Replace ENTRANCE.SOUND menu with dropdowns

/instantbuttons displays a soundboard via a button ui

.gitignore

@@ -12,3 +12,4 @@ Pipfile.lock
 /disabled_voicelines/
 *.svg
 .vscode
+Heidi_User.conf

.gitlab-ci.yml

@@ -1,38 +0,0 @@
-workflow: # for entire pipeline
-  rules:
-    - if: '$CI_COMMIT_REF_NAME == "master"' # only run on master...
-      changes: # ...and when these files have changed
-      - "*.py"
-      - "Dockerfile"
-
-docker-build:
-  stage: build
-  image: docker:20 # provides the docker toolset (but without an active daemon)
-  services: # configure images that run during jobs linked to the image (above)
-  - docker:dind # dind build on docker and starts up the dockerdaemon (docker itself doesn't do that), which is needed to call docker build etc.
-  before_script:
-    - docker login -u $CI_REGISTRY_USER -p "$CI_REGISTRY_PASSWORD" $CI_REGISTRY
-  script:
-    - docker pull $CI_REGISTRY_IMAGE:latest || true # latest image for cache (not failing if image is not found)
-    - >
-      docker build
-      --pull
-      --cache-from $CI_REGISTRY_IMAGE:latest
-      --label "org.opencontainers.image.title=$CI_PROJECT_TITLE"
-      --label "org.opencontainers.image.url=$CI_PROJECT_URL"
-      --label "org.opencontainers.image.created=$CI_JOB_STARTED_AT"
-      --label "org.opencontainers.image.revision=$CI_COMMIT_SHA"
-      --label "org.opencontainers.image.version=$CI_COMMIT_REF_NAME"
-      --tag $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA
-      .
-    - docker tag $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA $CI_REGISTRY_IMAGE:latest
-    - docker push $CI_REGISTRY_IMAGE:latest
-
-docker-deploy:
-  stage: deploy
-  image: alpine:3.15
-  needs: ["docker-build"]
-  script:
-    - chmod og= $ID_RSA
-    - apk update && apk add openssh-client
-    - ssh -i $ID_RSA -o StrictHostKeyChecking=no $SERVER_USER@$SERVER_IP "/home/christoph/$CI_PROJECT_TITLE/launch.sh"

bot.py

@@ -1,221 +1,21 @@
 # Example: https://github.com/Rapptz/discord.py/blob/master/examples/app_commands/basic.py
 
-import os, re, random, logging, asyncio, discord, configparser
-from discord import app_commands
+from ast import Call
+import random, logging
+from discord import DMChannel
 from discord.app_commands import Choice
-from functools import reduce
-from dotenv import load_dotenv
-from typing import Dict, List, Optional, Union
-
-# TODO: Reenable + extend textgen
-# from textgen import textgen
-# from textgen_markov import MarkovTextGenerator
-# from textgen_lstm import LSTMTextGenerator
-
-# TODO: Reenable + extend scraper
-# from models import Models
-
-# We're fancy today
+from typing import Awaitable, Dict, List, Optional, Union, Callable, Any
 from rich.traceback import install
 
+from heidi_client import *
+
+# Install rich traceback
 install(show_locals=True)
-load_dotenv()
 
 
-# ================================================================================================ #
-# ================================================================================================ #
-# NOTE: Always set this correctly:
-DOCKER = os.getenv("DOCKER") == "True"
-# ================================================================================================ #
-# ================================================================================================ #
+# @todo yt-dlp music support
 
 
-# TODO: Only post in heidi-spam channel
-# TODO: yt-dlp music support
-# TODO: Somehow upload voicelines more easily (from discord voice message?)
-
-# IDs of the servers Heidi is used on
-LINUS_GUILD = discord.Object(id=431154792308408340)
-TEST_GUILD = discord.Object(id=821511861178204161)
-
-CONFIGPATH = "/config" if DOCKER else "."
-USERCONFIGNAME = "Heidi_User.conf"
-
-
-class HeidiClient(discord.Client):
-    def __init__(self, *, intents: discord.Intents):
-        super().__init__(status="Nur eine kann GNTM werden!", intents=intents)
-
-        # Separate object that keeps all application command state
-        self.tree = app_commands.CommandTree(self)
-
-        # Handle persistent user configuration
-        self.user_config = configparser.ConfigParser()
-        if not os.path.exists(f"{CONFIGPATH}/{USERCONFIGNAME}"):
-            os.mknod(f"{CONFIGPATH}/{USERCONFIGNAME}")
-        self.user_config.read(f"{CONFIGPATH}/{USERCONFIGNAME}")
-        self.update_to_default_user_config()
-        self.print_user_config()
-
-        # self.models = Models()  # scraped model list
-
-        # automatic actions on all messages
-        # on_message_triggers is a map with tuples of two functions: (predicate, action)
-        # the predicate receives the message as argument
-        # if the predicate is true the action is performed
-        self.on_message_triggers = {
-            # lambda m: m.author.nick.lower() in self.models.get_in_names(): self.autoreact_to_girls,
-            lambda m: "jeremy" in m.author.nick.lower(): self._autoreact_to_jeremy,
-            lambda m: "kardashian" in m.author.nick.lower()
-            or "jenner" in m.author.nick.lower(): self._autoreact_to_kardashian,
-        }
-
-        # automatic actions on voice state changes
-        # on_voice_state_triggers is a map with tuples of two functions: (predicate, action)
-        # the predicate receives the member, before- and after-state as arguments
-        # if the predicate is true, the action is performed
-        self.on_voice_state_triggers = {
-            lambda m, b, a: b.channel != a.channel
-            and a.channel != None
-            and isinstance(a.channel, discord.VoiceChannel): self._play_entrance_sound,
-        }
-
-        # Textgen
-        # self.textgen_models: dict[str, textgen] = {
-        #     # The name must correspond to the name of the training text file
-        #     "kommunistisches_manifest": LSTMTextGenerator(10),
-        #     "musk": LSTMTextGenerator(10),
-        #     "bibel": LSTMTextGenerator(10)
-        #     "bibel": MarkovTextGenerator(3), # Prefix length of 3
-        #     "kommunistisches_manifest": MarkovTextGenerator(3),
-        #     "musk": MarkovTextGenerator(3)
-        # }
-
-        # for name, model in self.textgen_models.items():
-        #     model.init(name)  # Loads the textfile
-
-        #     if os.path.exists(f"weights/{name}_lstm_model.pt"):
-        #         model.load()
-        #     elif not DOCKER:
-        #         model.train()
-        #     else:
-        #         print("Error: Can't load model", name)
-
-        #     print("Generating test sentence for", name)
-        #     self.textgen_models[name].generate_sentence()
-
-    # Synchronize commands to guilds
-    async def setup_hook(self):
-        self.tree.copy_global_to(guild=LINUS_GUILD)
-        await self.tree.sync(guild=LINUS_GUILD)
-
-        self.tree.copy_global_to(guild=TEST_GUILD)
-        await self.tree.sync(guild=TEST_GUILD)
-
-    def update_to_default_user_config(self):
-        """
-        Adds config keys to the config, if they don't exist yet.
-        """
-        user_config_sections = ["ENTRANCE.SOUND"]
-
-        for section in user_config_sections:
-            if section not in self.user_config:
-                print(f"Adding section {section} to {CONFIGPATH}/{USERCONFIGNAME}")
-                self.user_config[section] = dict()
-
-        self.write_user_config()
-
-    def print_user_config(self):
-        print("Read persistent configuration:\n")
-
-        for section in self.user_config.sections():
-            print(f"[{section}]")
-            for key in self.user_config[section]:
-                print(f"{key}={self.user_config[section][key]}")
-
-        print("")
-
-    def write_user_config(self):
-        if not os.path.exists(f"{CONFIGPATH}/{USERCONFIGNAME}"):
-            print(f"Error: {CONFIGPATH}/{USERCONFIGNAME} doesn't exist!")
-            return
-
-        print(f"Writing {CONFIGPATH}/{USERCONFIGNAME}")
-
-        with open(f"{CONFIGPATH}/{USERCONFIGNAME}", "w") as file:
-            self.user_config.write(file)
-
-    # Commands -----------------------------------------------------------------------------------
-
-    # async def list_models_in(self, message):
-    #     """
-    #     wer ist dabei?
-    #     """
-    #     await message.channel.send("\n".join(self.models.get_in_names()))
-
-    # async def list_models_out(self, message):
-    #     """
-    #     wer ist raus? (Liste der Keks welche ge*ickt wurden)
-    #     """
-    #     await message.channel.send("\n".join(self.models.get_out_names()))
-
-    # async def show_model_picture(self, message):
-    #     """
-    #     gib Bild von <Name>
-    #     """
-    #     name = message.content.split()[-1]
-    #     picture = discord.Embed()
-    #     picture.set_image(url=self.models.get_image(name))
-    #     picture.set_footer(text=name)
-    #     await message.channel.send(embed=picture)
-
-    # Automatic Actions --------------------------------------------------------------------------
-
-    # @staticmethod
-    # async def autoreact_to_girls(message):
-    #     """
-    #     ❤ aktives Model
-    #     """
-    #     await message.add_reaction("❤")
-
-    @staticmethod
-    async def _autoreact_to_jeremy(message: discord.Message):
-        """
-        🧀 Jeremy
-        """
-        await message.add_reaction("🧀")
-
-    @staticmethod
-    async def _autoreact_to_kardashian(message: discord.Message):
-        """
-        💄 Kardashian
-        """
-        await message.add_reaction("💄")
-
-    async def _play_entrance_sound(
-        self,
-        member: discord.Member,
-        before: discord.VoiceState,
-        after: discord.VoiceState,
-    ):
-        soundpath: Union[str, None] = self.user_config["ENTRANCE.SOUND"].get(
-            member.name, None
-        )
-
-        if soundpath == None:
-            print(f"User {member.name} has not set an entrance sound")
-            return
-
-        board, sound = soundpath.split("/")
-
-        # Wait a bit to not have simultaneous joins
-        await asyncio.sleep(1)
-
-        await play_voice_line_for_member(None, member, board, sound)
-
-
-# ------------------------------------------------------------------------------------------------
-
 # Log to file
 handler = logging.FileHandler(filename="discord.log", encoding="utf-8", mode="w")
 
