ExternalVendorCode/lollms-webui
1
0
Fork 0
mirror of https://github.com/ParisNeo/lollms-webui.git synced 2024-12-18 20:17:50 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity
75d8fbd3c3
lollms-webui/api/__init__.py
Saifeddine ALOUI 75d8fbd3c3 upgraded ui
2024-01-14 01:05:54 +01:00

2221 lines
115 KiB
Python
Raw Blame History

######
# Project : lollms-webui
# File : api/__init__.py
# Author : ParisNeo with the help of the community
# license : Apache 2.0
# Description :
# A simple api to communicate with lollms-webui and its models.
######
from flask import request
from datetime import datetime
from api.db import DiscussionsDB, Discussion
from pathlib import Path
from lollms.config import InstallOption
from lollms.types import MSG_TYPE, SENDER_TYPES
from lollms.extension import LOLLMSExtension, ExtensionBuilder
from lollms.personality import AIPersonality, PersonalityBuilder
from lollms.binding import LOLLMSConfig, BindingBuilder, LLMBinding, ModelBuilder, BindingType
from lollms.paths import LollmsPaths
from lollms.helpers import ASCIIColors, trace_exception
from lollms.com import NotificationType, NotificationDisplayType, LoLLMsCom
from lollms.app import LollmsApplication
from lollms.utilities import File64BitsManager, PromptReshaper, PackageManager, find_first_available_file_index, terminate_thread
try:
from lollms.media import WebcamImageSender, AudioRecorder
Media_on=True
except:
ASCIIColors.warning("Couldn't load media library.\nYou will not be able to perform any of the media linked operations. please verify the logs and install any required installations")
Media_on=False
from safe_store import TextVectorizer, VectorizationMethod, VisualizationMethod
import threading
from tqdm import tqdm
import traceback
import sys
import gc
import ctypes
from functools import partial
import json
import shutil
import re
import string
import requests
from datetime import datetime
from typing import List, Tuple
import time
import numpy as np
from lollms.utilities import find_first_available_file_index, convert_language_name
if not PackageManager.check_package_installed("requests"):
PackageManager.install_package("requests")
if not PackageManager.check_package_installed("bs4"):
PackageManager.install_package("beautifulsoup4")
import requests
from flask_socketio import SocketIO
from bs4 import BeautifulSoup
__author__ = "parisneo"
__github__ = "https://github.com/ParisNeo/lollms-webui"
__copyright__ = "Copyright 2023, "
__license__ = "Apache 2.0"
import subprocess
import pkg_resources
# ===========================================================
# Manage automatic install scripts
def is_package_installed(package_name):
try:
dist = pkg_resources.get_distribution(package_name)
return True
except pkg_resources.DistributionNotFound:
return False
def install_package(package_name):
try:
# Check if the package is already installed
__import__(package_name)
print(f"{package_name} is already installed.")
except ImportError:
print(f"{package_name} is not installed. Installing...")
# Install the package using pip
subprocess.check_call(["pip", "install", package_name])
print(f"{package_name} has been successfully installed.")
def parse_requirements_file(requirements_path):
with open(requirements_path, 'r') as f:
for line in f:
line = line.strip()
if not line or line.startswith('#'):
# Skip empty and commented lines
continue
package_name, _, version_specifier = line.partition('==')
package_name, _, version_specifier = line.partition('>=')
if is_package_installed(package_name):
# The package is already installed
print(f"{package_name} is already installed.")
else:
# The package is not installed, install it
if version_specifier:
install_package(f"{package_name}{version_specifier}")
else:
install_package(package_name)
# ===========================================================
class LoLLMsAPI(LollmsApplication):
def __init__(self, config:LOLLMSConfig, socketio:SocketIO, config_file_path:str, lollms_paths: LollmsPaths) -> None:
self.socketio = socketio
super().__init__("Lollms_webui",config, lollms_paths, callback=self.process_chunk, socketio=socketio)
self.busy = False
self.nb_received_tokens = 0
self.config_file_path = config_file_path
self.cancel_gen = False
# Keeping track of current discussion and message
self._current_user_message_id = 0
self._current_ai_message_id = 0
self._message_id = 0
self.db_path = config["db_path"]
if Path(self.db_path).is_absolute():
# Create database object
self.db = DiscussionsDB(self.db_path)
else:
# Create database object
self.db = DiscussionsDB(self.lollms_paths.personal_databases_path/self.db_path)
# If the database is empty, populate it with tables
ASCIIColors.info("Checking discussions database... ",end="")
self.db.create_tables()
self.db.add_missing_columns()
ASCIIColors.success("ok")
# prepare vectorization
if self.config.data_vectorization_activate and self.config.use_discussions_history:
try:
ASCIIColors.yellow("Loading long term memory")
folder = self.lollms_paths.personal_databases_path/"vectorized_dbs"
folder.mkdir(parents=True, exist_ok=True)
self.build_long_term_skills_memory()
ASCIIColors.yellow("Ready")
except Exception as ex:
trace_exception(ex)
self.long_term_memory = None
else:
self.long_term_memory = None
# This is used to keep track of messages
self.download_infos={}
self.connections = {
0:{
"current_discussion":None,
"generated_text":"",
"cancel_generation": False,
"generation_thread": None,
"processing":False,
"schedule_for_deletion":False,
"continuing": False,
"first_chunk": True,
}
}
if Media_on:
try:
self.webcam = WebcamImageSender(socketio,lollmsCom=self)
except:
self.webcam = None
try:
self.rec_output_folder = lollms_paths.personal_outputs_path/"audio_rec"
self.rec_output_folder.mkdir(exist_ok=True, parents=True)
self.summoned = False
self.audio_cap = AudioRecorder(socketio,self.rec_output_folder/"rt.wav", callback=self.audio_callback,lollmsCom=self)
except:
self.audio_cap = None
self.rec_output_folder = None
else:
self.webcam = None
self.rec_output_folder = None
# =========================================================================================
# Socket IO stuff
# =========================================================================================
@socketio.on('connect')
def connect():
#Create a new connection information
self.connections[request.sid] = {
"current_discussion":self.db.load_last_discussion(),
"generated_text":"",
"continuing": False,
"first_chunk": True,
"cancel_generation": False,
"generation_thread": None,
"processing":False,
"schedule_for_deletion":False
}
self.socketio.emit('connected', room=request.sid)
ASCIIColors.success(f'Client {request.sid} connected')
@socketio.on('disconnect')
def disconnect():
try:
self.socketio.emit('disconnected', room=request.sid)
if self.connections[request.sid]["processing"]:
self.connections[request.sid]["schedule_for_deletion"]=True
else:
del self.connections[request.sid]
except Exception as ex:
pass
ASCIIColors.error(f'Client {request.sid} disconnected')
# ---- chatbox -----
@socketio.on('add_webpage')
def add_webpage(data):
ASCIIColors.yellow("Scaping web page")
url = data['url']
index = find_first_available_file_index(self.lollms_paths.personal_uploads_path,"web_",".txt")
file_path=self.lollms_paths.personal_uploads_path/f"web_{index}.txt"
self.scrape_and_save(url=url, file_path=file_path)
try:
if not self.personality.processor is None:
self.personality.processor.add_file(file_path, partial(self.process_chunk, client_id = request.sid))
# File saved successfully
socketio.emit('web_page_added', {'status':True,})
else:
self.personality.add_file(file_path, partial(self.process_chunk, client_id = request.sid))
# File saved successfully
socketio.emit('web_page_added', {'status':True})
except Exception as e:
# Error occurred while saving the file
socketio.emit('web_page_added', {'status':False})
@socketio.on('take_picture')
def take_picture():
try:
self.info("Loading camera")
if not PackageManager.check_package_installed("cv2"):
PackageManager.install_package("opencv-python")
import cv2
cap = cv2.VideoCapture(0)
n = time.time()
self.info("Stand by for taking a shot in 2s")
while(time.time()-n<2):
_, frame = cap.read()
_, frame = cap.read()
cap.release()
self.info("Shot taken")
cam_shot_path = self.lollms_paths.personal_uploads_path/"camera_shots"
cam_shot_path.mkdir(parents=True, exist_ok=True)
filename = find_first_available_file_index(cam_shot_path, "cam_shot_", extension=".png")
save_path = cam_shot_path/f"cam_shot_{filename}.png" # Specify the desired folder path
try:
cv2.imwrite(str(save_path), frame)
if not self.personality.processor is None:
self.info("Sending file to scripted persona")
self.personality.processor.add_file(save_path, partial(self.process_chunk, client_id = request.sid))
# File saved successfully
socketio.emit('picture_taken', {'status':True, 'progress': 100})
self.info("File sent to scripted persona")
else:
self.info("Sending file to persona")
self.personality.add_file(save_path, partial(self.process_chunk, client_id = request.sid))
# File saved successfully
socketio.emit('picture_taken', {'status':True, 'progress': 100})
self.info("File sent to persona")
except Exception as e:
trace_exception(e)
# Error occurred while saving the file
socketio.emit('picture_taken', {'status':False, 'error': str(e)})
except Exception as ex:
trace_exception(ex)
self.error("Couldn't use the webcam")
@socketio.on('create_empty_message')
def create_empty_message(data):
client_id = request.sid
type = data.get("type",0)
message = data.get("message","")
if type==0:
ASCIIColors.info(f"Building empty User message requested by : {client_id}")
# send the message to the bot
print(f"Creating an empty message for AI answer orientation")
if self.connections[client_id]["current_discussion"]:
if not self.model:
self.error("No model selected. Please make sure you select a model before starting generation", client_id = client_id)
return
self.new_message(client_id, self.config.user_name, message, sender_type=SENDER_TYPES.SENDER_TYPES_USER, open=True)
self.socketio.sleep(0.01)
else:
if self.personality is None:
self.warning("Select a personality")
return
ASCIIColors.info(f"Building empty AI message requested by : {client_id}")
# send the message to the bot
print(f"Creating an empty message for AI answer orientation")
if self.connections[client_id]["current_discussion"]:
if not self.model:
self.error("No model selected. Please make sure you select a model before starting generation", client_id=client_id)
return
self.new_message(client_id, self.personality.name, "[edit this to put your ai answer start]", open=True)
self.socketio.sleep(0.01)
# -- interactive view --
@socketio.on('start_webcam_video_stream')
def start_webcam_video_stream():
self.info("Starting video capture")
self.webcam.start_capture()
@socketio.on('stop_webcam_video_stream')
def stop_webcam_video_stream():
self.info("Stopping video capture")
self.webcam.stop_capture()
