def forgot_password():
global telegram_chat_id
new_password = ''
letters = string.ascii_letters
for i in range(12):
character = random.choice(letters)
new_password = new_password + character
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
table = dynamodb.Table('credentials')
response = table.update_item(
Key = {
'username': '******'
},
UpdateExpression = "set password = :p, is_temp = :t",
ExpressionAttributeValues = {
':p': new_password,
':t': 'true'
},
ReturnValues = "UPDATED_NEW"
)
message = {}
message["default"] = 'Your new password is ' + new_password + '.'
client.publish('password/reset', json.dumps(message), 1)
bot.sendMessage(telegram_chat_id, 'We\'ve just received a password reset request! Go check your email for the temporary password. Once logged in, it is best you change it immediately.')
return render_template("forgot-password.html")
def change_password_check():
if request.method == 'POST':
try:
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
cur_password = request.form["currentPassword"]
new_password = request.form["newPassword"]
new_password_confirm = request.form["newPasswordConfirm"]
table = dynamodb.Table('credentials')
response = table.query(KeyConditionExpression = Key('username').eq('WateringPi'),
ScanIndexForward = False
)
results = response["Items"]
stored_password = results[0]["password"]
if cur_password == stored_password and new_password == new_password_confirm:
response = table.update_item(
Key = {
'username': '******'
},
UpdateExpression = "set password = :p, is_temp = :t",
ExpressionAttributeValues = {
':p': new_password,
':t': 'false'
},
ReturnValues = "UPDATED_NEW"
)
global telegram_chat_id
bot.sendMessage(telegram_chat_id, 'Your WateringPi password was changed. If it wasn\'t you who changed the password, reset it immediately.')
response = make_response(redirect('/password_changed/'))
return response
else:
response = make_response(redirect('/change_password/'))
return response
except:
print(sys.exc_info()[0])
print(sys.exc_info()[1])
def update_user_prefs():
if request.method == 'POST':
try:
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
table = dynamodb.Table('user_preferences')
soil_interval = request.form['soilInterval']
environment_interval = request.form['environmentInterval']
photo_interval = request.form['photoInterval']
dry_soil_threshold = request.form['minimumMoisture']
watering_duration = request.form['wateringDuration']
response = table.update_item(
Key = {
'name': 'default'
},
UpdateExpression = "set soil_refresh_interval = :s, environment_refresh_interval = :e, photo_interval = :p, dry_soil_threshold = :t, watering_duration = :w",
ExpressionAttributeValues = {
':s': soil_interval,
':e': environment_interval,
':p': photo_interval,
':t': dry_soil_threshold,
':w': watering_duration
},
ReturnValues = "UPDATED_NEW"
)
response = make_response(redirect("/"))
return response
except:
print(sys.exc_info()[0])
print(sys.exc_info()[1])
def get_user_prefs():
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
table = dynamodb.Table('user_preferences')
deviceid = 'deviceid_dorachua'
response = table.query(KeyConditionExpression = Key('name').eq('default'),
ScanIndexForward = False
)
results = response["Items"]
return jsonify(results)
def lambda_handler(event, context):
logger.info("Event: " + str(event))
trigger = json.loads(event['Records'][0]['Sns']['Message'])['Trigger']
logger.info("Trigger: " + json.dumps(trigger, indent=4))
metricName = trigger['MetricName']
namespace = trigger['Namespace']
dimensions = trigger['Dimensions']
if namespace != cloudwatch.NAMESPACE:
raise Exception("Namespace is not match: " + namespace)
metric = cloudwatch.Metrics(dimensions, metricName)
provision = metric.calcProvision()
table = dynamodb.Table(metric.dimensions)
table.update(metric.name, provision)
for key in cloudwatch.BOUNDARIES.keys():
metric.alarm(key).update(provision)
def apidata_get_soil_data():
if request.method == 'POST' or request.method == 'GET':
try:
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
table = dynamodb.Table('soilmoisture')
startdate = '2019-08'
response = table.query(
KeyConditionExpression=Key('device_id').eq('raspberrypi-1')
& Key('datetime_id').begins_with(startdate),
ScanIndexForward=False
)
items = response['Items']
n=75 # limit to last 10 items
data = items[:n]
data_reversed = data[::-1]
response = data_reversed[0]
return jsonify(data_reversed)
except:
import sys
print(sys.exc_info()[0])
print(sys.exc_info()[1])
def login_check_credentials():
if request.method == 'POST':
try:
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
password = request.form["password"]
table = dynamodb.Table('credentials')
response = table.query(KeyConditionExpression = Key('username').eq('WateringPi'),
ScanIndexForward = False
)
results = response["Items"]
stored_password = results[0]["password"]
if password == stored_password:
response = make_response(redirect('/'))
response.set_cookie('logged_in', 'True')
return response
else:
global telegram_chat_id
bot.sendMessage(telegram_chat_id, 'Someone unsuccessfully tried to log in to your WateringPi. If you see this message a few more times and it wasn\'t you trying to login, it might be someone trying to gain unauthorised access to your WateringPi. Consider changing the password to make your WateringPi more secure.')