@@ -225,23 +25,30 @@ intents.members = True  # Allow to react to member join/leave etc
 intents.message_content = True  # Allow to read message content from arbitrary messages
 intents.voice_states = True  # Allow to process on_voice_state_update
 
-# Setup our client
+# Set up our client
 client = HeidiClient(intents=intents)
 
+
 # Events -----------------------------------------------------------------------------------------
-# NOTE: I defined the events outside of the Client class, don't know if I like it or not...
+# NOTE: I defined the events outside the Client class, don't know if I like it or not...
 
 
 @client.event
-async def on_ready():
-    if client.user != None:
+async def on_ready() -> None:
+    """
+    This event triggers when the Heidi client has finished connecting.
+    """
+    if client.user is not None:
         print(f"{client.user} (id: {client.user.id}) has connected to Discord!")
     else:
         print("client.user is None!")
 
 
 @client.event
-async def on_message(message: discord.Message):
+async def on_message(message: Message) -> None:
+    """
+    This event triggers when a message is sent in any text channel.
+    """
     # Skip Heidis own messages
     if message.author == client.user:
         return
@@ -257,8 +64,11 @@ async def on_message(message: discord.Message):
 
 @client.event
 async def on_voice_state_update(
-    member: discord.Member, before: discord.VoiceState, after: discord.VoiceState
-):
+    member: Member, before: VoiceState, after: VoiceState
+) -> None:
+    """
+    This event triggers when a member joins/changes/leaves a voice channel or mutes/unmutes.
+    """
     # Skip Heidis own voice state updates (e.g. on /say)
     if member._user == client.user:
         return
@@ -267,7 +77,7 @@ async def on_voice_state_update(
     # python iterates over the keys of a map
     for predicate in client.on_voice_state_triggers:
         if predicate(member, before, after):
-            action = client.on_voice_state_triggers[predicate]
+            action: Callable = client.on_voice_state_triggers[predicate]
             print(f"on_voice_state_update: calling {action.__name__}")
             await action(member, before, after)
 
@@ -275,9 +85,65 @@ async def on_voice_state_update(
 # Config Commands --------------------------------------------------------------------------------
 
 
+class EntranceSoundSoundSelect(discord.ui.Select):
+    def __init__(self, board: str, on_sound_select_callback):
+        self.board = board
+        self.on_sound_select_callback = on_sound_select_callback
+
+        options: List[discord.SelectOption] = [
+            discord.SelectOption(label=sound.split(".")[0], value=sound)
+            for sound in os.listdir(f"{SOUNDDIR}/{board}")
+        ]
+
+        super().__init__(
+            placeholder="Select Sound", min_values=1, max_values=1, options=options
+        )
+
+    async def callback(self, interaction: Interaction):
+        await self.on_sound_select_callback(interaction, self.board, self.values[0])
+
+
+class EntranceSoundSoundView(discord.ui.View):
+    def __init__(self, board: str, on_sound_select_callback):
+        super().__init__(timeout=600)
+
+        self.add_item(EntranceSoundSoundSelect(board, on_sound_select_callback))
+
+
+class EntranceSoundBoardSelect(discord.ui.Select):
+    def __init__(self, on_sound_select_callback):
+        self.on_sound_select_callback = on_sound_select_callback
+
+        options: List[discord.SelectOption] = [
+            discord.SelectOption(label=board, value=board)
+            for board in os.listdir(f"{SOUNDDIR}")
+        ]
+
+        super().__init__(
+            placeholder="Select Board", min_values=1, max_values=1, options=options
+        )
+
+    async def callback(self, interaction: Interaction):
+        await interaction.response.send_message(
+            f"Welchen sound willst du?",
+            view=EntranceSoundSoundView(self.values[0], self.on_sound_select_callback),
+            ephemeral=True,
+        )
+
+
+class EntranceSoundBoardView(discord.ui.View):
+    def __init__(self, on_sound_select_callback):
+        super().__init__(timeout=600)
+
+        self.add_item(EntranceSoundBoardSelect(on_sound_select_callback))
+
+
 async def user_config_key_autocomplete(
-    interaction: discord.Interaction, current: str
+    interaction: Interaction, current: str
 ) -> List[Choice[str]]:
+    """
+    Suggest a value from the user config keys (each .conf section is a key).
+    """
     return [
         Choice(name=key, value=key)
         for key in client.user_config.sections()
@@ -285,45 +151,6 @@ async def user_config_key_autocomplete(
     ]
 
