Пример #1
0
    async def on_message(self, message: discord.Message):
        # Prevent feedback loop
        if str(message.author) == DISCORD_USERNAME:
            return

        filtered_content = DiscordHelper.filter_content(message)

        learn = True
        # Learn from private messages
        if message.guild is None and DISCORD_LEARN_FROM_DIRECT_MESSAGE:
            DiscordTrainingDataManager().store(message)
            learn = True
        # Learn from all server messages
        elif message.guild is not None and DISCORD_LEARN_FROM_ALL:
            if str(message.channel) not in DISCORD_LEARN_CHANNEL_EXCEPTIONS:
                DiscordTrainingDataManager().store(message)
                learn = True
        # Learn from User
        elif str(message.author) == DISCORD_LEARN_FROM_USER:
            DiscordTrainingDataManager().store(message)
            learn = True

        # real-time learning
        if learn:
            self._worker.send(
                ConnectorRecvMessage(filtered_content, learn=True,
                                     reply=False))
            self._worker.recv()

        # Reply to mentions
        for mention in message.mentions:
            if str(mention) == DISCORD_USERNAME:
                self._logger.debug("Message: %s" % filtered_content)
                self._worker.send(ConnectorRecvMessage(filtered_content))
                reply = self._worker.recv()
                self._logger.debug("Reply: %s" % reply)
                if reply is not None:
                    await channel.send(message.channel, reply)
                return

        # Reply to private messages
        if message.guild is None:
            self._logger.debug("Private Message: %s" % filtered_content)
            self._worker.send(ConnectorRecvMessage(filtered_content))
            reply = self._worker.recv()
            self._logger.debug("Reply: %s" % reply)
            if reply is not None:
                await channel.send(message.channel, reply)
            return
Пример #2
0
 def run(self):
     from storage.discord import DiscordTrainingDataManager
     self._logger = logging.getLogger(self.__class__.__name__)
     self._db = DiscordTrainingDataManager()
     self._client = DiscordClient(self)
     self._client.loop.create_task(self._watchdog())
     self._client.run(self._credentials.token)
Пример #3
0
    def train(self,
              retrain_structure: bool = False,
              retrain_markov: bool = False):

        self._logger.info("Training begin")
        self._train_markov(retrain_markov)
        self._train_structure(retrain_structure)

        # Mark data as trained
        if self._twitter_connector is not None:
            from storage.twitter import TwitterTrainingDataManager
            TwitterTrainingDataManager().mark_trained()
        if self._discord_connector is not None:
            from storage.discord import DiscordTrainingDataManager
            DiscordTrainingDataManager().mark_trained()
        ImportTrainingDataManager().mark_trained()

        self._logger.info("Training end")
Пример #4
0
    async def on_message(self, message: discord.Message):
        # Ignore attachements and Feedback Loop
        if message.attachments or message.author.bot:
            return

        filtered_content = DiscordHelper.filter_content(message)

        # Ignore empty and letter messages
        if not len(filtered_content) > 2:
            return
        if filtered_content == '':
            return

        learn = False
        # Learn from private messages
        if message.guild is None and DISCORD_LEARN_FROM_DIRECT_MESSAGE:
            DiscordTrainingDataManager().store(message)
            learn = True
        # Learn from all server messages
        elif message.guild is not None and DISCORD_LEARN_FROM_ALL:
            if str(message.channel) not in DISCORD_LEARN_CHANNEL_EXCEPTIONS:
                DiscordTrainingDataManager().store(message)
                learn = True
        # Learn will accept new input from specified channel and neglect specified user
        elif str(message.channel) in DISCORD_LEARN_CHANNEL and message.content is not None:
            if str(message.author) in DISCORD_NEGLECT_LEARN:
                return
            else:
                DiscordTrainingDataManager().store(message)
                learn = True
        # Learn from Specific User
        elif str(message.author) == DISCORD_LEARN_FROM_USER:
            DiscordTrainingDataManager().store(message)
            learn = True
        # Real-time learning
        if learn:
            self._worker.send(ConnectorRecvMessage(filtered_content, learn=True, reply=False))
            self._worker.recv()


