mirror of
https://github.com/ParisNeo/lollms-webui.git
synced 2024-12-24 14:36:39 +00:00
752 lines
31 KiB
Python
752 lines
31 KiB
Python
######
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# Project : GPT4ALL-UI
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# File : api.py
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# Author : ParisNeo with the help of the community
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# Supported by Nomic-AI
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# license : Apache 2.0
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# Description :
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# A simple api to communicate with gpt4all-ui and its models.
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######
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from datetime import datetime
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from gpt4all_api.db import DiscussionsDB
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from pathlib import Path
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import importlib
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from pyaipersonality import AIPersonality
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import multiprocessing as mp
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import threading
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import time
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import requests
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from tqdm import tqdm
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import traceback
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__author__ = "parisneo"
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__github__ = "https://github.com/nomic-ai/gpt4all-ui"
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__copyright__ = "Copyright 2023, "
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__license__ = "Apache 2.0"
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import subprocess
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import pkg_resources
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# ===========================================================
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# Manage automatic install scripts
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def is_package_installed(package_name):
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try:
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dist = pkg_resources.get_distribution(package_name)
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return True
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except pkg_resources.DistributionNotFound:
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return False
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def install_package(package_name):
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try:
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# Check if the package is already installed
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__import__(package_name)
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print(f"{package_name} is already installed.")
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except ImportError:
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print(f"{package_name} is not installed. Installing...")
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# Install the package using pip
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subprocess.check_call(["pip", "install", package_name])
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print(f"{package_name} has been successfully installed.")
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def parse_requirements_file(requirements_path):
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with open(requirements_path, 'r') as f:
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for line in f:
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line = line.strip()
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if not line or line.startswith('#'):
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# Skip empty and commented lines
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continue
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package_name, _, version_specifier = line.partition('==')
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package_name, _, version_specifier = line.partition('>=')
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if is_package_installed(package_name):
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# The package is already installed
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print(f"{package_name} is already installed.")
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else:
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# The package is not installed, install it
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if version_specifier:
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install_package(f"{package_name}{version_specifier}")
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else:
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install_package(package_name)
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# ===========================================================
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class ModelProcess:
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def __init__(self, config=None):
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self.config = config
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self.generate_queue = mp.Queue()
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self.generation_queue = mp.Queue()
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self.cancel_queue = mp.Queue(maxsize=1)
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self.clear_queue_queue = mp.Queue(maxsize=1)
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self.set_config_queue = mp.Queue(maxsize=1)
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self.set_config_result_queue = mp.Queue(maxsize=1)
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self.started_queue = mp.Queue()
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self.process = None
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self.is_generating = mp.Value('i', 0)
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self.model_ready = mp.Value('i', 0)
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self.ready = False
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self.id=0
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self.n_predict=2048
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self.reset_config_result()
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def reset_config_result(self):
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self._set_config_result = {
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'status': 'succeeded',
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'backend_status':'ok',
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'model_status':'ok',
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'personality_status':'ok',
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'errors':[]
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}
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def load_backend(self, backend_name:str, install=False):
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backend_path = Path("backends")/backend_name
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if install:
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print("Installing the backend")
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# first find out if there is a requirements.txt file
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install_file_name="install.py"
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install_script_path = backend_path / install_file_name
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if install_script_path.exists():
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module_name = install_file_name[:-3] # Remove the ".py" extension
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module_spec = importlib.util.spec_from_file_location(module_name, str(install_script_path))
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module = importlib.util.module_from_spec(module_spec)
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module_spec.loader.exec_module(module)
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if hasattr(module, "Install"):
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self._install = module.Install(self)