@socketio.on('start_audio_stream')
def start_audio_stream():
self.info("Starting audio capture")
self.audio_cap.start_recording()
@socketio.on('stop_audio_stream')
def stop_audio_stream():
self.info("Stopping audio capture")
self.audio_cap.stop_recording()
# -- vectorization --
@socketio.on('upgrade_vectorization')
def upgrade_vectorization():
if self.config.data_vectorization_activate and self.config.use_discussions_history:
try:
self.socketio.emit('show_progress')
self.socketio.sleep(0)
ASCIIColors.yellow("0- Detected discussion vectorization request")
folder = self.lollms_paths.personal_databases_path/"vectorized_dbs"
folder.mkdir(parents=True, exist_ok=True)
self.build_long_term_skills_memory()
ASCIIColors.yellow("1- Exporting discussions")
discussions = self.db.export_all_as_markdown_list_for_vectorization()
ASCIIColors.yellow("2- Adding discussions to vectorizer")
index = 0
nb_discussions = len(discussions)
for (title,discussion) in tqdm(discussions):
self.socketio.emit('update_progress',{'value':int(100*(index/nb_discussions))})
self.socketio.sleep(0)
index += 1
if discussion!='':
skill = self.learn_from_discussion(title, discussion)
self.long_term_memory.add_document(title, skill, chunk_size=self.config.data_vectorization_chunk_size, overlap_size=self.config.data_vectorization_overlap_size, force_vectorize=False, add_as_a_bloc=False)
ASCIIColors.yellow("3- Indexing database")
self.long_term_memory.index()
ASCIIColors.yellow("4- Saving database")
self.long_term_memory.save_to_json()
if self.config.data_vectorization_visualize_on_vectorization:
self.long_term_memory.show_document(show_interactive_form=True)
ASCIIColors.yellow("Ready")
except Exception as ex:
ASCIIColors.error(f"Couldn't vectorize database:{ex}")
self.socketio.emit('hide_progress')
self.socketio.sleep(0)
# -- model --
@socketio.on('cancel_install')
def cancel_install(data):
try:
model_name = data["model_name"]
binding_folder = data["binding_folder"]
model_url = data["model_url"]
signature = f"{model_name}_{binding_folder}_{model_url}"
self.download_infos[signature]["cancel"]=True
self.socketio.emit('canceled', {
'status': True
},
room=request.sid
)
except Exception as ex:
trace_exception(ex)
self.socketio.emit('canceled', {
'status': False,
'error':str(ex)
},
room=request.sid
)
@socketio.on('install_model')
def install_model(data):
room_id = request.sid
def install_model_():
print("Install model triggered")
model_path = data["path"].replace("\\","/")
if data["type"].lower() in model_path.lower():
model_type:str=data["type"]
else:
mtt = None
for mt in self.binding.models_dir_names:
if mt.lower() in model_path.lower():
mtt = mt
break
if mtt:
model_type = mtt
else:
model_type:str=self.binding.models_dir_names[0]
progress = 0
installation_dir = self.binding.searchModelParentFolder(model_path.split('/')[-1], model_type)
if model_type=="gptq" or model_type=="awq" or model_type=="transformers":
parts = model_path.split("/")
if len(parts)==2:
filename = parts[1]
else:
filename = parts[4]
installation_path = installation_dir / filename
elif model_type=="gpt4all":
filename = data["variant_name"]
model_path = "http://gpt4all.io/models/gguf/"+filename
installation_path = installation_dir / filename
else:
filename = Path(model_path).name
installation_path = installation_dir / filename
print("Model install requested")
print(f"Model path : {model_path}")
model_name = filename
binding_folder = self.config["binding_name"]
model_url = model_path
signature = f"{model_name}_{binding_folder}_{model_url}"
try:
self.download_infos[signature]={
"start_time":datetime.now(),
"total_size":self.binding.get_file_size(model_path),
"downloaded_size":0,
"progress":0,
"speed":0,
"cancel":False
}
if installation_path.exists():
print("Error: Model already exists. please remove it first")
socketio.emit('install_progress',{
'status': False,
'error': f'model already exists. Please remove it first.\nThe model can be found here:{installation_path}',
'model_name' : model_name,
'binding_folder' : binding_folder,
'model_url' : model_url,
'start_time': self.download_infos[signature]['start_time'].strftime("%Y-%m-%d %H:%M:%S"),
'total_size': self.download_infos[signature]['total_size'],
'downloaded_size': self.download_infos[signature]['downloaded_size'],
'progress': self.download_infos[signature]['progress'],
'speed': self.download_infos[signature]['speed'],
}, room=room_id
)
return
socketio.emit('install_progress',{
'status': True,
'progress': progress,
'model_name' : model_name,
'binding_folder' : binding_folder,
'model_url' : model_url,
'start_time': self.download_infos[signature]['start_time'].strftime("%Y-%m-%d %H:%M:%S"),
'total_size': self.download_infos[signature]['total_size'],
'downloaded_size': self.download_infos[signature]['downloaded_size'],
'progress': self.download_infos[signature]['progress'],
'speed': self.download_infos[signature]['speed'],
}, room=room_id)
def callback(downloaded_size, total_size):
progress = (downloaded_size / total_size) * 100
now = datetime.now()
dt = (now - self.download_infos[signature]['start_time']).total_seconds()
speed = downloaded_size/dt
self.download_infos[signature]['downloaded_size'] = downloaded_size
self.download_infos[signature]['speed'] = speed
if progress - self.download_infos[signature]['progress']>2:
self.download_infos[signature]['progress'] = progress
socketio.emit('install_progress',{
'status': True,
'model_name' : model_name,
'binding_folder' : binding_folder,
'model_url' : model_url,
'start_time': self.download_infos[signature]['start_time'].strftime("%Y-%m-%d %H:%M:%S"),
'total_size': self.download_infos[signature]['total_size'],
'downloaded_size': self.download_infos[signature]['downloaded_size'],
'progress': self.download_infos[signature]['progress'],
'speed': self.download_infos[signature]['speed'],
}, room=room_id)
if self.download_infos[signature]["cancel"]:
raise Exception("canceled")
if hasattr(self.binding, "download_model"):
try:
self.binding.download_model(model_path, installation_path, callback)
except Exception as ex:
ASCIIColors.warning(str(ex))
trace_exception(ex)
socketio.emit('install_progress',{
'status': False,
'error': 'canceled',
'model_name' : model_name,
'binding_folder' : binding_folder,
'model_url' : model_url,
'start_time': self.download_infos[signature]['start_time'].strftime("%Y-%m-%d %H:%M:%S"),
'total_size': self.download_infos[signature]['total_size'],
'downloaded_size': self.download_infos[signature]['downloaded_size'],
'progress': self.download_infos[signature]['progress'],
'speed': self.download_infos[signature]['speed'],
}, room=room_id
)
del self.download_infos[signature]
try:
if installation_path.is_dir():
shutil.rmtree(installation_path)
else:
installation_path.unlink()
except Exception as ex:
trace_exception(ex)
ASCIIColors.error(f"Couldn't delete file. Please try to remove it manually.\n{installation_path}")
return
else:
try:
self.download_file(model_path, installation_path, callback)
except Exception as ex:
ASCIIColors.warning(str(ex))
trace_exception(ex)
socketio.emit('install_progress',{
'status': False,
'error': 'canceled',
'model_name' : model_name,
'binding_folder' : binding_folder,
'model_url' : model_url,
'start_time': self.download_infos[signature]['start_time'].strftime("%Y-%m-%d %H:%M:%S"),
'total_size': self.download_infos[signature]['total_size'],
'downloaded_size': self.download_infos[signature]['downloaded_size'],
'progress': self.download_infos[signature]['progress'],
'speed': self.download_infos[signature]['speed'],
}, room=room_id
)
del self.download_infos[signature]
installation_path.unlink()
return
socketio.emit('install_progress',{
'status': True,
'error': '',
'model_name' : model_name,
'binding_folder' : binding_folder,
'model_url' : model_url,
'start_time': self.download_infos[signature]['start_time'].strftime("%Y-%m-%d %H:%M:%S"),
'total_size': self.download_infos[signature]['total_size'],
'downloaded_size': self.download_infos[signature]['downloaded_size'],
'progress': 100,
'speed': self.download_infos[signature]['speed'],
}, room=room_id)
del self.download_infos[signature]
except Exception as ex:
trace_exception(ex)
socketio.emit('install_progress',{
'status': False,
'error': str(ex),
'model_name' : model_name,
'binding_folder' : binding_folder,
'model_url' : model_url,
'start_time': '',
'total_size': 0,
'downloaded_size': 0,
'progress': 0,
'speed': 0,
}, room=room_id
)
tpe = threading.Thread(target=install_model_, args=())
tpe.start()
@socketio.on('uninstall_model')
def uninstall_model(data):
model_path = data['path']
model_type:str=data.get("type","ggml")
installation_dir = self.binding.searchModelParentFolder(model_path)
binding_folder = self.config["binding_name"]
if model_type=="gptq" or model_type=="awq":
filename = model_path.split("/")[4]
installation_path = installation_dir / filename
else:
filename = Path(model_path).name
installation_path = installation_dir / filename
model_name = filename
if not installation_path.exists():
socketio.emit('uninstall_progress',{
'status': False,
'error': 'The model does not exist',
'model_name' : model_name,
'binding_folder' : binding_folder
}, room=request.sid)
try:
if not installation_path.exists():
# Try to find a version
model_path = installation_path.name.lower().replace("-ggml","").replace("-gguf","")
candidates = [m for m in installation_dir.iterdir() if model_path in m.name]
if len(candidates)>0:
model_path = candidates[0]
installation_path = model_path
if installation_path.is_dir():
shutil.rmtree(installation_path)
else:
installation_path.unlink()
socketio.emit('uninstall_progress',{
'status': True,
'error': '',
'model_name' : model_name,
'binding_folder' : binding_folder
}, room=request.sid)
except Exception as ex:
trace_exception(ex)
ASCIIColors.error(f"Couldn't delete {installation_path}, please delete it manually and restart the app")
socketio.emit('uninstall_progress',{
'status': False,
'error': f"Couldn't delete {installation_path}, please delete it manually and restart the app",
'model_name' : model_name,
'binding_folder' : binding_folder
}, room=request.sid)
# -- discussion --
@socketio.on('new_discussion')
def new_discussion(data):
ASCIIColors.yellow("New descussion requested")
client_id = request.sid
title = data["title"]
if self.connections[client_id]["current_discussion"] is not None:
if self.long_term_memory is not None:
title, content = self.connections[client_id]["current_discussion"].export_for_vectorization()
skill = self.learn_from_discussion(title, content)
self.long_term_memory.add_document(title, skill, chunk_size=self.config.data_vectorization_chunk_size, overlap_size=self.config.data_vectorization_overlap_size, force_vectorize=False, add_as_a_bloc=False, add_to_index=True)
ASCIIColors.yellow("4- Saving database")
self.long_term_memory.save_to_json()
self.connections[client_id]["current_discussion"] = self.db.create_discussion(title)
# Get the current timestamp
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