        # This pulls from discord config, just the embed footer for gags
        TALKING_VARIANT = random.choice(TALKING_TO)
        # Reply to mentions
        # Typically has embeds so be sure to enable the embed permission across all channels
        for mention in message.mentions:
            if str(mention) == DISCORD_USERNAME:
                self._logger.debug("Message: %s" % filtered_content)
                self._worker.send(ConnectorRecvMessage(filtered_content))
                reply = self._worker.recv()
                self._logger.debug("Reply: %s" % reply)
                if reply is not None:
                    embed = discord.Embed(description=reply, color=message.author.color)
                    embed.set_footer(text = TALKING_VARIANT + message.author.name, icon_url = message.author.avatar_url)
                    embed.timestamp = datetime.utcnow()
                    await asyncio.sleep(0.25)
                    await message.channel.send(embed=embed)
                return

        # Extra chunck where the bot will reply via keyword or prefix found in CHATTER_PREFIX
        # Keep in mind this can happen anywhere the bot has access to send messages
        if message.content.lower().startswith(tuple(CHATTER_PREFIX)):
            self._logger.debug("Message: %s" % filtered_content)
            self._worker.send(ConnectorRecvMessage(filtered_content))
            reply = self._worker.recv()
            self._logger.debug("Reply: %s" % reply)
            if reply is not None:
                embed = discord.Embed(description=reply, color=message.author.color)
                embed.set_footer(text = TALKING_VARIANT + message.author.name, icon_url = message.author.avatar_url)
                embed.timestamp = datetime.utcnow()
                await asyncio.sleep(0.25)
                await message.channel.send(embed=embed)
            return

        # Channel which the bot will respond without any prefixes or @mentions
        elif str(message.channel) in DISCORD_AUTO_TALK and message.content is not None:
            self._logger.debug("Message: %s" % filtered_content)
            self._worker.send(ConnectorRecvMessage(filtered_content))
            reply = self._worker.recv()
            self._logger.debug("Reply: %s" % reply)
            if reply is not None:
                embed = discord.Embed(description=reply, color=message.author.color)
                embed.set_footer(text = str(TALKING_VARIANT) + message.author.name, icon_url = message.author.avatar_url)
                embed.timestamp = datetime.utcnow()
                await asyncio.sleep(0.25)
                await message.channel.send(embed=embed)
            return

        # For the bot to reply in private messages, no embeds for private channels
        elif message.guild is None:
                self._logger.debug("Private Message: %s" % filtered_content)
                self._worker.send(ConnectorRecvMessage(filtered_content))
                reply = self._worker.recv()
                self._logger.debug("Reply: %s" % reply)
                if reply is not None:
                    await asyncio.sleep(0.25)
                    await message.channel.send(reply)
                return
Пример #5
0
    def _preprocess_markov_data(self, all_training_data: bool = False):
        spacy_preprocessor = SpacyPreprocessor()

        self._logger.info("Training_Preprocessing_Markov(Import)")
        if not all_training_data:
            imported_messages = ImportTrainingDataManager().new_training_data()
        else:
            imported_messages = ImportTrainingDataManager().all_training_data()
        for message_idx, message in enumerate(imported_messages):
            # Print Progress
            if message_idx % 100 == 0:
                self._logger.info(
                    "Training_Preprocessing_Markov(Import): %f%%" %
                    (message_idx / len(imported_messages) * 100))

            doc = self._nlp(MarkovFilters.filter_input(message[0].decode()))
            spacy_preprocessor.preprocess(doc)

        tweets = None
        if self._twitter_connector is not None:
            self._logger.info("Training_Preprocessing_Markov(Twitter)")
            from storage.twitter import TwitterTrainingDataManager

            if not all_training_data:
                tweets = TwitterTrainingDataManager().new_training_data()
            else:
                tweets = TwitterTrainingDataManager().all_training_data()
            for tweet_idx, tweet in enumerate(tweets):
                # Print Progress
                if tweet_idx % 100 == 0:
                    self._logger.info(
                        "Training_Preprocessing_Markov(Twitter): %f%%" %
                        (tweet_idx / len(tweets) * 100))

                doc = self._nlp(MarkovFilters.filter_input(tweet[0].decode()))
                spacy_preprocessor.preprocess(doc)

        discord_messages = None
        if self._discord_connector is not None:
            self._logger.info("Training_Preprocessing_Markov(Discord)")
            from storage.discord import DiscordTrainingDataManager