 
-async def user_config_value_autocomplete(
-    interaction: discord.Interaction, current: str
-) -> List[Choice[str]]:
-    """
-    Calls an autocomplete function depending on the entered config_key.
-    """
-    autocompleters = {"ENTRANCE.SOUND": user_entrance_sound_autocomplete}
-    autocompleter = autocompleters[interaction.namespace.option]
-
-    print(f"config_value_autocomplete: calling {autocompleter.__name__}")
-
-    return autocompleter(interaction, current)
-
-
-def user_entrance_sound_autocomplete(
-    interaction: discord.Interaction, current: str
-) -> List[Choice[str]]:
-    """
-    Generates autocomplete options for the ENTRANCE.SOUND config key.
-    """
-    boards: List[str] = os.listdir(SOUNDDIR)
-    all_sounds: Dict[str, List[str]] = {
-        board: list(map(lambda x: x.split(".")[0], os.listdir(f"{SOUNDDIR}/{board}/")))
-        for board in boards
-    }  # These are all sounds, organized per board
-
-    completions: List[Choice[str]] = []
-    for (
-        board,
-        board_sounds,
-    ) in all_sounds.items():  # Iterate over all sounds, organized per board
-        for sound in board_sounds:  # Iterate over board specific sounds
-            soundpath = f"{board}/{sound}"
-            if soundpath.lower().startswith(current.lower()):
-                completions += [Choice(name=soundpath, value=soundpath)]
-
-    return completions
-
-
 @client.tree.command(
     name="userconfig",
     description="User-spezifische Heidi-Einstellungen (Heidi merkt sie sich in ihrem riesigen Gehirn).",
@@ -331,48 +158,54 @@ def user_entrance_sound_autocomplete(
 @app_commands.rename(config_key="option")
 @app_commands.describe(config_key="Die Option, welche du ändern willst.")
 @app_commands.autocomplete(config_key=user_config_key_autocomplete)
-@app_commands.rename(config_value="wert")
-@app_commands.describe(
-    config_value="Der Wert, auf welche die Option gesetzt werden soll."
-)
-@app_commands.autocomplete(config_value=user_config_value_autocomplete)
-async def user_config(
-    interaction: discord.Interaction, config_key: str, config_value: str
-):
+@enforce_channel(HEIDI_SPAM_ID)
+async def user_config(interaction: Interaction, config_key: str) -> None:
+    """
+    Set a user config value for the calling user.
+    """
     # Only Members can set settings
-    if not isinstance(interaction.user, discord.Member):
+    if not isinstance(interaction.user, Member):
         print("User not a member")
-        await interaction.response.send_message("Heidi sagt: Komm in die Gruppe!")
+        await interaction.response.send_message(
+            "Heidi sagt: Komm in die Gruppe!", ephemeral=True
+        )
         return
 
-    member: discord.Member = interaction.user
+    member: Member = interaction.user
 
-    client.user_config[config_key][member.name] = config_value
-    client.write_user_config()
+    async def on_sound_select_callback(interaction, board: str, sound: str):
+        """
+        This function is called, when an EntrySoundSoundSelect option is selected.
+        """
+        client.user_config[config_key][member.name] = f"{board}/{sound}"
+        client.write_user_config()
+
+        await interaction.response.send_message(
+            f"Ok, ich schreibe {member.name}={board}/{sound} in mein fettes Gehirn!",
+            ephemeral=True,
+        )
+
+    # Views for different user config options are defined here
+    views = {"ENTRANCE.SOUND": (EntranceSoundBoardView, on_sound_select_callback)}
+
+    view, select_callback = views[config_key]
 
     await interaction.response.send_message(
-        f"Ok, ich schreibe {member.name}={config_value} in mein fettes Gehirn!"
+        f"Aus welchem Soundboard soll dein sound sein?",
+        view=view(select_callback),
+        ephemeral=True,
     )
 
 
 # Commands ---------------------------------------------------------------------------------------
 
 
-@client.tree.command(
-    name="giblinkbruder",
-    description="Heidi hilft mit dem Link zu deiner Lieblingsshow im Qualitätsfernsehen.",
-)
-async def show_link(interaction: discord.Interaction):
-    link_pro7 = "https://www.prosieben.de/tv/germanys-next-topmodel/livestream"
-    link_joyn = "https://www.joyn.de/serien/germanys-next-topmodel"
-
-    await interaction.response.send_message(
-        f"ProSieben: {link_pro7}\nJoyn: {link_joyn}"
-    )
-
-
 @client.tree.command(name="heidi", description="Heidi!")
-async def heidi_exclaim(interaction: discord.Interaction):
+@enforce_channel(HEIDI_SPAM_ID)
+async def heidi_exclaim(interaction: Interaction) -> None:
+    """
+    Print a random Heidi quote.
+    """
     messages = [
         "Die sind doch fast 18!",
         "Heidi!",
@@ -381,6 +214,10 @@ async def heidi_exclaim(interaction: discord.Interaction):
         "Warum denn so schüchtern?",
         "Im TV ist das legal!",
         "Das Stroh ist nur fürs Shooting!",
+        "Jetzt sei doch mal sexy!",
+        "Stell dich nicht so an!",
+        "Models müssen da halt durch!",
+        "Heul doch nicht!",
     ]
     await interaction.response.send_message(random.choice(messages))
 
@@ -388,16 +225,27 @@ async def heidi_exclaim(interaction: discord.Interaction):
 @client.tree.command(name="miesmuschel", description="Was denkt Heidi?")
 @app_commands.rename(question="frage")
 @app_commands.describe(question="Heidi wird es beantworten!")
-async def magic_shell(interaction: discord.Interaction, question: str):
+@enforce_channel(HEIDI_SPAM_ID)
+async def magic_shell(interaction: Interaction, question: str) -> None:
+    """
+    Answer a yes/no question.
+    """
+    # Should be equal amounts of yes/no answers, to have a 50/50 chance.
     choices = [
         "Ja!",
-        "Jo.",
+        "Jo",
         "Total!",
-        "Natürlich.",
+        "Natürlich",
+        "Klaro Karo",
+        "Offensichtlich Sherlock",
+        "Tom sagt Ja",
         "Nein!",
         "Nö.",
         "Nä.",
         "Niemals!",
+        "Nur über meine Leiche du Hurensohn!",
+        "In deinen Träumen.",
+        "Tom sagt Nein",
     ]
     question = question.strip()
     question_mark = "" if question[-1] == "?" else "?"
@@ -406,58 +254,32 @@ async def magic_shell(interaction: discord.Interaction, question: str):
     )
 
 
-# TODO: Allow , separated varargs, need to parse manually as slash commands don't support varargs
+# @todo Allow , separated varargs, need to parse manually as slash commands don't support varargs
 @client.tree.command(name="wähle", description="Heidi trifft die Wahl!")
 @app_commands.rename(option_a="entweder")
 @app_commands.describe(option_a="Ist es vielleicht dies?")
 @app_commands.rename(option_b="oder")
 @app_commands.describe(option_b="Oder doch eher das?")
-async def choose(interaction: discord.Interaction, option_a: str, option_b: str):
+@enforce_channel(HEIDI_SPAM_ID)
+async def choose(interaction: Interaction, option_a: str, option_b: str) -> None:
+    """
+    Select an answer from two options.
+    """
     options = [option_a.strip(), option_b.strip()]
     await interaction.response.send_message(
         f"{options[0]} oder {options[1]}?\nHeidi sagt: {random.choice(options)}"
     )
 
 
-# TextGen ----------------------------------------------------------------------------------------
-
-
-# async def quote_model_autocomplete(interaction: discord.Interaction, current: str) -> list[Choice[str]]:
-#     models = client.textgen_models.keys()
-#     return [Choice(name=model, value=model) for model in models]
-
-# @client.tree.command(name="zitat", description="Heidi zitiert!")
-# @app_commands.rename(quote_model = "style")
-# @app_commands.describe(quote_model = "Woraus soll Heidi zitieren?")
-# @app_commands.autocomplete(quote_model = quote_model_autocomplete)
-# async def quote(interaction: discord.Interaction, quote_model: str):
-#     generated_quote = client.textgen_models[quote_model].generate_sentence()
-#     joined_quote = " ".join(generated_quote)
-#     await interaction.response.send_message(f"Heidi zitiert: \"{joined_quote}\"")
-
-# @client.tree.command(name="vervollständige", description="Heidi beendet den Satz!")
-# @app_commands.rename(prompt = "satzanfang")
-# @app_commands.describe(prompt = "Der Satzanfang wird vervollständigt.")
-# @app_commands.rename(quote_model = "style")
-# @app_commands.describe(quote_model = "Woraus soll Heidi vervollständigen?")
-# @app_commands.autocomplete(quote_model = quote_model_autocomplete)
-# async def complete(interaction: discord.Interaction, prompt: str, quote_model: str):
-#     prompt = re.sub(r"[^a-zäöüß'.,]+", " ", prompt.lower()) # only keep valid chars
-#     generated_quote = client.textgen_models[quote_model].complete_sentence(prompt.split())
-#     joined_quote = " ".join(generated_quote)
-#     await interaction.response.send_message(f"Heidi sagt: \"{joined_quote}\"")
-
-
 # Sounds -----------------------------------------------------------------------------------------
 