# Return a success response
if self.connections[client_id]["current_discussion"] is None:
self.connections[client_id]["current_discussion"] = self.db.load_last_discussion()
if self.personality.welcome_message!="":
if self.config.force_output_language_to_be and self.config.force_output_language_to_be.lower().strip() !="english":
welcome_message = self.personality.fast_gen(f"!@>instruction: Translate the following text to {self.config.force_output_language_to_be.lower()}:\n{self.personality.welcome_message}\n!@>translation:")
else:
welcome_message = self.personality.welcome_message
message = self.connections[client_id]["current_discussion"].add_message(
message_type = MSG_TYPE.MSG_TYPE_FULL.value if self.personality.include_welcome_message_in_disucssion else MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_AI.value,
sender_type = SENDER_TYPES.SENDER_TYPES_AI.value,
sender = self.personality.name,
content = welcome_message,
metadata = None,
rank = 0,
parent_message_id = -1,
binding = self.config.binding_name,
model = self.config.model_name,
personality = self.config.personalities[self.config.active_personality_id],
created_at=None,
finished_generating_at=None
)
self.socketio.emit('discussion_created',
{'id':self.connections[client_id]["current_discussion"].discussion_id},
room=client_id
)
else:
self.socketio.emit('discussion_created',
{'id':0},
room=client_id
)
@socketio.on('load_discussion')
def load_discussion(data):
client_id = request.sid
ASCIIColors.yellow(f"Loading discussion for client {client_id} ... ", end="")
if "id" in data:
discussion_id = data["id"]
self.connections[client_id]["current_discussion"] = Discussion(discussion_id, self.db)
else:
if self.connections[client_id]["current_discussion"] is not None:
discussion_id = self.connections[client_id]["current_discussion"].discussion_id
self.connections[client_id]["current_discussion"] = Discussion(discussion_id, self.db)
else:
self.connections[client_id]["current_discussion"] = self.db.create_discussion()
messages = self.connections[client_id]["current_discussion"].get_messages()
jsons = [m.to_json() for m in messages]
self.socketio.emit('discussion',
jsons,
room=client_id
)
ASCIIColors.green(f"ok")
# -- upload file --
@socketio.on('upload_file')
def upload_file(data):
ASCIIColors.yellow("Uploading file")
file = data['file']
filename = file.filename
save_path = self.lollms_paths.personal_uploads_path/filename # Specify the desired folder path
try:
if not self.personality.processor is None:
file.save(save_path)
self.personality.processor.add_file(save_path, partial(self.process_chunk, client_id = request.sid))
# File saved successfully
socketio.emit('progress', {'status':True, 'progress': 100})
else:
file.save(save_path)
self.personality.add_file(save_path, partial(self.process_chunk, client_id = request.sid))
# File saved successfully
socketio.emit('progress', {'status':True, 'progress': 100})
except Exception as e:
# Error occurred while saving the file
socketio.emit('progress', {'status':False, 'error': str(e)})
# -- personality --
@socketio.on('get_personality_files')
def get_personality_files(data):
client_id = request.sid
self.connections[client_id]["generated_text"] = ""
self.connections[client_id]["cancel_generation"] = False
try:
self.personality.setCallback(partial(self.process_chunk,client_id = client_id))
except Exception as ex:
trace_exception(ex)
@socketio.on('send_file_chunk')
def send_file_chunk(data):
client_id = request.sid
filename = data['filename']
chunk = data['chunk']
offset = data['offset']
is_last_chunk = data['isLastChunk']
chunk_index = data['chunkIndex']
path:Path = self.lollms_paths.personal_uploads_path / self.personality.personality_folder_name
path.mkdir(parents=True, exist_ok=True)
file_path = path / data["filename"]
# Save the chunk to the server or process it as needed
# For example:
if chunk_index==0:
with open(file_path, 'wb') as file:
file.write(chunk)
else:
with open(file_path, 'ab') as file:
file.write(chunk)
if is_last_chunk:
print('File received and saved successfully')
if self.personality.processor:
result = self.personality.processor.add_file(file_path, partial(self.process_chunk, client_id=client_id))
else:
result = self.personality.add_file(file_path, partial(self.process_chunk, client_id=client_id))
self.socketio.emit('file_received', {'status': True, 'filename': filename})
else:
# Request the next chunk from the client
self.socketio.emit('request_next_chunk', {'offset': offset + len(chunk)})
@socketio.on('execute_command')
def execute_command(data):
client_id = request.sid
command = data["command"]
parameters = data["parameters"]
if self.personality.processor is not None:
self.start_time = datetime.now()
self.personality.processor.callback = partial(self.process_chunk, client_id=client_id)
self.personality.processor.execute_command(command, parameters)
else:
self.warning("Non scripted personalities do not support commands",client_id=client_id)
self.close_message(client_id)
# -- misc --
@self.socketio.on('execute_python_code')
def execute_python_code(data):
"""Executes Python code and returns the output."""
client_id = request.sid
code = data["code"]
# Import the necessary modules.
import io
import sys
import time
# Create a Python interpreter.
interpreter = io.StringIO()
sys.stdout = interpreter
# Execute the code.
start_time = time.time()
exec(code)
end_time = time.time()
# Get the output.
output = interpreter.getvalue()
self.socketio.emit("execution_output", {"output":output,"execution_time":end_time - start_time}, room=client_id)
# -- generation --
@socketio.on('cancel_generation')
def cancel_generation():
client_id = request.sid
self.cancel_gen = True
#kill thread
ASCIIColors.error(f'Client {request.sid} requested cancelling generation')
terminate_thread(self.connections[client_id]['generation_thread'])
ASCIIColors.error(f'Client {request.sid} canceled generation')
self.busy=False
@self.socketio.on('cancel_text_generation')
def cancel_text_generation(data):
client_id = request.sid
self.connections[client_id]["requested_stop"]=True
print(f"Client {client_id} requested canceling generation")
self.socketio.emit("generation_canceled", {"message":"Generation is canceled."}, room=client_id)
self.socketio.sleep(0)
self.busy = False
# A copy of the original lollms-server generation code needed for playground
@self.socketio.on('generate_text')
def handle_generate_text(data):
client_id = request.sid
self.cancel_gen = False
ASCIIColors.info(f"Text generation requested by client: {client_id}")
if self.busy:
self.socketio.emit("busy", {"message":"I am busy. Come back later."}, room=client_id)
self.socketio.sleep(0)
ASCIIColors.warning(f"OOps request {client_id} refused!! Server busy")
return
def generate_text():
self.busy = True
try:
model = self.model
self.connections[client_id]["is_generating"]=True
self.connections[client_id]["requested_stop"]=False
prompt = data['prompt']
tokenized = model.tokenize(prompt)
personality_id = data.get('personality', -1)
n_crop = data.get('n_crop', len(tokenized))
if n_crop!=-1:
prompt = model.detokenize(tokenized[-n_crop:])
n_predicts = data["n_predicts"]
parameters = data.get("parameters",{
"temperature":self.config["temperature"],
"top_k":self.config["top_k"],
"top_p":self.config["top_p"],
"repeat_penalty":self.config["repeat_penalty"],
"repeat_last_n":self.config["repeat_last_n"],
"seed":self.config["seed"]
})
if personality_id==-1:
# Raw text generation
self.answer = {"full_text":""}
def callback(text, message_type: MSG_TYPE, metadata:dict={}):
if message_type == MSG_TYPE.MSG_TYPE_CHUNK:
ASCIIColors.success(f"generated:{len(self.answer['full_text'].split())} words", end='\r')
if text is not None:
self.answer["full_text"] = self.answer["full_text"] + text
self.socketio.emit('text_chunk', {'chunk': text, 'type':MSG_TYPE.MSG_TYPE_CHUNK.value}, room=client_id)
self.socketio.sleep(0)
if client_id in self.connections:# Client disconnected
if self.connections[client_id]["requested_stop"]:
return False
else:
return True
else:
return False
tk = model.tokenize(prompt)
n_tokens = len(tk)
fd = model.detokenize(tk[-min(self.config.ctx_size-n_predicts,n_tokens):])
try:
ASCIIColors.print("warming up", ASCIIColors.color_bright_cyan)
generated_text = model.generate(fd,
n_predict=n_predicts,
callback=callback,
temperature = parameters["temperature"],
top_k = parameters["top_k"],
top_p = parameters["top_p"],
repeat_penalty = parameters["repeat_penalty"],
repeat_last_n = parameters["repeat_last_n"],
seed = parameters["seed"],
)
ASCIIColors.success(f"\ndone")
if client_id in self.connections:
if not self.connections[client_id]["requested_stop"]:
# Emit the generated text to the client
self.socketio.emit('text_generated', {'text': generated_text}, room=client_id)
self.socketio.sleep(0)
except Exception as ex:
self.socketio.emit('generation_error', {'error': str(ex)}, room=client_id)
ASCIIColors.error(f"\ndone")
self.busy = False
else:
try:
personality: AIPersonality = self.personalities[personality_id]
ump = self.config.discussion_prompt_separator +self.config.user_name.strip() if self.config.use_user_name_in_discussions else self.personality.user_message_prefix
personality.model = model
cond_tk = personality.model.tokenize(personality.personality_conditioning)
n_cond_tk = len(cond_tk)
# Placeholder code for text generation
# Replace this with your actual text generation logic
print(f"Text generation requested by client: {client_id}")
self.answer["full_text"] = ''
full_discussion_blocks = self.connections[client_id]["full_discussion_blocks"]
if prompt != '':
if personality.processor is not None and personality.processor_cfg["process_model_input"]:
preprocessed_prompt = personality.processor.process_model_input(prompt)
else:
preprocessed_prompt = prompt
if personality.processor is not None and personality.processor_cfg["custom_workflow"]:
full_discussion_blocks.append(ump)
full_discussion_blocks.append(preprocessed_prompt)
else:
full_discussion_blocks.append(ump)
full_discussion_blocks.append(preprocessed_prompt)
full_discussion_blocks.append(personality.link_text)
full_discussion_blocks.append(personality.ai_message_prefix)
full_discussion = personality.personality_conditioning + ''.join(full_discussion_blocks)
def callback(text, message_type: MSG_TYPE, metadata:dict={}):
if message_type == MSG_TYPE.MSG_TYPE_CHUNK:
self.answer["full_text"] = self.answer["full_text"] + text
self.socketio.emit('text_chunk', {'chunk': text}, room=client_id)
self.socketio.sleep(0)
try:
if self.connections[client_id]["requested_stop"]:
return False
else:
return True
except: # If the client is disconnected then we stop talking to it
return False
tk = personality.model.tokenize(full_discussion)
n_tokens = len(tk)
fd = personality.model.detokenize(tk[-min(self.config.ctx_size-n_cond_tk-personality.model_n_predicts,n_tokens):])
if personality.processor is not None and personality.processor_cfg["custom_workflow"]:
ASCIIColors.info("processing...")
