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Merge pull request #118 from bobloy/chatter_develop

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Chatter Updates
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bobloy 2020-08-13 12:06:57 -04:00 committed by GitHub
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2 changed files with 140 additions and 19 deletions
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31
chatter/README.md
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@ -167,7 +167,38 @@ settings. This can take a long time to process.
```
[p]chatter algorithm X
```
or
```
[p]chatter algo X 0.95
```
Chatter can be configured to use one of three different Similarity algorithms.
Changing this can help if the response speed is too slow, but can reduce the accuracy of results.
The second argument is the minimum similarity threshold,
raising this will make the bot me more selective with the responses it finds.
Default minimum similarity threshold is 0.90
## Switching Pretrained Models
```
[p]chatter model X
```
Chatter can be configured to use one of three pretrained statistical models for English.
I have not noticed any advantage to changing this,
but supposedly it would help by splitting the search term into more useful parts.
See [here](https://spacy.io/models) for more info on spaCy models.
Before you're able to use the *large* model (option 3), you must install it through pip.
*Warning:* This is ~800MB download.
```
[p]pipinstall https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.3.1/en_core_web_lg-2.3.1.tar.gz#egg=en_core_web_lg
```

124
chatter/chat.py
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@ -1,4 +1,5 @@
import asyncio
import logging
import os
import pathlib
from datetime import datetime, timedelta
@ -7,13 +8,16 @@ import discord
from chatterbot import ChatBot
from chatterbot.comparisons import JaccardSimilarity, LevenshteinDistance, SpacySimilarity
from chatterbot.response_selection import get_random_response
from chatterbot.trainers import ChatterBotCorpusTrainer, ListTrainer
from chatterbot.trainers import ChatterBotCorpusTrainer, ListTrainer, UbuntuCorpusTrainer
from redbot.core import Config, commands
from redbot.core.commands import Cog
from redbot.core.data_manager import cog_data_path
from redbot.core.utils.predicates import MessagePredicate
log = logging.getLogger("red.fox_v3.chat")
class ENG_LG: # TODO: Add option to use this large model
class ENG_LG:
ISO_639_1 = "en_core_web_lg"
ISO_639 = "eng"
ENGLISH_NAME = "English"
@ -25,6 +29,12 @@ class ENG_MD:
ENGLISH_NAME = "English"
class ENG_SM:
ISO_639_1 = "en_core_web_sm"
ISO_639 = "eng"
ENGLISH_NAME = "English"
class Chatter(Cog):
"""
This cog trains a chatbot that will talk like members of your Guild
@ -39,7 +49,13 @@ class Chatter(Cog):
path: pathlib.Path = cog_data_path(self)
self.data_path = path / "database.sqlite3"
self.chatbot = self._create_chatbot(self.data_path, SpacySimilarity, 0.45, ENG_MD)
# TODO: Move training_model and similarity_algo to config
# TODO: Add an option to see current settings
self.tagger_language = ENG_MD
self.similarity_algo = SpacySimilarity
self.similarity_threshold = 0.90
self.chatbot = self._create_chatbot()
# self.chatbot.set_trainer(ListTrainer)
# self.trainer = ListTrainer(self.chatbot)
@ -49,18 +65,18 @@ class Chatter(Cog):
self.loop = asyncio.get_event_loop()
def _create_chatbot(
self, data_path, similarity_algorithm, similarity_threshold, tagger_language
):
def _create_chatbot(self):
return ChatBot(
"ChatterBot",
storage_adapter="chatterbot.storage.SQLStorageAdapter",
database_uri="sqlite:///" + str(data_path),
statement_comparison_function=similarity_algorithm,
database_uri="sqlite:///" + str(self.data_path),
statement_comparison_function=self.similarity_algo,