 
-SOUNDDIR: str = "/sounds" if DOCKER else "./heidi-sounds"
-
-
-# Example: https://discordpy.readthedocs.io/en/latest/interactions/api.html?highlight=autocomplete#discord.app_commands.autocomplete
 async def board_autocomplete(
-    interaction: discord.Interaction, current: str
+    interaction: Interaction, current: str
 ) -> List[Choice[str]]:
+    """
+    Suggest a sound board.
+    """
     boards: List[str] = os.listdir(SOUNDDIR)
 
     return [
@@ -468,15 +290,16 @@ async def board_autocomplete(
 
 
 async def sound_autocomplete(
-    interaction: discord.Interaction, current: str
+    interaction: Interaction, current: str
 ) -> List[Choice[str]]:
+    """
+    Suggest a sound from an already selected board.
+    """
     board: str = interaction.namespace.board
-    sounds: List[str] = list(
-        map(lambda x: x.split(".")[0], os.listdir(f"{SOUNDDIR}/{board}/"))
-    )
+    sounds: List[str] = os.listdir(f"{SOUNDDIR}/{board}/")
 
     return [
-        Choice(name=sound, value=sound)
+        Choice(name=sound.split(".")[0], value=sound)
         for sound in sounds
         if sound.lower().startswith(current.lower())
     ]
@@ -488,32 +311,86 @@ async def sound_autocomplete(
 @app_commands.describe(sound="Was soll Heidi sagen?")
 @app_commands.autocomplete(board=board_autocomplete)
 @app_commands.autocomplete(sound=sound_autocomplete)
-async def say_voiceline(interaction: discord.Interaction, board: str, sound: str):
+@enforce_channel(HEIDI_SPAM_ID)
+async def say_voiceline(interaction: Interaction, board: str, sound: str) -> None:
+    """
+    Play a voiceline in the calling member's current voice channel.
+    """
     # Only Members can access voice channels
-    if not isinstance(interaction.user, discord.Member):
+    if not isinstance(interaction.user, Member):
         print("User not a member")
-        await interaction.response.send_message("Heidi sagt: Komm in die Gruppe!")
+        await interaction.response.send_message(
+            "Heidi sagt: Komm in die Gruppe!", ephemeral=True
+        )
         return
 
-    member: discord.Member = interaction.user
+    member: Member = interaction.user
 
     await play_voice_line_for_member(interaction, member, board, sound)
 
 
+class InstantButton(discord.ui.Button):
+    def __init__(self, label: str, board: str, sound: str):
+        super().__init__(style=discord.ButtonStyle.red, label=label)
+
+        self.board = board
+        self.sound = sound
+
+    async def callback(self, interaction: Interaction):
+        """
+        Handle a press of the button.
+        """
+        if not isinstance(interaction.user, Member):
+            await interaction.response.send_message(
+                "Heidi mag keine discord.User, nur discord.Member!", ephemeral=True
+            )
+            return
+
+        await play_voice_line_for_member(
+            interaction, interaction.user, self.board, self.sound
+        )
+
+
+class InstantButtonsView(discord.ui.View):
+    def __init__(self, board: str, timeout=None):
+        super().__init__(timeout=timeout)
+
+        sounds = os.listdir(f"{SOUNDDIR}/{board}")
+        for sound in sounds:
+            self.add_item(InstantButton(sound.split(".")[0], board, sound))
+
+
+@client.tree.command(
+    name="instantbuttons", description="Heidi malt Knöpfe für Sounds in den Chat."
+)
+@app_commands.describe(board="Welches Soundboard soll knöpfe bekommen?")
+@app_commands.autocomplete(board=board_autocomplete)
+@enforce_channel(HEIDI_SPAM_ID)
+async def soundboard_buttons(interaction: Interaction, board: str) -> None:
+    await interaction.response.send_message(
+        f"Soundboard: {board.capitalize()}", view=InstantButtonsView(board)
+    )
+
+
 # Contextmenu ------------------------------------------------------------------------------------
 
 
 # Callable on members
 @client.tree.context_menu(name="beleidigen")
 async def insult(
-    interaction: discord.Interaction, member: discord.Member
-):  # with message: discord.Message this can be called on a message
+    interaction: Interaction, member: Member
+) -> None:  # with message: discord.Message this can be called on a message
+    """
+    Send an insult to a member via direct message.
+    """
     if not member.dm_channel:
         await member.create_dm()
 
     if not member.dm_channel:
         print("Error creating DMChannel!")
-        await interaction.response.send_message("Heidi sagt: Gib mal DM Nummer süße*r!")
+        await interaction.response.send_message(
+            "Heidi sagt: Gib mal DM Nummer süße*r!", ephemeral=True
+        )
         return
 
     insults = [
@@ -534,67 +411,10 @@ async def insult(
 
     await member.dm_channel.send(random.choice(insults))
     await interaction.response.send_message(
-        "Anzeige ist raus!"
+        "Anzeige ist raus!", ephemeral=True
     )  # with ephemeral = True only the caller can see the answer
 
 
-# Helpers ----------------------------------------------------------------------------------------
-
-
-async def play_voice_line(
-    interaction: Union[discord.Interaction, None],
-    voice_channel: discord.VoiceChannel,
-    board: str,
-    sound: str,
-):
-    try:
-        open(f"{SOUNDDIR}/{board}/{sound}.mkv")
-    except IOError:
-        print("Error: Invalid soundfile!")
-        if interaction != None:
-            await interaction.response.send_message(
-                f'Heidi sagt: "{board}/{sound}" kanninich finden bruder'
-            )
-        return
-
-    if interaction != None:
-        await interaction.response.send_message(f'Heidi sagt: "{board}/{sound}"')
-
-    audio_source = discord.FFmpegPCMAudio(
-        f"{SOUNDDIR}/{board}/{sound}.mkv"
-    )  # only works from docker
-    voice_client = await voice_channel.connect()
-    voice_client.play(audio_source)
-
-    while voice_client.is_playing():
-        await asyncio.sleep(1)
-
-    await voice_client.disconnect()
-
-
-async def play_voice_line_for_member(
-    interaction: Union[discord.Interaction, None],
-    member: discord.Member,
-    board: str,
-    sound: str,
-):
-    # Member needs to be in voice channel to hear audio (Heidi needs to know the channel to join)
-    if (
-        member == None
-        or member.voice == None
-        or member.voice.channel == None
-        or not isinstance(member.voice.channel, discord.VoiceChannel)
-    ):
-        print("User not in (valid) voice channel!")
-        if interaction != None:
-            await interaction.response.send_message("Heidi sagt: Komm in den Channel!")
-        return
-
-    voice_channel: discord.VoiceChannel = member.voice.channel
-
-    await play_voice_line(interaction, voice_channel, board, sound)
-
-
 # ------------------------------------------------------------------------------------------------
 