generated_text = personality.processor.run_workflow(prompt, previous_discussion_text=personality.personality_conditioning+fd, callback=callback)
else:
ASCIIColors.info("generating...")
generated_text = personality.model.generate(
personality.personality_conditioning+fd,
n_predict=personality.model_n_predicts,
callback=callback)
if personality.processor is not None and personality.processor_cfg["process_model_output"]:
generated_text = personality.processor.process_model_output(generated_text)
full_discussion_blocks.append(generated_text.strip())
ASCIIColors.success("\ndone")
# Emit the generated text to the client
self.socketio.emit('text_generated', {'text': generated_text}, room=client_id)
self.socketio.sleep(0)
except Exception as ex:
self.socketio.emit('generation_error', {'error': str(ex)}, room=client_id)
ASCIIColors.error(f"\ndone")
self.busy = False
except Exception as ex:
trace_exception(ex)
self.socketio.emit('generation_error', {'error': str(ex)}, room=client_id)
self.busy = False
# Start the text generation task in a separate thread
task = self.socketio.start_background_task(target=generate_text)
@socketio.on('generate_msg')
def generate_msg(data):
client_id = request.sid
self.cancel_gen = False
self.connections[client_id]["generated_text"]=""
self.connections[client_id]["cancel_generation"]=False
self.connections[client_id]["continuing"]=False
self.connections[client_id]["first_chunk"]=True
if not self.model:
ASCIIColors.error("Model not selected. Please select a model")
self.error("Model not selected. Please select a model", client_id=client_id)
return
if not self.busy:
if self.connections[client_id]["current_discussion"] is None:
if self.db.does_last_discussion_have_messages():
self.connections[client_id]["current_discussion"] = self.db.create_discussion()
else:
self.connections[client_id]["current_discussion"] = self.db.load_last_discussion()
prompt = data["prompt"]
ump = self.config.discussion_prompt_separator +self.config.user_name.strip() if self.config.use_user_name_in_discussions else self.personality.user_message_prefix
message = self.connections[client_id]["current_discussion"].add_message(
message_type = MSG_TYPE.MSG_TYPE_FULL.value,
sender_type = SENDER_TYPES.SENDER_TYPES_USER.value,
sender = ump.replace(self.config.discussion_prompt_separator,"").replace(":",""),
content=prompt,
metadata=None,
parent_message_id=self.message_id
)
ASCIIColors.green("Starting message generation by "+self.personality.name)
self.connections[client_id]['generation_thread'] = threading.Thread(target=self.start_message_generation, args=(message, message.id, client_id))
self.connections[client_id]['generation_thread'].start()
self.socketio.sleep(0.01)
ASCIIColors.info("Started generation task")
self.busy=True
#tpe = threading.Thread(target=self.start_message_generation, args=(message, message_id, client_id))
#tpe.start()
else:
self.error("I am busy. Come back later.", client_id=client_id)
@socketio.on('generate_msg_from')
def generate_msg_from(data):
client_id = request.sid
self.cancel_gen = False
self.connections[client_id]["continuing"]=False
self.connections[client_id]["first_chunk"]=True
if self.connections[client_id]["current_discussion"] is None:
ASCIIColors.warning("Please select a discussion")
self.error("Please select a discussion first", client_id=client_id)
return
id_ = data['id']
generation_type = data.get('msg_type',None)
if id_==-1:
message = self.connections[client_id]["current_discussion"].current_message
else:
message = self.connections[client_id]["current_discussion"].load_message(id_)
if message is None:
return
self.connections[client_id]['generation_thread'] = threading.Thread(target=self.start_message_generation, args=(message, message.id, client_id, False, generation_type))
self.connections[client_id]['generation_thread'].start()
@socketio.on('continue_generate_msg_from')
def handle_connection(data):
client_id = request.sid
self.cancel_gen = False
self.connections[client_id]["continuing"]=True
self.connections[client_id]["first_chunk"]=True
if self.connections[client_id]["current_discussion"] is None:
ASCIIColors.yellow("Please select a discussion")
self.error("Please select a discussion", client_id=client_id)
return
id_ = data['id']
if id_==-1:
message = self.connections[client_id]["current_discussion"].current_message
else:
message = self.connections[client_id]["current_discussion"].load_message(id_)
self.connections[client_id]["generated_text"]=message.content
self.connections[client_id]['generation_thread'] = threading.Thread(target=self.start_message_generation, args=(message, message.id, client_id, True))
self.connections[client_id]['generation_thread'].start()
# generation status
self.generating=False
ASCIIColors.blue(f"Your personal data is stored here :",end="")
ASCIIColors.green(f"{self.lollms_paths.personal_path}")
def audio_callback(self, text):
if self.summoned:
client_id = 0
self.cancel_gen = False
self.connections[client_id]["generated_text"]=""
self.connections[client_id]["cancel_generation"]=False
self.connections[client_id]["continuing"]=False
self.connections[client_id]["first_chunk"]=True
if not self.model:
ASCIIColors.error("Model not selected. Please select a model")
self.error("Model not selected. Please select a model", client_id=client_id)
return
if not self.busy:
if self.connections[client_id]["current_discussion"] is None:
if self.db.does_last_discussion_have_messages():
self.connections[client_id]["current_discussion"] = self.db.create_discussion()
else:
self.connections[client_id]["current_discussion"] = self.db.load_last_discussion()
prompt = text
ump = self.config.discussion_prompt_separator +self.config.user_name.strip() if self.config.use_user_name_in_discussions else self.personality.user_message_prefix
message = self.connections[client_id]["current_discussion"].add_message(
message_type = MSG_TYPE.MSG_TYPE_FULL.value,
sender_type = SENDER_TYPES.SENDER_TYPES_USER.value,
sender = ump.replace(self.config.discussion_prompt_separator,"").replace(":",""),
content=prompt,
metadata=None,
parent_message_id=self.message_id
)
ASCIIColors.green("Starting message generation by " + self.personality.name)
self.connections[client_id]['generation_thread'] = threading.Thread(target=self.start_message_generation, args=(message, message.id, client_id))
self.connections[client_id]['generation_thread'].start()
self.socketio.sleep(0.01)
ASCIIColors.info("Started generation task")
self.busy=True
#tpe = threading.Thread(target=self.start_message_generation, args=(message, message_id, client_id))
#tpe.start()
else:
self.error("I am busy. Come back later.", client_id=client_id)
else:
if output["text"].lower()=="lollms":
self.summoned = True
def scrape_and_save(self, url, file_path):
# Send a GET request to the URL
response = requests.get(url)
# Parse the HTML content using BeautifulSoup
soup = BeautifulSoup(response.content, 'html.parser')
# Find all the text content in the webpage
text_content = soup.get_text()
# Remove extra returns and spaces
text_content = ' '.join(text_content.split())
# Save the text content as a text file
with open(file_path, 'w', encoding="utf-8") as file:
file.write(text_content)
self.info(f"Webpage content saved to {file_path}")
def rebuild_personalities(self, reload_all=False):
if reload_all:
self.mounted_personalities=[]
loaded = self.mounted_personalities
loaded_names = [f"{p.category}/{p.personality_folder_name}:{p.selected_language}" if p.selected_language else f"{p.category}/{p.personality_folder_name}" for p in loaded]
mounted_personalities=[]
ASCIIColors.success(f" ╔══════════════════════════════════════════════════╗ ")
ASCIIColors.success(f" ║ Building mounted Personalities ║ ")
ASCIIColors.success(f" ╚══════════════════════════════════════════════════╝ ")
to_remove=[]
for i,personality in enumerate(self.config['personalities']):
if i==self.config["active_personality_id"]:
ASCIIColors.red("*", end="")
ASCIIColors.green(f" {personality}")
else:
ASCIIColors.yellow(f" {personality}")
if personality in loaded_names:
mounted_personalities.append(loaded[loaded_names.index(personality)])
else:
personality_path = f"{personality}" if not ":" in personality else f"{personality.split(':')[0]}"
try:
personality = AIPersonality(personality_path,
self.lollms_paths,
self.config,
model=self.model,
app=self,
selected_language=personality.split(":")[1] if ":" in personality else None,
run_scripts=True)
mounted_personalities.append(personality)
if self.config.enable_voice_service and self.config.auto_read and len(personality.audio_samples)>0:
try:
from lollms.services.xtts.lollms_xtts import LollmsXTTS
if self.tts is None:
self.tts = LollmsXTTS(self, voice_samples_path=Path(__file__).parent.parent/"voices")
except:
self.warning(f"Personality {personality.name} request using custom voice but couldn't load XTTS")
except Exception as ex:
ASCIIColors.error(f"Personality file not found or is corrupted ({personality_path}).\nReturned the following exception:{ex}\nPlease verify that the personality you have selected exists or select another personality. Some updates may lead to change in personality name or category, so check the personality selection in settings to be sure.")