response_selection_method=get_random_response,
logic_adapters=["chatterbot.logic.BestMatch"],
# maximum_similarity_threshold=similarity_threshold,
tagger_language=tagger_language,
maximum_similarity_threshold=self.similarity_threshold,
tagger_language=self.tagger_language,
logger=log,
)
async def _get_conversation(self, ctx, in_channel: discord.TextChannel = None):
@ -130,6 +146,11 @@ class Chatter(Cog):
return out
def _train_ubuntu(self):
trainer = UbuntuCorpusTrainer(self.chatbot)
trainer.train()
return True
def _train_english(self):
trainer = ChatterBotCorpusTrainer(self.chatbot)
# try:
@ -182,14 +203,18 @@ class Chatter(Cog):
try:
os.remove(self.data_path)
except PermissionError:
await ctx.maybe_send_embed("Failed to clear training database. Please wait a bit and try again")
await ctx.maybe_send_embed(
"Failed to clear training database. Please wait a bit and try again"
)
self._create_chatbot(self.data_path, SpacySimilarity, 0.45, ENG_MD)
self._create_chatbot()
await ctx.tick()
@chatter.command(name="algorithm")
async def chatter_algorithm(self, ctx: commands.Context, algo_number: int):
@chatter.command(name="algorithm", aliases=["algo"])
async def chatter_algorithm(
self, ctx: commands.Context, algo_number: int, threshold: float = None
):
"""
Switch the active logic algorithm to one of the three. Default after reload is Spacy
@ -198,18 +223,62 @@ class Chatter(Cog):
2: Levenshtein
"""
algos = [(SpacySimilarity, 0.45), (JaccardSimilarity, 0.75), (LevenshteinDistance, 0.75)]
algos = [SpacySimilarity, JaccardSimilarity, LevenshteinDistance]
if algo_number < 0 or algo_number > 2:
await ctx.send_help()
return
self.chatbot = self._create_chatbot(
self.data_path, algos[algo_number][0], algos[algo_number][1], ENG_MD
if threshold is not None:
if threshold >= 1 or threshold <= 0:
await ctx.maybe_send_embed(
"Threshold must be a number between 0 and 1 (exclusive)"
)
return
else:
self.similarity_algo = threshold
self.similarity_algo = algos[algo_number]
async with ctx.typing():
self.chatbot = self._create_chatbot()
await ctx.tick()
@chatter.command(name="model")
async def chatter_model(self, ctx: commands.Context, model_number: int):
"""
Switch the active model to one of the three. Default after reload is Medium
0: Small
1: Medium
2: Large (Requires additional setup)
"""
models = [ENG_SM, ENG_MD, ENG_LG]
if model_number < 0 or model_number > 2:
await ctx.send_help()
return
if model_number == 2:
await ctx.maybe_send_embed(
"Additional requirements needed. See guide before continuing.\n" "Continue?"
)
pred = MessagePredicate.yes_or_no(ctx)
try:
await self.bot.wait_for("message", check=pred, timeout=30)
except TimeoutError:
await ctx.send("Response timed out, please try again later.")
return
if not pred.result:
return
self.tagger_language = models[model_number]
async with ctx.typing():
self.chatbot = self._create_chatbot()
await ctx.maybe_send_embed(f"Model has been switched to {self.tagger_language.ISO_639_1}")
@chatter.command(name="minutes")
async def minutes(self, ctx: commands.Context, minutes: int):
"""
@ -260,6 +329,27 @@ class Chatter(Cog):
else:
await ctx.send("Error occurred :(")
@chatter.command(name="trainubuntu")
async def chatter_train_ubuntu(self, ctx: commands.Context, confirmation: bool = False):
"""
WARNING: Large Download! Trains the bot using Ubuntu Dialog Corpus data.
"""
if not confirmation:
await ctx.maybe_send_embed(
"Warning: This command downloads ~500MB then eats your CPU for training\n"
"If you're sure you want to continue, run `[p]chatter trainubuntu True`"
)
return
async with ctx.typing():
future = await self.loop.run_in_executor(None, self._train_ubuntu)
if future:
await ctx.send("Training successful!")
else:
await ctx.send("Error occurred :(")
@chatter.command(name="trainenglish")
async def chatter_train_english(self, ctx: commands.Context):
"""