 

flake.lock

@@ -6,11 +6,11 @@
         "systems": "systems"
       },
       "locked": {
-        "lastModified": 1700815693,
-        "narHash": "sha256-JtKZEQUzosrCwDsLgm+g6aqbP1aseUl1334OShEAS3s=",
+        "lastModified": 1701787589,
+        "narHash": "sha256-ce+oQR4Zq9VOsLoh9bZT8Ip9PaMLcjjBUHVPzW5d7Cw=",
         "owner": "numtide",
         "repo": "devshell",
-        "rev": "7ad1c417c87e98e56dcef7ecd0e0a2f2e5669d51",
+        "rev": "44ddedcbcfc2d52a76b64fb6122f209881bd3e1e",
         "type": "github"
       },
       "original": {
@@ -24,11 +24,11 @@
         "systems": "systems_2"
       },
       "locked": {
-        "lastModified": 1694529238,
-        "narHash": "sha256-zsNZZGTGnMOf9YpHKJqMSsa0dXbfmxeoJ7xHlrt+xmY=",
+        "lastModified": 1701680307,
+        "narHash": "sha256-kAuep2h5ajznlPMD9rnQyffWG8EM/C73lejGofXvdM8=",
         "owner": "numtide",
         "repo": "flake-utils",
-        "rev": "ff7b65b44d01cf9ba6a71320833626af21126384",
+        "rev": "4022d587cbbfd70fe950c1e2083a02621806a725",
         "type": "github"
       },
       "original": {
@@ -55,11 +55,11 @@
     },
     "nixpkgs_2": {
       "locked": {
-        "lastModified": 1700856099,
-        "narHash": "sha256-RnEA7iJ36Ay9jI0WwP+/y4zjEhmeN6Cjs9VOFBH7eVQ=",
+        "lastModified": 1701693815,
+        "narHash": "sha256-7BkrXykVWfkn6+c1EhFA3ko4MLi3gVG0p9G96PNnKTM=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "0bd59c54ef06bc34eca01e37d689f5e46b3fe2f1",
+        "rev": "09ec6a0881e1a36c29d67497693a67a16f4da573",
         "type": "github"
       },
       "original": {

flake.nix

@@ -14,27 +14,6 @@
           overlays = [ devshell.overlays.default ];
         };
 
-        # TODO: Originally it was nixpkgs.fetchurl but that didn't work, pkgs.fetchurl did...
-        #       Determine the difference between nixpkgs and pkgs
-
-        # Taken from: https://github.com/gbtb/nix-stable-diffusion/blob/master/flake.nix
-        # Overlay: https://nixos.wiki/wiki/Overlays
-        # FetchURL: https://ryantm.github.io/nixpkgs/builders/fetchers/
-        torch-rocm = pkgs.hiPrio (pkgs.python310Packages.torch-bin.overrideAttrs (old: {
-          src = pkgs.fetchurl {
-            name = "torch-1.12.1+rocm5.1.1-cp310-cp310-linux_x86_64.whl";
-            url = "https://download.pytorch.org/whl/rocm5.1.1/torch-1.12.1%2Brocm5.1.1-cp310-cp310-linux_x86_64.whl";
-            hash = "sha256-kNShDx88BZjRQhWgnsaJAT8hXnStVMU1ugPNMEJcgnA=";
-          };
-        }));
-        torchvision-rocm = pkgs.hiPrio (pkgs.python310Packages.torchvision-bin.overrideAttrs (old: {
-          src = pkgs.fetchurl {
-            name = "torchvision-0.13.1+rocm5.1.1-cp310-cp310-linux_x86_64.whl";
-            url = "https://download.pytorch.org/whl/rocm5.1.1/torchvision-0.13.1%2Brocm5.1.1-cp310-cp310-linux_x86_64.whl";
-            hash = "sha256-mYk4+XNXU6rjpgWfKUDq+5fH/HNPQ5wkEtAgJUDN/Jg=";
-          };
-        }));
-
         myPython = pkgs.python311.withPackages (p: with p; [
           # Basic
           rich

heidi_client.py

@@ -0,0 +1,145 @@
+import configparser
+from discord import app_commands, Message, VoiceState
+
+from heidi_constants import *
+from heidi_helpers import *
+
+
+class HeidiClient(discord.Client):
+    def __init__(self, *, intents: discord.Intents):
+        super().__init__(status="Nur eine kann GNTM werden!", intents=intents)
+
+        # Separate object that keeps all application command state
+        self.tree = app_commands.CommandTree(self)
+
+        # Handle persistent user configuration
+        self.user_config = configparser.ConfigParser()
+        if not os.path.exists(f"{CONFIGPATH}/{USERCONFIGNAME}"):
+            open(f"{CONFIGPATH}/{USERCONFIGNAME}", "x")
+        self.user_config.read(f"{CONFIGPATH}/{USERCONFIGNAME}")
+        self.update_to_default_user_config()
+        self.print_user_config()
+
+        # automatic actions on all messages
+        # on_message_triggers is a map with tuples of two functions: (predicate, action)
+        # the predicate receives the message as argument
+        # if the predicate is true the action is performed
+        self.on_message_triggers = {
+            # lambda m: m.author.nick.lower() in self.models.get_in_names(): self.autoreact_to_girls,
+            lambda m: "jeremy" in m.author.nick.lower(): self._autoreact_to_jeremy,
+            lambda m: "kardashian" in m.author.nick.lower()
+            or "jenner" in m.author.nick.lower(): self._autoreact_to_kardashian,
+        }
+
+        # automatic actions on voice state changes
+        # on_voice_state_triggers is a map with tuples of two functions: (predicate, action)
+        # the predicate receives the member, before- and after-state as arguments
+        # if the predicate is true, the action is performed
+        self.on_voice_state_triggers = {
+            lambda m, b, a: b.channel != a.channel
+            and a.channel is not None
+            and isinstance(a.channel, VoiceChannel): self._play_entrance_sound,
+        }
+
+    # Synchronize commands to guilds
+    async def setup_hook(self):
+        self.tree.copy_global_to(guild=LINUS_GUILD)
+        await self.tree.sync(guild=LINUS_GUILD)
+
+        self.tree.copy_global_to(guild=TEST_GUILD)
+        await self.tree.sync(guild=TEST_GUILD)
+
+    def update_to_default_user_config(self) -> None:
+        """
+        Adds config keys to the config, if they don't exist yet.
+        This writes the user config file.
+        """
+        user_config_sections = ["ENTRANCE.SOUND"]
+
+        for section in user_config_sections:
+            if section not in self.user_config:
+                print(f"Adding section {section} to {CONFIGPATH}/{USERCONFIGNAME}")
+                self.user_config[section] = dict()
+
+        self.write_user_config()
+
+    def print_user_config(self) -> None:
+        """
+        Print the current user config from memory.
+        This does not read the user config file.
+        """
+        print("Heidi User Config:\n")
+
+        for section in self.user_config.sections():
+            print(f"[{section}]")
+            for key in self.user_config[section]:
+                print(f"{key}={self.user_config[section][key]}")
+
+        print("")
+
+    def write_user_config(self) -> None:
+        """
+        Write the current configuration to disk.
+        """
+        if not os.path.exists(f"{CONFIGPATH}/{USERCONFIGNAME}"):
+            print(f"Error: {CONFIGPATH}/{USERCONFIGNAME} doesn't exist!")
+            return
+
+        print(f"Writing {CONFIGPATH}/{USERCONFIGNAME}")
+
+        with open(f"{CONFIGPATH}/{USERCONFIGNAME}", "w") as file:
+            self.user_config.write(file)
+
+    # Automatic Actions ------------------------------------------------------------------------------
+
+    @staticmethod
+    async def _autoreact_to_jeremy(message: Message) -> None:
+        """
+        🧀 Jeremy.
+        This function is set in on_message_triggers and triggered by the on_message event.
+        """
+        await message.add_reaction("🧀")
+
+    @staticmethod
+    async def _autoreact_to_kardashian(message: Message) -> None:
+        """
+        💄 Kardashian.
+        This function is set in on_message_triggers and triggered by the on_message event.
+        """
+        await message.add_reaction("💄")
+
+    async def _play_entrance_sound(
+        self,
+        member: Member,
+        before: VoiceState,
+        after: VoiceState,
+    ) -> None:
+        """
+        Play a sound when a member joins a voice channel (and another member is present).
+        This function is set in on_voice_state_triggers and triggered by the on_voice_state_update event.
+        """
+
+        # Don't play anything when no other users are present
+        if (
+            member is not None
+            and member.voice is not None
+            and member.voice.channel is not None
+            and len(member.voice.channel.members) <= 1
+        ):
+            print("Not playing entrance sound, as no other members are present")
+            return
+
+        soundpath: Union[str, None] = self.user_config["ENTRANCE.SOUND"].get(
+            member.name, None
+        )
+
+        if soundpath is None:
+            print(f"User {member.name} has not set an entrance sound")
+            return
+
+        board, sound = soundpath.split("/")
+
+        # Wait a bit to not have simultaneous joins
+        await asyncio.sleep(1)
+
+        await play_voice_line_for_member(None, member, board, sound)