ASCIIColors.info("Trying to force reinstall")
if self.config["debug"]:
print(ex)
try:
personality = AIPersonality(
personality_path,
self.lollms_paths,
self.config,
self.model,
app = self,
run_scripts=True,
selected_language=personality.split(":")[1] if ":" in personality else None,
installation_option=InstallOption.FORCE_INSTALL)
mounted_personalities.append(personality)
if personality.processor:
personality.processor.mounted()
except Exception as ex:
ASCIIColors.error(f"Couldn't load personality at {personality_path}")
trace_exception(ex)
ASCIIColors.info(f"Unmounting personality")
to_remove.append(i)
personality = AIPersonality(None,
self.lollms_paths,
self.config,
self.model,
app=self,
run_scripts=True,
installation_option=InstallOption.FORCE_INSTALL)
mounted_personalities.append(personality)
if personality.processor:
personality.processor.mounted()
ASCIIColors.info("Reverted to default personality")
if self.config["active_personality_id"]>=0 and self.config["active_personality_id"]<len(self.config["personalities"]):
ASCIIColors.success(f'selected model : {self.config["personalities"][self.config["active_personality_id"]]}')
else:
ASCIIColors.warning('An error was encountered while trying to mount personality')
ASCIIColors.success(f" ╔══════════════════════════════════════════════════╗ ")
ASCIIColors.success(f" ║ Done ║ ")
ASCIIColors.success(f" ╚══════════════════════════════════════════════════╝ ")
# Sort the indices in descending order to ensure correct removal
to_remove.sort(reverse=True)
# Remove elements from the list based on the indices
for index in to_remove:
if 0 <= index < len(mounted_personalities):
mounted_personalities.pop(index)
self.config["personalities"].pop(index)
ASCIIColors.info(f"removed personality {personality_path}")
if self.config["active_personality_id"]>=len(self.config["personalities"]):
self.config["active_personality_id"]=0
return mounted_personalities
def rebuild_extensions(self, reload_all=False):
if reload_all:
self.mounted_extensions=[]
loaded = self.mounted_extensions
loaded_names = [f"{p.category}/{p.extension_folder_name}" for p in loaded]
mounted_extensions=[]
ASCIIColors.success(f" ╔══════════════════════════════════════════════════╗ ")
ASCIIColors.success(f" ║ Building mounted Extensions ║ ")
ASCIIColors.success(f" ╚══════════════════════════════════════════════════╝ ")
to_remove=[]
for i,extension in enumerate(self.config['extensions']):
ASCIIColors.yellow(f" {extension}")
if extension in loaded_names:
mounted_extensions.append(loaded[loaded_names.index(extension)])
else:
extension_path = self.lollms_paths.extensions_zoo_path/f"{extension}"
try:
extension = ExtensionBuilder().build_extension(extension_path,self.lollms_paths, self)
mounted_extensions.append(extension)
except Exception as ex:
ASCIIColors.error(f"Extension file not found or is corrupted ({extension_path}).\nReturned the following exception:{ex}\nPlease verify that the personality you have selected exists or select another personality. Some updates may lead to change in personality name or category, so check the personality selection in settings to be sure.")
trace_exception(ex)
ASCIIColors.info("Trying to force reinstall")
if self.config["debug"]:
print(ex)
ASCIIColors.success(f" ╔══════════════════════════════════════════════════╗ ")
ASCIIColors.success(f" ║ Done ║ ")
ASCIIColors.success(f" ╚══════════════════════════════════════════════════╝ ")
# Sort the indices in descending order to ensure correct removal
to_remove.sort(reverse=True)
# Remove elements from the list based on the indices
for index in to_remove:
if 0 <= index < len(mounted_extensions):
mounted_extensions.pop(index)
self.config["extensions"].pop(index)
ASCIIColors.info(f"removed personality {extension_path}")
return mounted_extensions
# ================================== LOLLMSApp
#properties
@property
def message_id(self):
return self._message_id
@message_id.setter
def message_id(self, id):
self._message_id=id
@property
def current_user_message_id(self):
return self._current_user_message_id
@current_user_message_id.setter
def current_user_message_id(self, id):
self._current_user_message_id=id
self._message_id = id
@property
def current_ai_message_id(self):
return self._current_ai_message_id
@current_ai_message_id.setter
def current_ai_message_id(self, id):
self._current_ai_message_id=id
self._message_id = id
def download_file(self, url, installation_path, callback=None):
"""
Downloads a file from a URL, reports the download progress using a callback function, and displays a progress bar.
Args:
url (str): The URL of the file to download.
installation_path (str): The path where the file should be saved.
callback (function, optional): A callback function to be called during the download
with the progress percentage as an argument. Defaults to None.
"""
try:
response = requests.get(url, stream=True)
# Get the file size from the response headers
total_size = int(response.headers.get('content-length', 0))
with open(installation_path, 'wb') as file:
downloaded_size = 0
with tqdm(total=total_size, unit='B', unit_scale=True, ncols=80) as progress_bar:
for chunk in response.iter_content(chunk_size=8192):
if chunk:
file.write(chunk)
downloaded_size += len(chunk)
if callback is not None:
callback(downloaded_size, total_size)
progress_bar.update(len(chunk))
if callback is not None:
callback(total_size, total_size)
print("File downloaded successfully")
except Exception as e:
print("Couldn't download file:", str(e))
def clean_string(self, input_string):
# Remove extra spaces by replacing multiple spaces with a single space
#cleaned_string = re.sub(r'\s+', ' ', input_string)
# Remove extra line breaks by replacing multiple consecutive line breaks with a single line break
cleaned_string = re.sub(r'\n\s*\n', '\n', input_string)
# Create a string containing all punctuation characters
punctuation_chars = string.punctuation
# Define a regular expression pattern to match and remove non-alphanumeric characters
#pattern = f'[^a-zA-Z0-9\s{re.escape(punctuation_chars)}]' # This pattern matches any character that is not a letter, digit, space, or punctuation
pattern = f'[^a-zA-Z0-9\u00C0-\u017F\s{re.escape(punctuation_chars)}]'
# Use re.sub to replace the matched characters with an empty string
cleaned_string = re.sub(pattern, '', cleaned_string)
return cleaned_string
def make_discussion_title(self, discussion, client_id=None):
"""
Builds a title for a discussion
"""
# Get the list of messages
messages = discussion.get_messages()
discussion_messages = "!@>instruction: Create a short title to this discussion\n"
discussion_title = "\n!@>Discussion title:"
available_space = self.config.ctx_size - 150 - len(self.model.tokenize(discussion_messages))- len(self.model.tokenize(discussion_title))
# Initialize a list to store the full messages
full_message_list = []
# Accumulate messages until the cumulative number of tokens exceeds available_space
tokens_accumulated = 0
# Accumulate messages starting from message_index
for message in messages:
# Check if the message content is not empty and visible to the AI
if message.content != '' and (
message.message_type <= MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_USER.value and message.message_type != MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_AI.value):
# Tokenize the message content
message_tokenized = self.model.tokenize(
"\n" + self.config.discussion_prompt_separator + message.sender + ": " + message.content.strip())
# Check if adding the message will exceed the available space
if tokens_accumulated + len(message_tokenized) > available_space:
break
# Add the tokenized message to the full_message_list
full_message_list.insert(0, message_tokenized)
# Update the cumulative number of tokens
tokens_accumulated += len(message_tokenized)
# Build the final discussion messages by detokenizing the full_message_list
for message_tokens in full_message_list:
discussion_messages += self.model.detokenize(message_tokens)
discussion_messages += discussion_title
title = [""]
def receive(
chunk:str,
message_type:MSG_TYPE
):
if chunk:
title[0] += chunk
antiprompt = self.personality.detect_antiprompt(title[0])
if antiprompt:
ASCIIColors.warning(f"\nDetected hallucination with antiprompt: {antiprompt}")
title[0] = self.remove_text_from_string(title[0],antiprompt)
return False
else:
return True
self._generate(discussion_messages, 150, client_id, receive)
ASCIIColors.info(title[0])
return title[0]
def prepare_reception(self, client_id):
if not self.connections[client_id]["continuing"]:
self.connections[client_id]["generated_text"] = ""
self.connections[client_id]["first_chunk"]=True
self.nb_received_tokens = 0
self.start_time = datetime.now()
def recover_discussion(self,client_id, message_index=-1):
messages = self.connections[client_id]["current_discussion"].get_messages()
discussion=""
for msg in messages:
if message_index!=-1 and msg>message_index:
break
discussion += "\n" + self.config.discussion_prompt_separator + msg.sender + ": " + msg.content.strip()
return discussion
def prepare_query(self, client_id: str, message_id: int = -1, is_continue: bool = False, n_tokens: int = 0, generation_type = None) -> Tuple[str, str, List[str]]:
"""
Prepares the query for the model.
