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import logging
import pwnagotchi.plugins as plugins
from pwnagotchi.ui.components import LabeledValue
from pwnagotchi.ui.view import BLACK
import pwnagotchi.ui.fonts as fonts
class Example(plugins.Plugin):
__author__ = 'evilsocket@gmail.com'
__version__ = '1.0.0'
__license__ = 'GPL3'
__description__ = 'An example plugin for pwnagotchi that implements all the available callbacks.'
def __init__(self):
logging.debug("example plugin created")
# called when http://<host>:<port>/plugins/<plugin>/ is called
# must return a html page
# IMPORTANT: If you use "POST"s, add a csrf-token (via csrf_token() and render_template_string)
def on_webhook(self, path, request):
pass
# called when the plugin is loaded
def on_loaded(self):
logging.warning("WARNING: this plugin should be disabled! options = " % self.options)
# called before the plugin is unloaded
def on_unload(self, ui):
pass
# called hen there's internet connectivity
def on_internet_available(self, agent):
pass
# called to setup the ui elements
def on_ui_setup(self, ui):
# add custom UI elements
ui.add_element('ups', LabeledValue(color=BLACK, label='UPS', value='0%/0V', position=(ui.width() / 2 - 25, 0),
label_font=fonts.Bold, text_font=fonts.Medium))
# called when the ui is updated
def on_ui_update(self, ui):
# update those elements
some_voltage = 0.1
some_capacity = 100.0
ui.set('ups', "%4.2fV/%2i%%" % (some_voltage, some_capacity))
# called when the hardware display setup is done, display is an hardware specific object
def on_display_setup(self, display):
pass
# called when everything is ready and the main loop is about to start
def on_ready(self, agent):
logging.info("unit is ready")
# you can run custom bettercap commands if you want
# agent.run('ble.recon on')
# or set a custom state
# agent.set_bored()
# called when the AI finished loading
def on_ai_ready(self, agent):
pass
# called when the AI finds a new set of parameters
def on_ai_policy(self, agent, policy):
pass
# called when the AI starts training for a given number of epochs
def on_ai_training_start(self, agent, epochs):
pass
# called after the AI completed a training epoch
def on_ai_training_step(self, agent, _locals, _globals):
pass
# called when the AI has done training
def on_ai_training_end(self, agent):
pass
# called when the AI got the best reward so far
def on_ai_best_reward(self, agent, reward):
pass
# called when the AI got the worst reward so far
def on_ai_worst_reward(self, agent, reward):
pass
# called when a non overlapping wifi channel is found to be free
def on_free_channel(self, agent, channel):
pass
# called when the status is set to bored
def on_bored(self, agent):
pass
# called when the status is set to sad
def on_sad(self, agent):
pass
# called when the status is set to excited
def on_excited(self, agent):
pass
# called when the status is set to lonely
def on_lonely(self, agent):
pass
# called when the agent is rebooting the board
def on_rebooting(self, agent):
pass
# called when the agent is waiting for t seconds
def on_wait(self, agent, t):
pass
# called when the agent is sleeping for t seconds
def on_sleep(self, agent, t):
pass
# called when the agent refreshed its access points list
def on_wifi_update(self, agent, access_points):
pass
# called when the agent refreshed an unfiltered access point list
# this list contains all access points that were detected BEFORE filtering
def on_unfiltered_ap_list(self, agent, access_points):
pass
# called when the agent is sending an association frame
def on_association(self, agent, access_point):
pass
# called when the agent is deauthenticating a client station from an AP
def on_deauthentication(self, agent, access_point, client_station):
pass
# callend when the agent is tuning on a specific channel
def on_channel_hop(self, agent, channel):
pass
# called when a new handshake is captured, access_point and client_station are json objects
# if the agent could match the BSSIDs to the current list, otherwise they are just the strings of the BSSIDs
def on_handshake(self, agent, filename, access_point, client_station):
pass
# called when an epoch is over (where an epoch is a single loop of the main algorithm)
def on_epoch(self, agent, epoch, epoch_data):
pass
# called when a new peer is detected
def on_peer_detected(self, agent, peer):
pass
# called when a known peer is lost
def on_peer_lost(self, agent, peer):
pass
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