heidi_constants.py

@@ -0,0 +1,29 @@
+import os
+import discord
+from dotenv import load_dotenv
+
+# This is run when this file is imported
+load_dotenv()
+
+print("Debug: Importing heidi_constants.py")
+
+
+# ================================================================================================ #
+# ================================================================================================ #
+# NOTE: Always set this correctly:
+DOCKER = os.getenv("DOCKER") == "True"
+# ================================================================================================ #
+# ================================================================================================ #
+
+# Constants
+CONFIGPATH = "/config" if DOCKER else "."
+USERCONFIGNAME = "Heidi_User.conf"
+
+SOUNDDIR: str = "/sounds" if DOCKER else "./heidi-sounds"
+
+# IDs of the servers Heidi is used on
+LINUS_GUILD = discord.Object(id=431154792308408340)
+TEST_GUILD = discord.Object(id=821511861178204161)
+
+# Channel IDs
+HEIDI_SPAM_ID = 822223476101742682

heidi_helpers.py

@@ -0,0 +1,105 @@
+import asyncio
+import functools
+from typing import Union
+
+import discord
+from discord import Interaction, VoiceChannel, Member
+
+from heidi_constants import *
+
+print("Debug: Importing heidi_helpers.py")
+
+
+# Checks -----------------------------------------------------------------------------------------
+
+
+# 1. @enforce_channel(ID) is added to a function, which evaluates to decorate with the channel_id in its closure
+# 2. The function is passed to decorate(function),
+def enforce_channel(channel_id):
+    """
+    Only run a function if called from the correct channel.
+    """
+    def decorate(function):
+
+        @functools.wraps(function)
+        async def wrapped(*args, **kwargs):
+            """
+            Sends an interaction response if the interaction is not triggered from the heidi_spam channel.
+            """
+            interaction: Interaction = args[0]
+
+            # Do not call the decorated function if the channel_id doesn't match
+            if not interaction.channel_id == channel_id:
+                await interaction.response.send_message("Heidi sagt: Geh in heidi_spam du dulli", ephemeral=True)
+                return
+
+            await function(*args, **kwargs)
+
+        return wrapped
+
+    return decorate
+
+
+# Sounds -----------------------------------------------------------------------------------------
+
+
+# @todo Normalize volume when playing
+async def play_voice_line(
+    interaction: Union[Interaction, None],
+    voice_channel: VoiceChannel,
+    board: str,
+    sound: str,
+) -> None:
+    """
+    Play a voice line in the specified channel.
+    """
+    try:
+        open(f"{SOUNDDIR}/{board}/{sound}")
+    except IOError:
+        print(f"Error: Invalid soundfile {SOUNDDIR}/{board}/{sound}!")
+        if interaction is not None:
+            await interaction.response.send_message(
+                f'Heidi sagt: "{board}/{sound}" kanninich finden bruder',
+                ephemeral=True
+            )
+        return
+
+    if interaction is not None:
+        await interaction.response.send_message(f'Heidi sagt: "{board}/{sound}"', ephemeral=True)
+
+    audio_source = discord.FFmpegPCMAudio(
+        f"{SOUNDDIR}/{board}/{sound}"
+    )  # only works from docker
+    voice_client = await voice_channel.connect()
+    voice_client.play(audio_source)
+
+    while voice_client.is_playing():
+        await asyncio.sleep(1)
+
+    await voice_client.disconnect()
+
+
+async def play_voice_line_for_member(
+    interaction: Union[Interaction, None],
+    member: Member,
+    board: str,
+    sound: str,
+) -> None:
+    """
+    Play a voice line in the member's current channel.
+    """
+    # Member needs to be in voice channel to hear audio (Heidi needs to know the channel to join)
+    if (
+        member is None
+        or member.voice is None
+        or member.voice.channel is None
+        or not isinstance(member.voice.channel, VoiceChannel)
+    ):
+        print("User not in (valid) voice channel!")
+        if interaction is not None:
+            await interaction.response.send_message("Heidi sagt: Komm in den Channel!", ephemeral=True)
+        return
+
+    voice_channel: VoiceChannel = member.voice.channel
+
+    await play_voice_line(interaction, voice_channel, board, sound)

launch.sh

@@ -1,9 +0,0 @@
-#!/bin/sh
-
-cd /home/christoph/HeidiBot
-git pull
-
-docker pull registry.gitlab.com/churl/heidibot
-docker container rm -f heidibot
-docker run -d --env-file /home/christoph/HeidiBot/.env --mount src=/home/christoph/HeidiBot/voicelines,target=/sounds,type=bind --name heidibot registry.gitlab.com/churl/heidibot
-docker image prune -f

models.py

@@ -1,33 +0,0 @@
-#!/usr/bin/env python3
-
-import requests
-import re
-from bs4 import BeautifulSoup
-
-
-class Models:
-    def __init__(self):
-        url_girls = "https://www.prosieben.de/tv/germanys-next-topmodel/models"
-
-        html_girls = requests.get(url_girls)
-        soup_girls = BeautifulSoup(html_girls.text, "html.parser")
-
-        girls_in = soup_girls.findAll("a", class_="candidate-in")
-        girls_out = soup_girls.findAll("a", class_="candidate-out")
-
-        self.girls_in = {girl.get("title").lower(): girl for girl in girls_in}
-        self.girls_out = {girl.get("title").lower(): girl for girl in girls_out}
-
-        self.girls = {**self.girls_in, **self.girls_out}
-
-    def get_in_names(self):
-        return self.girls_in.keys()
-
-    def get_out_names(self):
-        return self.girls_out.keys()
-
-    def get_image(self, name):
-        style = self.girls[name.lower()].find("figure", class_="teaser-img").get("style")
-        url = re.search(r"url\(.*\);", style).group()
-
-        return url[4:-9] + "562x996"  # increase resolution

requirements.txt

@@ -3,12 +3,3 @@ rich
 discord.py # maintained again
 pynacl # voice support
 python-dotenv # discord guild secrets
-
-# Webscraping
-# requests
-# beautifulsoup4
-
-# Textgeneration
-# torch
-# numpy
-# nltk