Args:
client_id (str): The client ID.
message_id (int): The message ID. Default is -1.
is_continue (bool): Whether the query is a continuation. Default is False.
n_tokens (int): The number of tokens. Default is 0.
Returns:
Tuple[str, str, List[str]]: The prepared query, original message content, and tokenized query.
"""
# Get the list of messages
messages = self.connections[client_id]["current_discussion"].get_messages()
# Find the index of the message with the specified message_id
message_index = -1
for i, message in enumerate(messages):
if message.id == message_id:
message_index = i
break
# Define current message
current_message = messages[message_index]
# Build the conditionning text block
conditionning = self.personality.personality_conditioning
# Check if there are document files to add to the prompt
documentation = ""
history = ""
# boosting information
if self.config.positive_boost:
positive_boost="\n!@>important information: "+self.config.positive_boost+"\n"
n_positive_boost = len(self.model.tokenize(positive_boost))
else:
positive_boost=""
n_positive_boost = 0
if self.config.negative_boost:
negative_boost="\n!@>important information: "+self.config.negative_boost+"\n"
n_negative_boost = len(self.model.tokenize(negative_boost))
else:
negative_boost=""
n_negative_boost = 0
if self.config.force_output_language_to_be:
force_language="\n!@>important information: Answer the user in this language :"+self.config.force_output_language_to_be+"\n"
n_force_language = len(self.model.tokenize(force_language))
else:
force_language=""
n_force_language = 0
if generation_type != "simple_question":
if self.personality.persona_data_vectorizer:
if documentation=="":
documentation="!@>Documentation:\n"
if self.config.data_vectorization_build_keys_words:
discussion = self.recover_discussion(client_id)[-512:]
query = self.personality.fast_gen(f"\n!@>instruction: Read the discussion and rewrite the last prompt for someone who didn't read the entire discussion.\nDo not answer the prompt. Do not add explanations.\n!@>discussion:\n{discussion}\n!@>enhanced query: ", max_generation_size=256, show_progress=True)
ASCIIColors.cyan(f"Query:{query}")
else:
query = current_message.content
try:
docs, sorted_similarities = self.personality.persona_data_vectorizer.recover_text(query, top_k=self.config.data_vectorization_nb_chunks)
for doc, infos in zip(docs, sorted_similarities):
documentation += f"document chunk:\n{doc}"
except:
self.warning("Couldn't add documentation to the context. Please verify the vector database")
if len(self.personality.text_files) > 0 and self.personality.vectorizer:
if documentation=="":
documentation="!@>Documentation:\n"
if self.config.data_vectorization_build_keys_words:
discussion = self.recover_discussion(client_id)[-512:]
query = self.personality.fast_gen(f"\n!@>instruction: Read the discussion and rewrite the last prompt for someone who didn't read the entire discussion.\nDo not answer the prompt. Do not add explanations.\n!@>discussion:\n{discussion}\n!@>enhanced query: ", max_generation_size=256, show_progress=True)
ASCIIColors.cyan(f"Query:{query}")
else:
query = current_message.content
try:
docs, sorted_similarities = self.personality.vectorizer.recover_text(query, top_k=self.config.data_vectorization_nb_chunks)
for doc, infos in zip(docs, sorted_similarities):
documentation += f"document chunk:\nchunk path: {infos[0]}\nchunk content:{doc}"
documentation += "\n!@>important information: Use the documentation data to answer the user questions. If the data is not present in the documentation, please tell the user that the information he is asking for does not exist in the documentation section. It is strictly forbidden to give the user an answer without having actual proof from the documentation."
except:
self.warning("Couldn't add documentation to the context. Please verify the vector database")
# Check if there is discussion history to add to the prompt
if self.config.use_discussions_history and self.long_term_memory is not None:
if history=="":
history="!@>previous discussions:\n"
try:
docs, sorted_similarities = self.long_term_memory.recover_text(current_message.content, top_k=self.config.data_vectorization_nb_chunks)
for i,(doc, infos) in enumerate(zip(docs, sorted_similarities)):
history += f"!@>previous discussion {i}:\n!@>discussion title:\n{infos[0]}\ndiscussion content:\n{doc}"
except:
self.warning("Couldn't add long term memory information to the context. Please verify the vector database") # Add information about the user
user_description=""
if self.config.use_user_name_in_discussions:
user_description="!@>User description:\n"+self.config.user_description+"\n"
# Tokenize the conditionning text and calculate its number of tokens
tokens_conditionning = self.model.tokenize(conditionning)
n_cond_tk = len(tokens_conditionning)
# Tokenize the documentation text and calculate its number of tokens
if len(documentation)>0:
tokens_documentation = self.model.tokenize(documentation)
n_doc_tk = len(tokens_documentation)
else:
tokens_documentation = []
n_doc_tk = 0
# Tokenize the history text and calculate its number of tokens
if len(history)>0:
tokens_history = self.model.tokenize(history)
n_history_tk = len(tokens_history)
else:
tokens_history = []
n_history_tk = 0
# Tokenize user description
if len(user_description)>0:
tokens_user_description = self.model.tokenize(user_description)
n_user_description_tk = len(tokens_user_description)
else:
tokens_user_description = []
n_user_description_tk = 0
# Calculate the total number of tokens between conditionning, documentation, and history
total_tokens = n_cond_tk + n_doc_tk + n_history_tk + n_user_description_tk + n_positive_boost + n_negative_boost + n_force_language
# Calculate the available space for the messages
available_space = self.config.ctx_size - n_tokens - total_tokens
if self.config.debug:
self.info(f"Tokens summary:\nConditionning:{n_cond_tk}\ndoc:{n_doc_tk}\nhistory:{n_history_tk}\nuser description:{n_user_description_tk}\nAvailable space:{available_space}",10)
# Raise an error if the available space is 0 or less
if available_space<1:
self.error("Not enough space in context!!")
raise Exception("Not enough space in context!!")
# Accumulate messages until the cumulative number of tokens exceeds available_space
tokens_accumulated = 0
# Initialize a list to store the full messages
full_message_list = []
# If this is not a continue request, we add the AI prompt
if not is_continue:
message_tokenized = self.model.tokenize(
"\n" +self.personality.ai_message_prefix.strip()
)
full_message_list.append(message_tokenized)
# Update the cumulative number of tokens
tokens_accumulated += len(message_tokenized)
if generation_type != "simple_question":
# Accumulate messages starting from message_index
for i in range(message_index, -1, -1):
message = messages[i]
# Check if the message content is not empty and visible to the AI
if message.content != '' and (
message.message_type <= MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_USER.value and message.message_type != MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_AI.value):
# Tokenize the message content
message_tokenized = self.model.tokenize(
"\n" + self.config.discussion_prompt_separator + message.sender + ": " + message.content.strip())
# Check if adding the message will exceed the available space
if tokens_accumulated + len(message_tokenized) > available_space:
break
# Add the tokenized message to the full_message_list
full_message_list.insert(0, message_tokenized)
# Update the cumulative number of tokens
tokens_accumulated += len(message_tokenized)
else:
message = messages[message_index]
# Check if the message content is not empty and visible to the AI
if message.content != '' and (
message.message_type <= MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_USER.value and message.message_type != MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_AI.value):
# Tokenize the message content
message_tokenized = self.model.tokenize(
"\n" + self.config.discussion_prompt_separator + message.sender + ": " + message.content.strip())
# Add the tokenized message to the full_message_list
full_message_list.insert(0, message_tokenized)
# Update the cumulative number of tokens
tokens_accumulated += len(message_tokenized)
# Build the final discussion messages by detokenizing the full_message_list
discussion_messages = ""
for i in range(len(full_message_list)-1):
message_tokens = full_message_list[i]
discussion_messages += self.model.detokenize(message_tokens)
# Build the final prompt by concatenating the conditionning and discussion messages
prompt_data = conditionning + documentation + history + user_description + discussion_messages + positive_boost + negative_boost + force_language + self.model.detokenize(full_message_list[-1])
# Tokenize the prompt data
tokens = self.model.tokenize(prompt_data)
# if this is a debug then show prompt construction details
if self.config["debug"]:
ASCIIColors.bold("CONDITIONNING")
ASCIIColors.yellow(conditionning)
ASCIIColors.bold("DOC")
ASCIIColors.yellow(documentation)
ASCIIColors.bold("HISTORY")
ASCIIColors.yellow(history)
ASCIIColors.bold("DISCUSSION")
ASCIIColors.hilight(discussion_messages,"!@>",ASCIIColors.color_yellow,ASCIIColors.color_bright_red,False)
ASCIIColors.bold("Final prompt")
ASCIIColors.hilight(prompt_data,"!@>",ASCIIColors.color_yellow,ASCIIColors.color_bright_red,False)
ASCIIColors.info(f"prompt size:{len(tokens)} tokens")
ASCIIColors.info(f"available space after doc and history:{available_space} tokens")