response = make_response(redirect('/login_page'))
return response
except:
print(sys.exc_info()[0])
print(sys.exc_info()[1])
def predictSafety(bid):
try:
# Obtain SQL Reading for selected BookingID (SQLQuery)
# https://www.kite.com/python/answers/how-to-convert-an-sql-query-result-to-a-pandas-dataframe-in-python
# https://stackoverflow.com/questions/12047193/how-to-convert-sql-query-result-to-pandas-data-structure
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
table = dynamodb.Table('modedata')
response = table.query(KeyConditionExpression=Key('bookingid').eq(bid))
datarows = response['Items']
df = pd.DataFrame(datarows, dtype=float)
#resoverall is yr sql statement
# df = DataFrame(resoverall.fetchall())
# df.columns = resoverall.keys()
df.drop(['accuracy'], axis=1, inplace=True)
df['acceleration'] = np.sqrt(
(df.loc[:, ('acceleration_x', 'acceleration_y',
'acceleration_z')]**2).sum(axis=1))
df.drop([
'acceleration_x', 'acceleration_y', 'acceleration_z', 'speedkmhour'
],
axis=1,
inplace=True)
pca_gyro = PCA(n_components=1).fit(
df.loc[:, ['gyro_x', 'gyro_y', 'gyro_z']])
df['gyro'] = pca_gyro.transform(df.loc[:,
('gyro_x', 'gyro_y', 'gyro_z')])
df.drop(['gyro_x', 'gyro_y', 'gyro_z'], axis=1, inplace=True)
data = pd.DataFrame()
for col in df.columns:
if col != "bookingid" and col != "label":
temp = df.groupby("bookingid")[col].agg(
["mean", "sum", "max", "min"])
data[col + "_mean"] = temp["mean"]
data[col + "_sum"] = temp["sum"]
data[col + "_max"] = temp["max"]
data[col + "_min"] = temp["min"]
#data = data.drop(columns=["bookingid"]).reset_index(drop=True)
data.drop(columns=["seconds_min"], inplace=True)
# generate distance, velocity and angle features
for col in data.columns:
if col.startswith("seconds"):
agg_method = col.split("_")[1]
data["distance_" +
agg_method] = data[col] * data["speed_" + agg_method]
data["velocity_" +
agg_method] = data[col] * data["acceleration_" +
agg_method]
data["angle_" +
agg_method] = data[col] * data["gyro_" + agg_method]
data = data.drop(columns=['seconds_sum', 'seconds_mean']).reset_index(
drop=True)
# Once Obtain model after hyper tuning save weights here and predict
from joblib import load
sr = load("GBR.joblib")
y_pred = sr.predict(data)
print("regression value: ", y_pred[0])
return jsonify(json.loads(jsonc.data_to_json(y_pred[0])))
except:
print(sys.exc_info()[0])
print(sys.exc_info()[1])