textgen.py

@@ -1,44 +0,0 @@
-#!/usr/bin/env python3
-
-from rich.traceback import install
-install()
-
-from abc import ABC, abstractmethod
-
-# In Python it is generally not needed to use abstract classes, but I wanted to do it safely
-
-class textgen(ABC):
-    @abstractmethod
-    def init(self, filename):
-        """
-        filename - The file (same directory as textgen.py) that contains the training text
-        """
-        raise NotImplementedError("Can't use abstract class")
-
-    @abstractmethod
-    def load(self):
-        """
-        Load the trained markov chain from a precomputed file
-        """
-        raise NotImplementedError("Can't use abstract class")
-
-    @abstractmethod
-    def train(self):
-        """
-        Generate the markov chain, uses prefix length defined in init()
-        """
-        raise NotImplementedError("Can't use abstract class")
-
-    @abstractmethod
-    def generate_sentence(self):
-        """
-        Generate a series of words/characters until a . is generated
-        """
-        raise NotImplementedError("Can't use abstract class")
-
-    @abstractmethod
-    def complete_sentence(self, prefix):
-        """
-        Generate the rest of a sentence for a given beginning
-        """
-        raise NotImplementedError("Can't use abstract class")

textgen_lstm.py

@@ -1,303 +0,0 @@
-#!/usr/bin/env python3
-
-import re, random
-import numpy as np
-import matplotlib.pyplot as plt
-import torch
-import torch.nn.functional as F
-from textgen import textgen
-from torch import nn, optim
-
-from rich.traceback import install
-install()
-
-# Model =======================================================================================
-# https://towardsdatascience.com/text-generation-with-bi-lstm-in-pytorch-5fda6e7cc22c
-# Embedding -> Bi-LSTM -> LSTM -> Linear
-
-class Model(nn.ModuleList):
-
-    def __init__(self, args, device):
-        super(Model, self).__init__()
-
-        self.device = device
-
-        self.batch_size = args["batch_size"]
-        self.hidden_dim = args["hidden_dim"]
-        self.input_size = args["vocab_size"]
-        self.num_classes = args["vocab_size"]
-        self.sequence_len = args["window"]
-
-        # Dropout
-        self.dropout = nn.Dropout(0.25) # Don't need to set device for the layers as we transfer the whole model later
-
-        # Embedding layer
-        self.embedding = nn.Embedding(self.input_size, self.hidden_dim, padding_idx=0)
-
-        # Bi-LSTM
-        # Forward and backward
-        self.lstm_cell_forward = nn.LSTMCell(self.hidden_dim, self.hidden_dim)
-        self.lstm_cell_backward = nn.LSTMCell(self.hidden_dim, self.hidden_dim)
-
-        # LSTM layer
-        self.lstm_cell = nn.LSTMCell(self.hidden_dim * 2, self.hidden_dim * 2)
-
-        # Linear layer
-        self.linear = nn.Linear(self.hidden_dim * 2, self.num_classes)
-
-
-    def forward(self, x):
-        # Bi-LSTM
-        # hs = [batch_size x hidden_size]
-        # cs = [batch_size x hidden_size]
-        hs_forward = torch.zeros(x.size(0), self.hidden_dim).to(self.device) # Need to specify device here as this is not part of the model directly
-        cs_forward = torch.zeros(x.size(0), self.hidden_dim).to(self.device)
-        hs_backward = torch.zeros(x.size(0), self.hidden_dim).to(self.device)
-        cs_backward = torch.zeros(x.size(0), self.hidden_dim).to(self.device)
-
-        # LSTM
-        # hs = [batch_size x (hidden_size * 2)]
-        # cs = [batch_size x (hidden_size * 2)]
-        hs_lstm = torch.zeros(x.size(0), self.hidden_dim * 2).to(self.device)
-        cs_lstm = torch.zeros(x.size(0), self.hidden_dim * 2).to(self.device)
-
-        # Weights initialization
-        torch.nn.init.kaiming_normal_(hs_forward)
-        torch.nn.init.kaiming_normal_(cs_forward)
-        torch.nn.init.kaiming_normal_(hs_backward)
-        torch.nn.init.kaiming_normal_(cs_backward)
-        torch.nn.init.kaiming_normal_(hs_lstm)
-        torch.nn.init.kaiming_normal_(cs_lstm)
-
-        # From idx to embedding
-        out = self.embedding(x)
-
-        # Prepare the shape for LSTM Cells
-        out = out.view(self.sequence_len, x.size(0), -1)
-
-        forward = []
-        backward = []
-
-        # Unfolding Bi-LSTM
-        # Forward
-        for i in range(self.sequence_len):
-            hs_forward, cs_forward = self.lstm_cell_forward(out[i], (hs_forward, cs_forward))
-            forward.append(hs_forward)
-
-        # Backward
-        for i in reversed(range(self.sequence_len)):
-            hs_backward, cs_backward = self.lstm_cell_backward(out[i], (hs_backward, cs_backward))
-            backward.append(hs_backward)
-
-        # LSTM
-        for fwd, bwd in zip(forward, backward):
-            input_tensor = torch.cat((fwd, bwd), 1)
-            hs_lstm, cs_lstm = self.lstm_cell(input_tensor, (hs_lstm, cs_lstm))
-
-        # Last hidden state is passed through a linear layer
-        out = self.linear(hs_lstm)
-        return out
-
-
-# =============================================================================================
-
-class LSTMTextGenerator(textgen):
-
-    def __init__(self, windowsize):
-        self.windowsize = windowsize # We slide a window over the character sequence and look at the next letter,
-                                     # similar to the Markov chain order
-
-
-    def init(self, filename):
-        self.filename = filename
-
-        # Use this to generate one hot vector and filter characters
-        self.letters = ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m",
-                        "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "ä", "ö", "ü", ".", " "]
-
-        with open(f"./textfiles/{filename}.txt", "r") as file:
-            lines = [line.lower() for line in file.readlines()] # lowercase list
-            text = " ".join(lines) # single string
-            self.charbase = [char for char in text if char in self.letters] # list of characters
-
-        # Select device
-        if torch.cuda.is_available():
-            dev = "cuda:0"
-            print("Selected GPU for LSTM")
-        else:
-            dev = "cpu"
-            print("Selected CPU for LSTM")
-        self.device = torch.device(dev)
-
-        # Init model
-        self.args = {
-            "window": self.windowsize,
-            "hidden_dim": 128,
-            "vocab_size": len(self.letters),
-            "batch_size": 128,
-            "learning_rate": 0.0005,
-            "num_epochs": 100
-        }
-        self.model = Model(self.args, self.device)
-        self.model.to(self.device) # All model layers need to use the correct tensors (cpu/gpu)
-
-        # Needed for both training and generation
-        self.__generate_char_sequences()
-
-    # Helper shit
-
-    def __char_to_idx(self, char):
-        return self.letters.index(char)
-
-    def __idx_to_char(self, idx):
-        return self.letters[idx]
-
-    def __generate_char_sequences(self):
-        # Example
-        # [[21, 20, 15],
-        #  [12, 12, 14]]
-        prefixes = []
-
-        # Example
-        # [[1],
-        #  [4]]
-        suffixes = []
-
-        print("Generating LSTM char sequences...")
-        for i in range(len(self.charbase) - self.windowsize - 1):
-            prefixes.append([self.__char_to_idx(char) for char in self.charbase[i:i+self.windowsize]])
-            suffixes += [self.__char_to_idx(char) for char in self.charbase[i+self.windowsize+1]] # Bit stupid wrapping this in a list but removes possible type error
-
-        # Enter numpy terretory NOW
-        self.prefixes = np.array(prefixes)
-        self.suffixes = np.array(suffixes)
-
-        print(f"Prefixes shape: {self.prefixes.shape}")
-        print(f"Suffixes shape: {self.suffixes.shape}")
-        print("Completed.")
-
-    # Interface shit
-
-    # TODO: Also save/load generated prefixes
-    def load(self):
-        print(f"Loading \"{self.filename}\" LSTM model with {len(self.charbase)} characters from file.")
-
-        self.model.load_state_dict(torch.load(f"weights/{self.filename}_lstm_model.pt"))
-
-    def train(self):
-        print(f"Training \"{self.filename}\" LSTM model with {len(self.charbase)} characters.")
-
-        # Optimizer initialization, RMSprop for RNN
-        optimizer = optim.RMSprop(self.model.parameters(), lr=self.args["learning_rate"])
-
-        # Defining number of batches
-        num_batches = int(len(self.prefixes) / self.args["batch_size"])
-
-        # Set model in training mode
-        self.model.train()
-
-        losses = []
-
-        # Training pahse
-        for epoch in range(self.args["num_epochs"]):
-
-            # Mini batches
-            for i in range(num_batches):
-
-                # Batch definition
-                try:
-                    x_batch = self.prefixes[i * self.args["batch_size"]:(i + 1) * self.args["batch_size"]]
-                    y_batch = self.suffixes[i * self.args["batch_size"]:(i + 1) * self.args["batch_size"]]
-                except:
-                    x_batch = self.prefixes[i * self.args["batch_size"]:]
-                    y_batch = self.suffixes[i * self.args["batch_size"]:]
-
-                # Convert numpy array into torch tensors
-                x = torch.from_numpy(x_batch).type(torch.long).to(self.device)
-                y = torch.from_numpy(y_batch).type(torch.long).to(self.device)
-
-                # Feed the model
-                y_pred = self.model(x)
-
-                # Loss calculation
-                loss = F.cross_entropy(y_pred, y.squeeze()).to(self.device)
-                losses += [loss.item()]
-
-                # Clean gradients
-                optimizer.zero_grad()
-
-                # Calculate gradientes
-                loss.backward()
-
-                # Updated parameters
-                optimizer.step()
-
-                print("Epoch: %d ,  loss: %.5f " % (epoch, loss.item()))
-
-        torch.save(self.model.state_dict(), f"weights/{self.filename}_lstm_model.pt")
-        print(f"Saved \"{self.filename}\" LSTM model to file")
-
-        plt.plot(np.arange(0, len(losses)), losses)
-        plt.title(self.filename)
-        plt.show()
-
-
-    def generate_sentence(self):
-        # Randomly is selected the index from the set of sequences
-        start = np.random.randint(0, len(self.prefixes)-1)
-
-        # Convert back to string to match complete_sentence
-        pattern = "".join([self.__idx_to_char(char) for char in self.prefixes[start]]) # random sequence from the training text
-
-        return self.complete_sentence(pattern)
-
-    def complete_sentence(self, prefix):
-        print("Prefix:", prefix)
-
-        # Convert to indexes np.array
-        pattern = np.array([self.__char_to_idx(char) for char in prefix])
-
-        # Set the model in evalulation mode
-        self.model.eval()
-
-        # Define the softmax function
-        softmax = nn.Softmax(dim=1).to(self.device)
-
-        # In full_prediction we will save the complete prediction
-        full_prediction = pattern.copy()
-
-        print("Generating sentence...")
-
-        # Predic the next characters one by one, append chars to the starting pattern until . is reached, max 500 iterations
-        for _ in range(500):
-            # the numpy patterns is transformed into a tesor-type and reshaped
-            pattern = torch.from_numpy(pattern).type(torch.long).to(self.device)
-            pattern = pattern.view(1,-1)
-
-            # make a prediction given the pattern
-            prediction = self.model(pattern)
-            # it is applied the softmax function to the predicted tensor
-            prediction = softmax(prediction)
-
-            # the prediction tensor is transformed into a numpy array
-            prediction = prediction.squeeze().detach().cpu().numpy()
-            # it is taken the idx with the highest probability
-            arg_max = np.argmax(prediction)
-
-            # the current pattern tensor is transformed into numpy array
-            pattern = pattern.squeeze().detach().cpu().numpy()
-            # the window is sliced 1 character to the right
-            pattern = pattern[1:]
-            # the new pattern is composed by the "old" pattern + the predicted character
-            pattern = np.append(pattern, arg_max)
-
-            # the full prediction is saved
-            full_prediction = np.append(full_prediction, arg_max)
-
-            # Stop on . character
-            if self.__idx_to_char(arg_max) == ".":
-                break
-
-        full_prediction = "".join([self.__idx_to_char(value) for value in full_prediction])
-        print("Generated:", full_prediction)
-        return full_prediction

