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Merge branch 'UI2' of https://github.com/ebolam/KoboldAI into UI2

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ebolam
2023-02-09 10:54:48 -05:00
parent b29d4cef29 709bd634b3
commit 0487b271b8
13 changed files with 331 additions and 255 deletions
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133
aiserver.py
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@@ -563,6 +563,7 @@ from flask_socketio import SocketIO, emit, join_room, leave_room
from flask_socketio import emit as _emit
from flask_session import Session
from flask_compress import Compress
from flask_cors import CORS
from werkzeug.exceptions import HTTPException, NotFound, InternalServerError
import secrets
app = Flask(__name__, root_path=os.getcwd())
@@ -1782,15 +1783,15 @@ def get_layer_count(model, directory=""):
model = directory
from transformers import AutoConfig
if(os.path.isdir(model.replace('/', '_'))):
model_config = AutoConfig.from_pretrained(model.replace('/', '_'), revision=koboldai_vars.revision, cache_dir="cache")
model_config = AutoConfig.from_pretrained(model.replace('/', '_'), revision=args.revision, cache_dir="cache")
elif(is_model_downloaded(model)):
model_config = AutoConfig.from_pretrained("models/{}".format(model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
model_config = AutoConfig.from_pretrained("models/{}".format(model.replace('/', '_')), revision=args.revision, cache_dir="cache")
elif(os.path.isdir(directory)):
model_config = AutoConfig.from_pretrained(directory, revision=koboldai_vars.revision, cache_dir="cache")
model_config = AutoConfig.from_pretrained(directory, revision=args.revision, cache_dir="cache")
elif(os.path.isdir(koboldai_vars.custmodpth.replace('/', '_'))):
model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth.replace('/', '_'), revision=koboldai_vars.revision, cache_dir="cache")
model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth.replace('/', '_'), revision=args.revision, cache_dir="cache")
else:
model_config = AutoConfig.from_pretrained(model, revision=koboldai_vars.revision, cache_dir="cache")
model_config = AutoConfig.from_pretrained(model, revision=args.revision, cache_dir="cache")
try:
if ((utils.HAS_ACCELERATE and model_config.model_type != 'gpt2') or model_config.model_type in ("gpt_neo", "gptj", "xglm", "opt")) and not koboldai_vars.nobreakmodel:
return utils.num_layers(model_config)
@@ -2507,15 +2508,12 @@ def patch_transformers():
if input_ids[i][-2] == null_character:
input_ids[i][-1] = null_character
elif data[i][-1*(len(koboldai_vars.chatname)+1):] == koboldai_vars.chatname + ":":
#We now have the user name in chat mode with a :. We want to remove that from the data
#We do need to check if the first token includes more than the chatname (Ie " You") so we don't loose the extra data
chatname_tokens = len(tokenizer.encode(koboldai_vars.chatname+":"))
if input_ids[i][-1*chatname_tokens] != koboldai_vars.chatname[0]:
input_ids[i][-1*chatname_tokens] = tokenizer.encode(tokenizer.decode(input_ids[i][-1*chatname_tokens]))[0]
else:
input_ids[i][-1*chatname_tokens] = null_character
for j in range(len(koboldai_vars.chatname)+1):
input_ids[i][-j] = tokenizer.encode(chr(0))[0]
#We'll decode the whole AI text, strip off the username, then re-encode it and pad it with nulls
temp = data[i][:-1*(len(koboldai_vars.chatname)+1)]
temp = tokenizer.encode(temp)
temp = temp + tokenizer.encode(chr(0))*(len(input_ids[i])-len(temp))
for j in range(len(input_ids[i])):
input_ids[i][j] = temp[j]
self.completed[i] = True
if all(self.completed):
koboldai_vars.generated_tkns = koboldai_vars.genamt
@@ -2774,19 +2772,19 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
from transformers import AutoConfig
if(os.path.isdir(koboldai_vars.custmodpth.replace('/', '_'))):
try:
model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth.replace('/', '_'), revision=koboldai_vars.revision, cache_dir="cache")
model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth.replace('/', '_'), revision=args.revision, cache_dir="cache")
koboldai_vars.model_type = model_config.model_type
except ValueError as e:
koboldai_vars.model_type = "not_found"
elif(os.path.isdir("models/{}".format(koboldai_vars.custmodpth.replace('/', '_')))):
try:
model_config = AutoConfig.from_pretrained("models/{}".format(koboldai_vars.custmodpth.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
model_config = AutoConfig.from_pretrained("models/{}".format(koboldai_vars.custmodpth.replace('/', '_')), revision=args.revision, cache_dir="cache")
koboldai_vars.model_type = model_config.model_type
except ValueError as e:
koboldai_vars.model_type = "not_found"
else:
try:
model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
model_config = AutoConfig.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
koboldai_vars.model_type = model_config.model_type
except ValueError as e:
koboldai_vars.model_type = "not_found"
@@ -2871,7 +2869,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
else:
tokenizer_id = {
"Colab": "EleutherAI/gpt-neo-2.7B",
"CLUSTER": koboldai_vars.cluster_requested_models[0],
"CLUSTER": koboldai_vars.cluster_requested_models[0] if len(koboldai_vars.cluster_requested_models) > 0 else "gpt2",
"OAI": "gpt2",
}[koboldai_vars.model]
@@ -2886,7 +2884,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
print(tokenizer_id, koboldai_vars.newlinemode)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=args.revision, cache_dir="cache")
loadsettings()
koboldai_vars.colaburl = url or koboldai_vars.colaburl
@@ -3071,19 +3069,19 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
with(maybe_use_float16()):
try:
if os.path.exists(koboldai_vars.custmodpth):
model = GPT2LMHeadModel.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
model = GPT2LMHeadModel.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
elif os.path.exists(os.path.join("models/", koboldai_vars.custmodpth)):
model = GPT2LMHeadModel.from_pretrained(os.path.join("models/", koboldai_vars.custmodpth), revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained(os.path.join("models/", koboldai_vars.custmodpth), revision=koboldai_vars.revision, cache_dir="cache")
model = GPT2LMHeadModel.from_pretrained(os.path.join("models/", koboldai_vars.custmodpth), revision=args.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained(os.path.join("models/", koboldai_vars.custmodpth), revision=args.revision, cache_dir="cache")
else:
model = GPT2LMHeadModel.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
model = GPT2LMHeadModel.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
except Exception as e:
if("out of memory" in traceback.format_exc().lower()):
raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
raise e
tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
model.save_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), max_shard_size="500MiB")
tokenizer.save_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')))
koboldai_vars.modeldim = get_hidden_size_from_model(model)
@@ -3130,38 +3128,38 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
lowmem = {}
if(os.path.isdir(koboldai_vars.custmodpth)):
try:
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
except Exception as e:
try:
tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache")
try:
model = AutoModelForCausalLM.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache", **lowmem)
model = AutoModelForCausalLM.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache", **lowmem)
except Exception as e:
if("out of memory" in traceback.format_exc().lower()):
raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
model = GPTNeoForCausalLM.from_pretrained(koboldai_vars.custmodpth, revision=koboldai_vars.revision, cache_dir="cache", **lowmem)
model = GPTNeoForCausalLM.from_pretrained(koboldai_vars.custmodpth, revision=args.revision, cache_dir="cache", **lowmem)
elif(os.path.isdir("models/{}".format(koboldai_vars.model.replace('/', '_')))):
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache")
except Exception as e:
try:
tokenizer = GPT2Tokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache")
try:
model = AutoModelForCausalLM.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache", **lowmem)
model = AutoModelForCausalLM.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache", **lowmem)
except Exception as e:
if("out of memory" in traceback.format_exc().lower()):
raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
model = GPTNeoForCausalLM.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache", **lowmem)
model = GPTNeoForCausalLM.from_pretrained("models/{}".format(koboldai_vars.model.replace('/', '_')), revision=args.revision, cache_dir="cache", **lowmem)
else:
old_rebuild_tensor = torch._utils._rebuild_tensor
def new_rebuild_tensor(storage: Union[torch_lazy_loader.LazyTensor, torch.Storage], storage_offset, shape, stride):
@@ -3177,21 +3175,21 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
torch._utils._rebuild_tensor = new_rebuild_tensor
try:
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.model, revision=args.revision, cache_dir="cache", use_fast=False)
except Exception as e:
try:
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = AutoTokenizer.from_pretrained(koboldai_vars.model, revision=args.revision, cache_dir="cache")
except Exception as e:
try:
tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained(koboldai_vars.model, revision=args.revision, cache_dir="cache")
except Exception as e:
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache")
try:
model = AutoModelForCausalLM.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache", **lowmem)
model = AutoModelForCausalLM.from_pretrained(koboldai_vars.model, revision=args.revision, cache_dir="cache", **lowmem)
except Exception as e:
if("out of memory" in traceback.format_exc().lower()):
raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
model = GPTNeoForCausalLM.from_pretrained(koboldai_vars.model, revision=koboldai_vars.revision, cache_dir="cache", **lowmem)
model = GPTNeoForCausalLM.from_pretrained(koboldai_vars.model, revision=args.revision, cache_dir="cache", **lowmem)
torch._utils._rebuild_tensor = old_rebuild_tensor
@@ -3208,13 +3206,13 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
import huggingface_hub
legacy = packaging.version.parse(transformers_version) < packaging.version.parse("4.22.0.dev0")
# Save the config.json
shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, transformers.configuration_utils.CONFIG_NAME, revision=koboldai_vars.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), transformers.configuration_utils.CONFIG_NAME))
shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, transformers.configuration_utils.CONFIG_NAME, revision=args.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), transformers.configuration_utils.CONFIG_NAME))
if(utils.num_shards is None):
# Save the pytorch_model.bin of an unsharded model
try:
shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, transformers.modeling_utils.WEIGHTS_NAME, revision=koboldai_vars.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), transformers.modeling_utils.WEIGHTS_NAME))
shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, transformers.modeling_utils.WEIGHTS_NAME, revision=args.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), transformers.modeling_utils.WEIGHTS_NAME))