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else:
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self._install = None
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# define the full absolute path to the module
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absolute_path = backend_path.resolve()
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# infer the module name from the file path
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module_name = backend_path.stem
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# use importlib to load the module from the file path
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loader = importlib.machinery.SourceFileLoader(module_name, str(absolute_path/"__init__.py"))
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backend_module = loader.load_module()
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backend_class = getattr(backend_module, backend_module.backend_name)
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return backend_class
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def start(self):
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if self.process is None:
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self.process = mp.Process(target=self._run)
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self.process.start()
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def stop(self):
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if self.process is not None:
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self.generate_queue.put(None)
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self.process.join()
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self.process = None
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def set_backend(self, backend_path):
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self.backend = backend_path
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def set_model(self, model_path):
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self.model = model_path
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def set_config(self, config):
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self.set_config_queue.put(config)
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# Wait for it t o be consumed
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while self.set_config_result_queue.empty():
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time.sleep(0.5)
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return self.set_config_result_queue.get()
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def generate(self, full_prompt, prompt, id, n_predict):
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self.generate_queue.put((full_prompt, prompt, id, n_predict))
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def cancel_generation(self):
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self.cancel_queue.put(('cancel',))
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def clear_queue(self):
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self.clear_queue_queue.put(('clear_queue',))
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def rebuild_backend(self, config):
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try:
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print(" ******************* Building Backend from main Process *************************")
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backend = self.load_backend(config["backend"], install=False)
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print("Backend loaded successfully")
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except Exception as ex:
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traceback.print_exc()
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print("Couldn't build backend")
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print(ex)
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backend = None
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self._set_config_result['backend_status'] ='failed'
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self._set_config_result['errors'].append(f"couldn't build backend:{ex}")
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return backend
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def _rebuild_model(self):
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try:
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print(" ******************* Building Backend from generation Process *************************")
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self.backend = self.load_backend(self.config["backend"], install=True)
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print("Backend loaded successfully")
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try:
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model_file = Path("models")/self.config["backend"]/self.config["model"]
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print(f"Loading model : {model_file}")
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self.model = self.backend(self.config)
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self.model_ready.value = 1
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print("Model created successfully\n")
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except Exception as ex:
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traceback.print_exc()
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print("Couldn't build model")
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print(ex)
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self.model = None
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self._set_config_result['model_status'] ='failed'
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self._set_config_result['errors'].append(f"couldn't build model:{ex}")
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except Exception as ex:
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traceback.print_exc()
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print("Couldn't build backend")
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print(ex)
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self.backend = None
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self.model = None
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def rebuild_personality(self):
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try:
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print(" ******************* Building Personality from main Process *************************")
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personality_path = f"personalities/{self.config['personality_language']}/{self.config['personality_category']}/{self.config['personality']}"
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personality = AIPersonality(personality_path, run_scripts=False)
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print(f" ************ Personality {personality.name} is ready (Main process) ***************************")
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except Exception as ex:
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print(f"Personality file not found or is corrupted ({personality_path}).\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.")
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if self.config["debug"]:
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print(ex)
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personality = AIPersonality()
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return personality
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def _rebuild_personality(self):
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try:
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print(" ******************* Building Personality from generation Process *************************")
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personality_path = f"personalities/{self.config['personality_language']}/{self.config['personality_category']}/{self.config['personality']}"
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self.personality = AIPersonality(personality_path)
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print(f" ************ Personality {self.personality.name} is ready (generation process) ***************************")
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except Exception as ex:
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print(f"Personality file not found or is corrupted ({personality_path}).")
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print(f"Please 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.")