            if not all_training_data:
                discord_messages = DiscordTrainingDataManager(
                ).new_training_data()
            else:
                discord_messages = DiscordTrainingDataManager(
                ).all_training_data()

            for message_idx, message in enumerate(discord_messages):
                # Print Progress
                if message_idx % 100 == 0:
                    self._logger.info(
                        "Training_Preprocessing_Markov(Discord): %f%%" %
                        (message_idx / len(discord_messages) * 100))

                doc = self._nlp(MarkovFilters.filter_input(
                    message[0].decode()))
                spacy_preprocessor.preprocess(doc)

        return spacy_preprocessor
Пример #6
0
    def _preprocess_structure_data(self):
        structure_preprocessor = StructurePreprocessor()

        self._logger.info("Training_Preprocessing_Structure(Import)")
        imported_messages = ImportTrainingDataManager().all_training_data(
            limit=STRUCTURE_MODEL_TRAINING_MAX_SIZE,
            order_by='id',
            order='desc')
        for message_idx, message in enumerate(imported_messages):
            # Print Progress
            if message_idx % 100 == 0:
                self._logger.info(
                    "Training_Preprocessing_Structure(Import): %f%%" %
                    (message_idx / min(STRUCTURE_MODEL_TRAINING_MAX_SIZE,
                                       len(imported_messages)) * 100))

            doc = self._nlp(MarkovFilters.filter_input(message[0].decode()))
            if not structure_preprocessor.preprocess(doc):
                return structure_preprocessor

        tweets = None
        if self._twitter_connector is not None:
            self._logger.info("Training_Preprocessing_Structure(Twitter)")
            from storage.twitter import TwitterTrainingDataManager

            tweets = TwitterTrainingDataManager().all_training_data(
                limit=STRUCTURE_MODEL_TRAINING_MAX_SIZE,
                order_by='timestamp',
                order='desc')
            for tweet_idx, tweet in enumerate(tweets):
                # Print Progress
                if tweet_idx % 100 == 0:
                    self._logger.info(
                        "Training_Preprocessing_Structure(Twitter): %f%%" %
                        (tweet_idx / min(STRUCTURE_MODEL_TRAINING_MAX_SIZE,
                                         len(tweets)) * 100))

                doc = self._nlp(MarkovFilters.filter_input(tweet[0].decode()))
                if not structure_preprocessor.preprocess(doc):
                    return structure_preprocessor

        discord_messages = None
        if self._discord_connector is not None:
            self._logger.info("Training_Preprocessing_Structure(Discord)")
            from storage.discord import DiscordTrainingDataManager

            discord_messages = DiscordTrainingDataManager().all_training_data(
                limit=STRUCTURE_MODEL_TRAINING_MAX_SIZE,
                order_by='timestamp',
                order='desc')
            for message_idx, message in enumerate(discord_messages):
                # Print Progress
                if message_idx % 100 == 0:
                    self._logger.info(
                        "Training_Preprocessing_Structure(Discord): %f%%" %
                        (message_idx / min(STRUCTURE_MODEL_TRAINING_MAX_SIZE,
                                           len(discord_messages)) * 100))

                doc = self._nlp(MarkovFilters.filter_input(
                    message[0].decode()))
                if not structure_preprocessor.preprocess(doc):
                    return structure_preprocessor

        return structure_preprocessor
Пример #7
0
    async def on_message(self, message: discord.Message):
        # Ignore attachements and Feedback Loop
        if message.attachments or message.author.bot:
            return

        filtered_content = DiscordHelper.filter_content(message)

        # Ignore empty and letter messages
        if not len(filtered_content) > 2:
            return
        if filtered_content == '':
            return

        lang_check = detectlanguage.simple_detect(filtered_content) # Checks if the message is english or not
        lang_es = 'en'
        if lang_check != lang_es:
            return

        learn = False
        # Learn from private messages

        DISCORD_LEARN_SERVER_ID_EXCEPTION = list(map(int, DISCORD_LEARN_SERVER_ID_EXCEPTIONS)) # Take note there is an S
        DISCORD_LEARN_NEGLECT_UID = list(map(int, DISCORD_LEARN_NEGLECT_UIDS))

        if message.guild is None:
            # checks for black listed user/s
            if not str(message.author) in DISCORD_LEARN_NEGLECT_USERNAMES or message.author.id in DISCORD_LEARN_NEGLECT_UID:
            # if user/s not in list, bot will learn
                if DISCORD_LEARN_FROM_DIRECT_MESSAGE is True:
                    DiscordTrainingDataManager().store(message)
                    learn = True