textgen_markov.py

@@ -1,82 +0,0 @@
-#!/usr/bin/env python3
-
-import re
-import random
-from textgen import textgen
-
-from rich.traceback import install
-install()
-
-# NOTE: This is word based, not character based
-# TODO: Serialize and save/load model (don't train on the server)
-# TODO: Maybe extract sentence beginnings and use them as starters?
-
-class MarkovTextGenerator(textgen):
-    # The greater the order (prefix length), the lesser the variation in generation, but the better the sentences (generally).
-    # If the prefix length is high there are less options to choose from, so the sentences are very close to the training text.
-    def __init__(self, order): # Set order here for better interface (only needed for markov model)
-        self.order = order
-
-    def init(self, filename): # Filename is needed for every type of model so it's part of the interface
-        with open(f"./textfiles/{filename}.txt", "r") as file:
-            # Remove all characters except a-zäöüß'.,
-            self.wordbase = re.sub(r"[^a-zäöüß'.,]+", " ", file.read().lower()).split()
-
-        self.word_table = dict()
-
-    def load(self):
-        print(f"Loaded Markov chain of order {self.order} with {len(self.wordbase)} words from file.")
-
-    def train(self):
-        print(f"Training Markov chain of order {self.order} with {len(self.wordbase)} words.")
-
-        # init the frequencies
-        for i in range(len(self.wordbase) - self.order - 1): # Look at every word in range
-            prefix = tuple(self.wordbase[i:i+self.order]) # Look at the next self.order words from current position
-            suffix = self.wordbase[i+self.order] # The next word is the suffix
-
-            if prefix not in self.word_table: # New option wooo
-                self.word_table[prefix] = []
-
-            # if suffix not in self.table[prefix]: # disable for probabilities: if the suffixes are in the list multiple times they are more common
-            self.word_table[prefix].append(suffix)
-
-        print(f"Generated suffixes for {len(self.word_table)} prefixes.")
-
-    # def generate_random(self, n):
-    #     fword = random.choice(list(self.word_table.keys())) # Random first word
-    #     output = [*fword]
-
-    #     for _ in range(self.order, n):
-    #         output.append(self.generate_word_by_word(tuple(output[-self.order :])))
-
-    #     return output
-
-    def generate_suffix_for_prefix(self, prefix: tuple):
-        if len(prefix) > self.order: # In this case we look at the last self.order elements of prefix
-            prefix = prefix[len(prefix)-self.order-1:-1]
-
-        if prefix not in self.word_table: # In this case we need to choose a possible suffix from the last word in the prefix (if prefix too short for example)
-            print(f"Prefix {prefix} not in table")
-            for key in self.word_table.keys():
-                if key[-1] == prefix[-1]:
-                    return random.choice(self.word_table[key])
-
-        return random.choice(self.word_table[prefix])
-
-    def generate_sentence(self):
-        fword = random.choice(list(self.word_table.keys()))
-        output = [*fword]
-
-        while "." not in output[-1]:
-            output.append(self.generate_suffix_for_prefix(tuple(output[-self.order:])))
-
-        return output
-
-    def complete_sentence(self, prefix):
-        output = [*prefix]
-
-        while "." not in output[-1]:
-            output.append(self.generate_suffix_for_prefix(tuple(output[-self.order:])))
-
-        return output