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print(f"Exception: {ex}")
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if self.config["debug"]:
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print(ex)
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self.personality = AIPersonality()
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self._set_config_result['personality_status'] ='failed'
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self._set_config_result['errors'].append(f"couldn't load personality:{ex}")
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def step_callback(self, text, message_type):
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if message_type==0:
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self.generation_queue.put((text,self.id, message_type))
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def _run(self):
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self._rebuild_model()
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self._rebuild_personality()
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if self.model_ready.value == 1:
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self.n_predict = 1
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self._generate("I",1)
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print()
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print("Ready to receive data")
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else:
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print("No model loaded. Waiting for new configuration instructions")
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self.ready = True
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print(f"Listening on :http://{self.config['host']}:{self.config['port']}")
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while True:
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try:
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self._check_set_config_queue()
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self._check_cancel_queue()
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self._check_clear_queue()
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if not self.generate_queue.empty():
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command = self.generate_queue.get()
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if command is None:
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break
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if self.cancel_queue.empty() and self.clear_queue_queue.empty():
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self.is_generating.value = 1
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self.started_queue.put(1)
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self.id=command[2]
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self.n_predict=command[3]
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if self.personality.processor is not None:
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if self.personality.processor_cfg is not None:
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if "custom_workflow" in self.personality.processor_cfg:
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if self.personality.processor_cfg["custom_workflow"]:
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print("Running workflow")
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output = self.personality.processor.run_workflow(self._generate, command[1], command[0], self.step_callback)
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self._callback(output)
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self.is_generating.value = 0
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continue
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self._generate(command[0], self.n_predict, self._callback)
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while not self.generation_queue.empty():
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time.sleep(1)
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self.is_generating.value = 0
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time.sleep(1)
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except Exception as ex:
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time.sleep(1)
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print(ex)
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def _generate(self, prompt, n_predict=50, callback=None):
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if self.model is not None:
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self.id = self.id
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if self.config["override_personality_model_parameters"]:
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output = self.model.generate(
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prompt,
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new_text_callback=callback,
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n_predict=n_predict,
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temp=self.config['temperature'],
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top_k=self.config['top_k'],
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top_p=self.config['top_p'],
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repeat_penalty=self.config['repeat_penalty'],
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repeat_last_n = self.config['repeat_last_n'],
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seed=self.config['seed'],
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n_threads=self.config['n_threads']
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)
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else:
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output = self.model.generate(
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prompt,
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new_text_callback=callback,
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n_predict=self.n_predict,
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temp=self.personality.model_temperature,
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top_k=self.personality.model_top_k,
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top_p=self.personality.model_top_p,
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repeat_penalty=self.personality.model_repeat_penalty,
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repeat_last_n = self.personality.model_repeat_last_n,
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#seed=self.config['seed'],
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n_threads=self.config['n_threads']
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)
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else:
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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.")
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print("To do this: Install the model to your models/<backend name> folder.")
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print("Then set your model information in your local configuration file that you can find in configs/local_default.yaml")
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print("You can also use the ui to set your model in the settings page.")
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output = ""
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return output
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def _callback(self, text):
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if not self.ready:
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print(".",end="", flush=True)
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return True
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else:
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# Stream the generated text to the main process
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self.generation_queue.put((text,self.id, 0))
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self._check_set_config_queue()
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self._check_cancel_queue()
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self._check_clear_queue()
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# if stop generation is detected then stop
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if self.is_generating.value==1:
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return True
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else:
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return False
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def _check_cancel_queue(self):
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while not self.cancel_queue.empty():
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command = self.cancel_queue.get()
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if command is not None:
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self._cancel_generation()
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def _check_clear_queue(self):
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while not self.clear_queue_queue.empty():
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command = self.clear_queue_queue.get()
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if command is not None:
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self._clear_queue()
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def _check_set_config_queue(self):
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while not self.set_config_queue.empty():
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config = self.set_config_queue.get()
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if config is not None:
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print("Inference process : Setting configuration")
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self.reset_config_result()
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self._set_config(config)
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self.set_config_result_queue.put(self._set_config_result)
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def _cancel_generation(self):
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self.is_generating.value = 0
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def _clear_queue(self):
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while not self.generate_queue.empty():