        # Learn from server
        elif message.guild is not None:
            # bot will ignore specified server ID's
            if message.guild.id not in DISCORD_LEARN_SERVER_ID_EXCEPTION:

            # Learn from Specific User
                if str(message.author) == DISCORD_LEARN_FROM_USER:
                        DiscordTrainingDataManager().store(message)
                        learn = True

            # bot will always learn from this channel except from black listed user/s
                if str(message.channel) in DISCORD_LEARN_CHANNEL and not str(message.author) in DISCORD_LEARN_NEGLECT_USERNAME or message.author.id in DISCORD_LEARN_NEGLECT_UID:
                    if message.content is not None:
                        DiscordTrainingDataManager().store(message)
                        learn = True

            # section where the bot will learn everything
                if DISCORD_LEARN_FROM_ALL is True:
            # specify the channel where you do not want the bot to learn from
                    if str(message.channel) not in DISCORD_LEARN_CHANNEL_EXCEPTIONS:
                        DiscordTrainingDataManager().store(message)
                        learn = True

        # Real-time learning
        if learn:
            self._worker.send(ConnectorRecvMessage(filtered_content, learn=True, reply=False))
            self._worker.recv()


        # This pulls from discord config, just the embed footer for gags
        TALKING_VARIANT = random.choice(TALKING_TO)
        # Reply to mentions
        # Typically has embeds so be sure to enable the embed permission across all channels
        for mention in message.mentions:
            if str(mention) == DISCORD_USERNAME:
                self._logger.debug("Message: %s" % filtered_content)
                self._worker.send(ConnectorRecvMessage(filtered_content))
                reply = self._worker.recv()
                self._logger.debug("Reply: %s" % reply)
                if reply is not None:
                    embed = discord.Embed(description=reply, color=message.author.color)
                    embed.set_footer(text = TALKING_VARIANT + message.author.name, icon_url = message.author.avatar_url)
                    embed.timestamp = datetime.utcnow()
                    await asyncio.sleep(0.25)
                    await message.channel.send(embed=embed)
                return

        # Extra chunck where the bot will reply via keyword or prefix found in CHATTER_PREFIX
        # Keep in mind this can happen anywhere the bot has access to send messages and embeds
        if message.content.lower().startswith(tuple(CHATTER_PREFIX)):
            self._logger.debug("Message: %s" % filtered_content)
            self._worker.send(ConnectorRecvMessage(filtered_content))
            reply = self._worker.recv()
            self._logger.debug("Reply: %s" % reply)
            if reply is not None:
                embed = discord.Embed(description=reply, color=message.author.color)
                embed.set_footer(text = TALKING_VARIANT + message.author.name, icon_url = message.author.avatar_url)
                embed.timestamp = datetime.utcnow()
                await asyncio.sleep(0.25)
                await message.channel.send(embed=embed)
            return

        # Channel which the bot will respond without any prefixes or @mentions
        elif str(message.channel) in DISCORD_AUTO_TALK and message.content is not None:
            self._logger.debug("Message: %s" % filtered_content)
            self._worker.send(ConnectorRecvMessage(filtered_content))
            reply = self._worker.recv()
            self._logger.debug("Reply: %s" % reply)
            if reply is not None:
                embed = discord.Embed(description=reply, color=message.author.color)
                embed.set_footer(text = str(TALKING_VARIANT) + message.author.name, icon_url = message.author.avatar_url)
                embed.timestamp = datetime.utcnow()
                await asyncio.sleep(0.25)
                await message.channel.send(embed=embed)
            return

        # For the bot to reply in private messages, no embeds for private channels
        elif message.guild is None:
                self._logger.debug("Private Message: %s" % filtered_content)
                self._worker.send(ConnectorRecvMessage(filtered_content))
                reply = self._worker.recv()
                self._logger.debug("Reply: %s" % reply)
                if reply is not None:
                    await asyncio.sleep(0.25)
                    await message.channel.send(reply)
                return