self.info(f"Tokens summary:\nPrompt size:{len(tokens)}\nTo generate:{available_space}",10)
# Return the prepared query, original message content, and tokenized query
return prompt_data, current_message.content, tokens
def get_discussion_to(self, client_id, message_id=-1):
messages = self.connections[client_id]["current_discussion"].get_messages()
full_message_list = []
ump = self.config.discussion_prompt_separator +self.config.user_name.strip() if self.config.use_user_name_in_discussions else self.personality.user_message_prefix
for message in messages:
if message["id"]<= message_id or message_id==-1:
if message["type"]!=MSG_TYPE.MSG_TYPE_FULL_INVISIBLE_TO_USER:
if message["sender"]==self.personality.name:
full_message_list.append(self.personality.ai_message_prefix+message["content"])
else:
full_message_list.append(ump + message["content"])
link_text = "\n"# self.personality.link_text
if len(full_message_list) > self.config["nb_messages_to_remember"]:
discussion_messages = self.personality.personality_conditioning+ link_text.join(full_message_list[-self.config["nb_messages_to_remember"]:])
else:
discussion_messages = self.personality.personality_conditioning+ link_text.join(full_message_list)
return discussion_messages # Removes the last return
def notify(
self,
content,
notification_type:NotificationType=NotificationType.NOTIF_SUCCESS,
duration:int=4,
client_id=None,
display_type:NotificationDisplayType=NotificationDisplayType.TOAST,
verbose=True
):
self.socketio.emit('notification', {
'content': content,# self.connections[client_id]["generated_text"],
'notification_type': notification_type.value,
"duration": duration,
'display_type':display_type.value
}, room=client_id
)
self.socketio.sleep(0.01)
if verbose:
if notification_type==NotificationType.NOTIF_SUCCESS:
ASCIIColors.success(content)
elif notification_type==NotificationType.NOTIF_INFO:
ASCIIColors.info(content)
elif notification_type==NotificationType.NOTIF_WARNING:
ASCIIColors.warning(content)
else:
ASCIIColors.red(content)
def new_message(self,
client_id,
sender=None,
content="",
parameters=None,
metadata=None,
ui=None,
message_type:MSG_TYPE=MSG_TYPE.MSG_TYPE_FULL,
sender_type:SENDER_TYPES=SENDER_TYPES.SENDER_TYPES_AI,
open=False
):
mtdt = metadata if metadata is None or type(metadata) == str else json.dumps(metadata, indent=4)
if sender==None:
sender= self.personality.name
msg = self.connections[client_id]["current_discussion"].add_message(
message_type = message_type.value,
sender_type = sender_type.value,
sender = sender,
content = content,
metadata = mtdt,
ui = ui,
rank = 0,
parent_message_id = self.connections[client_id]["current_discussion"].current_message.id,
binding = self.config["binding_name"],
model = self.config["model_name"],
personality = self.config["personalities"][self.config["active_personality_id"]],
) # first the content is empty, but we'll fill it at the end
self.socketio.emit('new_message',
{
"sender": sender,
"message_type": message_type.value,
"sender_type": SENDER_TYPES.SENDER_TYPES_AI.value,
"content": content,
"parameters": parameters,
"metadata": metadata,
"ui": ui,
"id": msg.id,
"parent_message_id": msg.parent_message_id,
'binding': self.config["binding_name"],
'model' : self.config["model_name"],
'personality': self.config["personalities"][self.config["active_personality_id"]],
'created_at': self.connections[client_id]["current_discussion"].current_message.created_at,
'finished_generating_at': self.connections[client_id]["current_discussion"].current_message.finished_generating_at,
'open': open
}, room=client_id
)
def update_message(self, client_id, chunk,
parameters=None,
metadata=[],
ui=None,
msg_type:MSG_TYPE=None
):
self.connections[client_id]["current_discussion"].current_message.finished_generating_at=datetime.now().strftime('%Y-%m-%d %H:%M:%S')
mtdt = json.dumps(metadata, indent=4) if metadata is not None and type(metadata)== list else metadata
if self.nb_received_tokens==1:
self.socketio.emit('update_message', {
"sender": self.personality.name,
'id':self.connections[client_id]["current_discussion"].current_message.id,
'content': "✍ warming up ...",# self.connections[client_id]["generated_text"],
'ui': ui,
'discussion_id':self.connections[client_id]["current_discussion"].discussion_id,
'message_type': MSG_TYPE.MSG_TYPE_STEP_END.value,
'finished_generating_at': self.connections[client_id]["current_discussion"].current_message.finished_generating_at,
'parameters':parameters,
'metadata':metadata
}, room=client_id
)
self.socketio.emit('update_message', {
"sender": self.personality.name,
'id':self.connections[client_id]["current_discussion"].current_message.id,
'content': chunk,# self.connections[client_id]["generated_text"],
'ui': ui,
'discussion_id':self.connections[client_id]["current_discussion"].discussion_id,
'message_type': msg_type.value if msg_type is not None else MSG_TYPE.MSG_TYPE_CHUNK.value if self.nb_received_tokens>1 else MSG_TYPE.MSG_TYPE_FULL.value,
'finished_generating_at': self.connections[client_id]["current_discussion"].current_message.finished_generating_at,
'parameters':parameters,
'metadata':metadata
}, room=client_id
)
self.socketio.sleep(0.01)
if msg_type != MSG_TYPE.MSG_TYPE_INFO:
self.connections[client_id]["current_discussion"].update_message(self.connections[client_id]["generated_text"], new_metadata=mtdt, new_ui=ui)
def close_message(self, client_id):
if not self.connections[client_id]["current_discussion"]:
return
#fix halucination
self.connections[client_id]["generated_text"]=self.connections[client_id]["generated_text"].split("!@>")[0]
# Send final message
self.connections[client_id]["current_discussion"].current_message.finished_generating_at=datetime.now().strftime('%Y-%m-%d %H:%M:%S')
self.socketio.emit('close_message', {
"sender": self.personality.name,
"id": self.connections[client_id]["current_discussion"].current_message.id,
"content":self.connections[client_id]["generated_text"],
'binding': self.config["binding_name"],
'model' : self.config["model_name"],
'personality':self.config["personalities"][self.config["active_personality_id"]],
'created_at': self.connections[client_id]["current_discussion"].current_message.created_at,
'finished_generating_at': self.connections[client_id]["current_discussion"].current_message.finished_generating_at,
}, room=client_id
)
def process_chunk(
self,
chunk:str,
message_type:MSG_TYPE,
parameters:dict=None,
metadata:list=None,
client_id:int=0,
personality:AIPersonality=None
):
"""
Processes a chunk of generated text
"""
if chunk is None:
return True
if not client_id in list(self.connections.keys()):
self.error("Connection lost", client_id=client_id)
return
if message_type == MSG_TYPE.MSG_TYPE_STEP:
ASCIIColors.info("--> Step:"+chunk)
if message_type == MSG_TYPE.MSG_TYPE_STEP_START:
ASCIIColors.info("--> Step started:"+chunk)
if message_type == MSG_TYPE.MSG_TYPE_STEP_END:
if parameters['status']:
ASCIIColors.success("--> Step ended:"+chunk)
else:
ASCIIColors.error("--> Step ended:"+chunk)
if message_type == MSG_TYPE.MSG_TYPE_EXCEPTION:
self.error(chunk, client_id=client_id)
ASCIIColors.error("--> Exception from personality:"+chunk)
if message_type == MSG_TYPE.MSG_TYPE_WARNING:
self.warning(chunk,client_id=client_id)
ASCIIColors.error("--> Exception from personality:"+chunk)
if message_type == MSG_TYPE.MSG_TYPE_INFO:
self.info(chunk, client_id=client_id)
ASCIIColors.info("--> Info:"+chunk)
if message_type == MSG_TYPE.MSG_TYPE_UI:
self.update_message(client_id, "", parameters, metadata, chunk, MSG_TYPE.MSG_TYPE_UI)
if message_type == MSG_TYPE.MSG_TYPE_NEW_MESSAGE:
self.nb_received_tokens = 0
self.start_time = datetime.now()
self.new_message(
client_id,
self.personality.name if personality is None else personality.name,
chunk if parameters["type"]!=MSG_TYPE.MSG_TYPE_UI.value else '',
metadata = [{
"title":chunk,
"content":parameters["metadata"]
}
] if parameters["type"]==MSG_TYPE.MSG_TYPE_JSON_INFOS.value else None,
ui= chunk if parameters["type"]==MSG_TYPE.MSG_TYPE_UI.value else None,
message_type= MSG_TYPE(parameters["type"]))
elif message_type == MSG_TYPE.MSG_TYPE_FINISHED_MESSAGE:
self.close_message(client_id)
elif message_type == MSG_TYPE.MSG_TYPE_CHUNK:
if self.nb_received_tokens==0:
self.start_time = datetime.now()
dt =(datetime.now() - self.start_time).seconds
if dt==0:
dt=1
spd = self.nb_received_tokens/dt
ASCIIColors.green(f"Received {self.nb_received_tokens} tokens (speed: {spd:.2f}t/s) ",end="\r",flush=True)
sys.stdout = sys.__stdout__
sys.stdout.flush()
if chunk:
self.connections[client_id]["generated_text"] += chunk
antiprompt = self.personality.detect_antiprompt(self.connections[client_id]["generated_text"])
if antiprompt:
ASCIIColors.warning(f"\nDetected hallucination with antiprompt: {antiprompt}")
self.connections[client_id]["generated_text"] = self.remove_text_from_string(self.connections[client_id]["generated_text"],antiprompt)
self.update_message(client_id, self.connections[client_id]["generated_text"], parameters, metadata, None, MSG_TYPE.MSG_TYPE_FULL)
return False
else:
self.nb_received_tokens += 1
if self.connections[client_id]["continuing"] and self.connections[client_id]["first_chunk"]:
self.update_message(client_id, self.connections[client_id]["generated_text"], parameters, metadata)
else:
self.update_message(client_id, chunk, parameters, metadata, msg_type=MSG_TYPE.MSG_TYPE_CHUNK)
self.connections[client_id]["first_chunk"]=False
# if stop generation is detected then stop
if not self.cancel_gen:
return True
else:
self.cancel_gen = False
ASCIIColors.warning("Generation canceled")
return False