except:
shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, "model.safetensors", revision=koboldai_vars.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), "model.safetensors"))
shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, "model.safetensors", revision=args.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), "model.safetensors"))
else:
with open(utils.from_pretrained_index_filename) as f:
map_data = json.load(f)
@@ -3223,7 +3221,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
shutil.move(os.path.realpath(utils.from_pretrained_index_filename), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), transformers.modeling_utils.WEIGHTS_INDEX_NAME))
# Then save the pytorch_model-#####-of-#####.bin files
for filename in filenames:
shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, filename, revision=koboldai_vars.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), filename))
shutil.move(os.path.realpath(huggingface_hub.hf_hub_download(koboldai_vars.model, filename, revision=args.revision, cache_dir="cache", local_files_only=True, legacy_cache_layout=legacy)), os.path.join("models/{}".format(koboldai_vars.model.replace('/', '_')), filename))
shutil.rmtree("cache/")
if(koboldai_vars.badwordsids is koboldai_settings.badwordsids_default and koboldai_vars.model_type not in ("gpt2", "gpt_neo", "gptj")):
@@ -3269,7 +3267,7 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
else:
from transformers import GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache")
else:
from transformers import PreTrainedModel
from transformers import modeling_utils
@@ -3682,7 +3680,7 @@ def lua_decode(tokens):
if("tokenizer" not in globals()):
from transformers import GPT2Tokenizer
global tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache")
return utils.decodenewlines(tokenizer.decode(tokens))
#==================================================================#
@@ -3694,7 +3692,7 @@ def lua_encode(string):
if("tokenizer" not in globals()):
from transformers import GPT2Tokenizer
global tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache")
return tokenizer.encode(utils.encodenewlines(string), max_length=int(4e9), truncation=True)
#==================================================================#
@@ -4852,19 +4850,19 @@ def actionsubmit(data, actionmode=0, force_submit=False, force_prompt_gen=False,
try:
if(os.path.isdir(tokenizer_id)):
try:
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=args.revision, cache_dir="cache")
except:
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=args.revision, cache_dir="cache", use_fast=False)
elif(os.path.isdir("models/{}".format(tokenizer_id.replace('/', '_')))):
try:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(tokenizer_id.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(tokenizer_id.replace('/', '_')), revision=args.revision, cache_dir="cache")
except:
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(tokenizer_id.replace('/', '_')), revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
tokenizer = AutoTokenizer.from_pretrained("models/{}".format(tokenizer_id.replace('/', '_')), revision=args.revision, cache_dir="cache", use_fast=False)
else:
try:
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=args.revision, cache_dir="cache")
except:
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=koboldai_vars.revision, cache_dir="cache", use_fast=False)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id, revision=args.revision, cache_dir="cache", use_fast=False)
except:
logger.warning(f"Unknown tokenizer {repr(tokenizer_id)}")
koboldai_vars.api_tokenizer_id = tokenizer_id
@@ -5239,7 +5237,7 @@ def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions, submission=None,
if("tokenizer" not in globals()):
from transformers import GPT2Tokenizer
global tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=koboldai_vars.revision, cache_dir="cache")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=args.revision, cache_dir="cache")
lnheader = len(tokenizer._koboldai_header)
@@ -6491,6 +6489,16 @@ def applyoutputformatting(txt, no_sentence_trimming=False, no_single_line=False)
if len(txt) == 0:
return txt
# Handle <|endoftext|> for models that want this
# In the future it would be nice if we could extend this to all EOS models.
# However, since EOS detection may have unforseen consequences for now we hardcode <|endoftext|> until more can be tested
# - Henk
eotregex = re.compile(r'<\|endoftext\|>[.|\n|\W|\w]*')
txt = eotregex.sub('', txt)
# Cleanup stray </s>
txt = txt.replace("</s>", "")
# Use standard quotes and apostrophes
txt = utils.fixquotes(txt)
@@ -8668,6 +8676,8 @@ def UI_2_Pinning(data):
@socketio.on('back')
@logger.catch
def UI_2_back(data):
if koboldai_vars.aibusy:
return
if koboldai_vars.debug:
print("back")
koboldai_vars.actions.clear_unused_options()
@@ -8679,6 +8689,8 @@ def UI_2_back(data):
@socketio.on('redo')
@logger.catch
def UI_2_redo(data):
if koboldai_vars.aibusy:
return
if len(koboldai_vars.actions.get_current_options()) == 1:
koboldai_vars.actions.use_option(0)
@@ -8689,7 +8701,8 @@ def UI_2_redo(data):
@socketio.on('retry')
@logger.catch
def UI_2_retry(data):
if koboldai_vars.aibusy:
return
if len(koboldai_vars.actions.get_current_options_no_edits()) == 0:
ignore = koboldai_vars.actions.pop(keep=True)
koboldai_vars.actions.clear_unused_options()
@@ -13254,6 +13267,8 @@ def run():
general_startup()
# Start flask & SocketIO
logger.init("Flask", status="Starting")
if koboldai_vars.host:
CORS(app)
Session(app)
logger.init_ok("Flask", status="OK")
logger.init("Webserver", status="Starting")