = update_data(table_name, data, condition, connection)
close_connection(connection)
return str(5)
elif result[0]['faceAttributes'] is None:
date_time = datetime.datetime.now()
data = {
'date': date_time,
'session_id': session_id,
'seq': seq,
'device_id': device_id,
'face_smile': 0,
'face_anger': 0,
'face_contempt': 0,
'face_disgust': 0,
'face_fear': 0,
'face_happiness': 0,
'face_neutral': 0,
'face_sadness': 0,
'face_surprise': 0
}
table_name = 'transaction_table'
condition = {'session_id': session_id, 'seq': seq}
connection = update_data(table_name, data, condition, connection)
close_connection(connection)
return str(6)
firstface_dic = result[0]
faceAttributes_dic = firstface_dic['faceAttributes']
smile = faceAttributes_dic['smile']
#gender = faceAttributes_dic['gender']
anger = faceAttributes_dic['emotion']['anger']
contempt = faceAttributes_dic['emotion']['contempt']
disgust = faceAttributes_dic['emotion']['disgust']
fear = faceAttributes_dic['emotion']['fear']
happiness = faceAttributes_dic['emotion']['happiness']
neutral = faceAttributes_dic['emotion']['neutral']
sadness = faceAttributes_dic['emotion']['sadness']
surprise = faceAttributes_dic['emotion']['surprise']
connection = get_connection()
date_time = datetime.datetime.now()
data = {
'date': date_time,
'session_id': session_id,
'seq': seq,
'device_id': device_id,
'face_smile': smile,
'face_anger': anger,
'face_contempt': contempt,
'face_disgust': disgust,
'face_fear': fear,
'face_happiness': happiness,
'face_neutral': neutral,
'face_sadness': sadness,
'face_surprise': surprise
}
table_name = 'transaction_table'
condition = {'session_id': session_id, 'seq': seq}
connection = update_data(table_name, data, condition, connection)
close_connection(connection)
return str(1)
# upload file "CSV/intergrated_accurate.wav.csv" to database
from CRUD_m import create_data
from CRUD_m import read_data
from CRUD_m import close_connection
from CRUD_m import get_connection
import csv
if __name__ == '__main__':
file_name = 'CSV/intergrated_accurate.wav.csv'
csvFile = open(file_name, "r")
reader = csv.reader(csvFile)
connection = get_connection()
table_name = 'speech_sentiment'
for row in reader:
# print(row)
sentence = ' '
if len(row) == 8:
sentence = row[7]
datum = {
'time': int(row[0]),
'video_id': row[1],
'score_val': float(row[2]),
'score_max': float(row[3]),
'score_min': float(row[4]),
'score_avg': float(row[5]),
def handle_request():
section_id = request.args.get('section_id')
seq = request.args.get('seq')
device_id = request.args.get('device_id')
imagefile = request.files['image']
filename = werkzeug.utils.secure_filename(imagefile.filename)
imagefile.save(filename)
with open(filename, 'rb') as f:
data = f.read()
_url = 'https://westus2.api.cognitive.microsoft.com/face/v1.0/detect'
_key = 'bc027dc227484433a77d7b613807d230' #Here you have to paste your primary key
headers = dict()
headers['Ocp-Apim-Subscription-Key'] = _key
headers['Content-Type'] = 'application/octet-stream'
json = None
params = {
'returnFaceId':
'true',
'returnFaceLandmarks':
'false',
'returnFaceAttributes':
'age,gender,headPose,smile,facialHair,glasses,emotion,hair,makeup,occlusion,accessories,blur,exposure,noise',
}
result = processRequest(json, data, headers, params, _url)
if result == []:
return 'no face'
elif result is None:
return 'no picture'
firstface_dic = result[0]
#print(result[0])
faceAttributes_dic = firstface_dic['faceAttributes']
#print(faceAttributes_dic)
#interval = math.ceil(sec/10)
smile = faceAttributes_dic['smile']
gender = faceAttributes_dic['gender']
#age = faceAttributes_dic['age']
anger = faceAttributes_dic['emotion']['anger']
contempt = faceAttributes_dic['emotion']['contempt']
disgust = faceAttributes_dic['emotion']['disgust']
fear = faceAttributes_dic['emotion']['fear']
happiness = faceAttributes_dic['emotion']['happiness']
neutral = faceAttributes_dic['emotion']['neutral']
sadness = faceAttributes_dic['emotion']['sadness']
surprise = faceAttributes_dic['emotion']['surprise']
connection = get_connection()
date_time = datetime.datetime.now()
data = {
'date': date,
'session_id': section_id,
'seq': section_id,
'device_id': device_id,
'face_smile': smile,
'face_anger': anger,
'face_contempt': contempt,
'face_disgust': disgust,
'face_fear': fear,
'face_happiness': happiness,
'face_neutral': neutral,
'face_sadness': sadness,
'face_surprise': surprise
}
#data = {'face_smile_prob':smile}
table_name = 'test'
create_data(table_name, data, connection)
close_connection(connection)