# Stream the generated text to the main process
elif message_type == MSG_TYPE.MSG_TYPE_FULL:
self.connections[client_id]["generated_text"] = chunk
self.nb_received_tokens += 1
dt =(datetime.now() - self.start_time).seconds
if dt==0:
dt=1
spd = self.nb_received_tokens/dt
ASCIIColors.green(f"Received {self.nb_received_tokens} tokens (speed: {spd:.2f}t/s) ",end="\r",flush=True)
antiprompt = self.personality.detect_antiprompt(self.connections[client_id]["generated_text"])
if antiprompt:
ASCIIColors.warning(f"\nDetected hallucination with antiprompt: {antiprompt}")
self.connections[client_id]["generated_text"] = self.remove_text_from_string(self.connections[client_id]["generated_text"],antiprompt)
self.update_message(client_id, self.connections[client_id]["generated_text"], parameters, metadata, None, MSG_TYPE.MSG_TYPE_FULL)
return False
self.update_message(client_id, chunk, parameters, metadata, ui=None, msg_type=message_type)
return True
# Stream the generated text to the frontend
else:
self.update_message(client_id, chunk, parameters, metadata, ui=None, msg_type=message_type)
return True
def generate(self, full_prompt, prompt, n_predict, client_id, callback=None):
if self.personality.processor is not None:
ASCIIColors.info("Running workflow")
try:
self.personality.callback = callback
self.personality.processor.run_workflow( prompt, full_prompt, callback)
except Exception as ex:
trace_exception(ex)
# Catch the exception and get the traceback as a list of strings
traceback_lines = traceback.format_exception(type(ex), ex, ex.__traceback__)
# Join the traceback lines into a single string
traceback_text = ''.join(traceback_lines)
ASCIIColors.error(f"Workflow run failed.\nError:{ex}")
ASCIIColors.error(traceback_text)
if callback:
callback(f"Workflow run failed\nError:{ex}", MSG_TYPE.MSG_TYPE_EXCEPTION)
print("Finished executing the workflow")
return
self._generate(full_prompt, n_predict, client_id, callback)
ASCIIColors.success("\nFinished executing the generation")
def _generate(self, prompt, n_predict, client_id, callback=None):
self.nb_received_tokens = 0
self.start_time = datetime.now()
if self.model is not None:
if self.model.binding_type==BindingType.TEXT_IMAGE and len(self.personality.image_files)>0:
ASCIIColors.info(f"warmup for generating up to {n_predict} tokens")
if self.config["override_personality_model_parameters"]:
output = self.model.generate_with_images(
prompt,
self.personality.image_files,
callback=callback,
n_predict=n_predict,
temperature=self.config['temperature'],
top_k=self.config['top_k'],
top_p=self.config['top_p'],
repeat_penalty=self.config['repeat_penalty'],
repeat_last_n = self.config['repeat_last_n'],
seed=self.config['seed'],
n_threads=self.config['n_threads']
)
else:
output = self.model.generate_with_images(
prompt,
self.personality.image_files,
callback=callback,
n_predict=min(n_predict,self.personality.model_n_predicts),
temperature=self.personality.model_temperature,
top_k=self.personality.model_top_k,
top_p=self.personality.model_top_p,
repeat_penalty=self.personality.model_repeat_penalty,
repeat_last_n = self.personality.model_repeat_last_n,
seed=self.config['seed'],
n_threads=self.config['n_threads']
)
else:
ASCIIColors.info(f"warmup for generating up to {n_predict} tokens")
if self.config["override_personality_model_parameters"]:
output = self.model.generate(
prompt,
callback=callback,
n_predict=n_predict,
temperature=self.config['temperature'],
top_k=self.config['top_k'],
top_p=self.config['top_p'],
repeat_penalty=self.config['repeat_penalty'],
repeat_last_n = self.config['repeat_last_n'],
seed=self.config['seed'],
n_threads=self.config['n_threads']
)
else:
output = self.model.generate(
prompt,
callback=callback,
n_predict=min(n_predict,self.personality.model_n_predicts),
temperature=self.personality.model_temperature,
top_k=self.personality.model_top_k,
top_p=self.personality.model_top_p,
repeat_penalty=self.personality.model_repeat_penalty,
repeat_last_n = self.personality.model_repeat_last_n,
seed=self.config['seed'],
n_threads=self.config['n_threads']
)
else:
print("No model is installed or selected. Please make sure to install a model and select it inside your configuration before attempting to communicate with the model.")
print("To do this: Install the model to your models/<binding name> folder.")
print("Then set your model information in your local configuration file that you can find in configs/local_config.yaml")
print("You can also use the ui to set your model in the settings page.")
output = ""
return output
def start_message_generation(self, message, message_id, client_id, is_continue=False, generation_type=None):
if self.personality is None:
self.warning("Select a personality")
return
ASCIIColors.info(f"Text generation requested by client: {client_id}")
# send the message to the bot
print(f"Received message : {message.content}")
if self.connections[client_id]["current_discussion"]:
if not self.model:
self.error("No model selected. Please make sure you select a model before starting generation", client_id=client_id)
return
# First we need to send the new message ID to the client
if is_continue:
self.connections[client_id]["current_discussion"].load_message(message_id)
self.connections[client_id]["generated_text"] = message.content
else:
self.new_message(client_id, self.personality.name, "")
self.update_message(client_id, "✍ warming up ...", msg_type=MSG_TYPE.MSG_TYPE_STEP_START)
self.socketio.sleep(0.01)
# prepare query and reception
self.discussion_messages, self.current_message, tokens = self.prepare_query(client_id, message_id, is_continue, n_tokens=self.config.min_n_predict, generation_type=generation_type)
self.prepare_reception(client_id)
self.generating = True
self.connections[client_id]["processing"]=True
try:
self.generate(
self.discussion_messages,
self.current_message,
n_predict = self.config.ctx_size-len(tokens)-1,
client_id=client_id,
callback=partial(self.process_chunk,client_id = client_id)
)
if self.config.enable_voice_service and self.config.auto_read and len(self.personality.audio_samples)>0:
try:
self.process_chunk("Generating voice output",MSG_TYPE.MSG_TYPE_STEP_START,client_id=client_id)
from lollms.services.xtts.lollms_xtts import LollmsXTTS
if self.tts is None:
self.tts = LollmsXTTS(self, voice_samples_path=Path(__file__).parent.parent/"voices")
language = convert_language_name(self.personality.language)
self.tts.set_speaker_folder(Path(self.personality.audio_samples[0]).parent)
fn = self.personality.name.lower().replace(' ',"_").replace('.','')
fn = f"{fn}_{message_id}.wav"
url = f"audio/{fn}"
self.tts.tts_to_file(self.connections[client_id]["generated_text"], Path(self.personality.audio_samples[0]).name, f"{fn}", language=language)
fl = f"""
<audio controls>
<source src="{url}" type="audio/wav">
Your browser does not support the audio element.
</audio>
"""
self.process_chunk("Generating voice output", MSG_TYPE.MSG_TYPE_STEP_END, {'status':True},client_id=client_id)
self.process_chunk(fl,MSG_TYPE.MSG_TYPE_UI, client_id=client_id)
"""
self.info("Creating audio output",10)
self.personality.step_start("Creating audio output")
if not PackageManager.check_package_installed("tortoise"):
PackageManager.install_package("tortoise-tts")
from tortoise import utils, api
import sounddevice as sd
if self.tts is None:
self.tts = api.TextToSpeech( kv_cache=True, half=True)
reference_clips = [utils.audio.load_audio(str(p), 22050) for p in self.personality.audio_samples]
tk = self.model.tokenize(self.connections[client_id]["generated_text"])
if len(tk)>100:
chunk_size = 100
for i in range(0, len(tk), chunk_size):
chunk = self.model.detokenize(tk[i:i+chunk_size])
if i==0:
pcm_audio = self.tts.tts_with_preset(chunk, voice_samples=reference_clips, preset='fast').numpy().flatten()
else:
pcm_audio = np.concatenate([pcm_audio, self.tts.tts_with_preset(chunk, voice_samples=reference_clips, preset='ultra_fast').numpy().flatten()])
else:
pcm_audio = self.tts.tts_with_preset(self.connections[client_id]["generated_text"], voice_samples=reference_clips, preset='fast').numpy().flatten()
sd.play(pcm_audio, 22050)
self.personality.step_end("Creating audio output")
"""
except Exception as ex:
ASCIIColors.error("Couldn't read")
trace_exception(ex)
print()
ASCIIColors.success("## Done Generation ##")
print()
except Exception as ex:
trace_exception(ex)
print()
ASCIIColors.error("## Generation Error ##")
print()
self.cancel_gen = False
# Send final message
self.close_message(client_id)
self.socketio.sleep(0.01)
self.connections[client_id]["processing"]=False
if self.connections[client_id]["schedule_for_deletion"]:
del self.connections[client_id]
ASCIIColors.success(f" ╔══════════════════════════════════════════════════╗ ")
ASCIIColors.success(f" ║ Done ║ ")
ASCIIColors.success(f" ╚══════════════════════════════════════════════════╝ ")
if self.config.auto_title:
d = self.connections[client_id]["current_discussion"]
ttl = d.title()
if ttl is None or ttl=="" or ttl=="untitled":
title = self.make_discussion_title(d, client_id=client_id)
d.rename(title)
self.socketio.emit('disucssion_renamed',{
'status': True,
'discussion_id':d.discussion_id,
'title':title
}, room=client_id)
self.busy=False
else:
ump = self.config.discussion_prompt_separator +self.config.user_name.strip() if self.config.use_user_name_in_discussions else self.personality.user_message_prefix
self.cancel_gen = False
#No discussion available
ASCIIColors.warning("No discussion selected!!!")
self.error("No discussion selected!!!", client_id=client_id)
print()
self.busy=False
return ""
Reference in New Issue View Git Blame Copy Permalink