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self.generate_queue.get()
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def _set_config(self, config):
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bk_cfg = self.config
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self.config = config
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print("Changing configuration")
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# verify that the backend is the same
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if self.config["backend"]!=bk_cfg["backend"] or self.config["model"]!=bk_cfg["model"]:
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self._rebuild_model()
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# verify that the personality is the same
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if self.config["personality"]!=bk_cfg["personality"] or self.config["personality_category"]!=bk_cfg["personality_category"] or self.config["personality_language"]!=bk_cfg["personality_language"]:
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self._rebuild_personality()
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class GPT4AllAPI():
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def __init__(self, config:dict, socketio, config_file_path:str) -> None:
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self.socketio = socketio
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#Create and launch the process
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self.process = ModelProcess(config)
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self.process.start()
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self.config = config
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self.backend = self.process.rebuild_backend(self.config)
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self.personality = self.process.rebuild_personality()
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if config["debug"]:
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print(print(f"{self.personality}"))
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self.config_file_path = config_file_path
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self.cancel_gen = False
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# Keeping track of current discussion and message
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self.current_discussion = None
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self._current_user_message_id = 0
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self._current_ai_message_id = 0
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self._message_id = 0
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self.db_path = config["db_path"]
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# Create database object
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self.db = DiscussionsDB(self.db_path)
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# If the database is empty, populate it with tables
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self.db.populate()
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# This is used to keep track of messages
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self.full_message_list = []
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# =========================================================================================
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# Socket IO stuff
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# =========================================================================================
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@socketio.on('connect')
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def connect():
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print('Client connected')
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@socketio.on('disconnect')
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def disconnect():
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print('Client disconnected')
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@socketio.on('install_model')
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def install_model(data):
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def install_model_():
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print("Install model triggered")
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model_path = data["path"]
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progress = 0
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installation_dir = Path(f'./models/{self.config["backend"]}/')
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filename = Path(model_path).name
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installation_path = installation_dir / filename
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print("Model install requested")
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print(f"Model path : {model_path}")
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if installation_path.exists():
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print("Error: Model already exists")
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socketio.emit('install_progress',{'status': 'failed', 'error': 'model already exists'})
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socketio.emit('install_progress',{'status': 'progress', 'progress': progress})
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def callback(progress):
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socketio.emit('install_progress',{'status': 'progress', 'progress': progress})
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self.download_file(model_path, installation_path, callback)
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socketio.emit('install_progress',{'status': 'succeeded', 'error': ''})
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tpe = threading.Thread(target=install_model_, args=())
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tpe.start()
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@socketio.on('uninstall_model')
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def uninstall_model(data):
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model_path = data['path']
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installation_dir = Path(f'./models/{self.config["backend"]}/')
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filename = Path(model_path).name
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installation_path = installation_dir / filename
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if not installation_path.exists():
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socketio.emit('install_progress',{'status': 'failed', 'error': 'The model does not exist'})
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installation_path.unlink()
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socketio.emit('install_progress',{'status': 'succeeded', 'error': ''})
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@socketio.on('generate_msg')
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def generate_msg(data):
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if self.process.model_ready.value==1:
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if self.current_discussion is None:
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if self.db.does_last_discussion_have_messages():
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self.current_discussion = self.db.create_discussion()
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else:
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self.current_discussion = self.db.load_last_discussion()
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message = data["prompt"]
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message_id = self.current_discussion.add_message(
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"user", message, parent=self.message_id
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)
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self.current_user_message_id = message_id
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tpe = threading.Thread(target=self.start_message_generation, args=(message, message_id))
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tpe.start()
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else:
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self.socketio.emit('infos',
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{
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"status":'model_not_ready',
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"type": "input_message_infos",
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'logo': "",
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"bot": self.personality.name,
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"user": self.personality.user_name,
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"message":"",
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"user_message_id": self.current_user_message_id,
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"ai_message_id": self.current_ai_message_id,
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}
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)
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@socketio.on('generate_msg_from')
|
|
def handle_connection(data):
|
|
message_id = int(data['id'])
|
|
message = data["prompt"]
|
|
self.current_user_message_id = message_id
|
|
tpe = threading.Thread(target=self.start_message_generation, args=(message, message_id))
|
|
tpe.start()
|
|
# generation status
|
|
self.generating=False
|
|
|
|
#properties
|
|
@property
|
|
def message_id(self):
|
|
return self._message_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:
|
|
percentage = (downloaded_size / total_size) * 100
|
|
callback(percentage)
|
|
progress_bar.update(len(chunk))
|
|
|
|
if callback is not None:
|
|
callback(100.0)
|
|
|
|
print("File downloaded successfully")
|
|
except Exception as e:
|
|
print("Couldn't download file:", str(e))
|
|
|
|
|
|
|
|
def condition_chatbot(self):
|
|
if self.current_discussion is None:
|
|
self.current_discussion = self.db.load_last_discussion()
|
|
|
|
if self.personality.welcome_message!="":
|
|
message_id = self.current_discussion.add_message(
|
|
self.personality.name, self.personality.welcome_message,
|
|
DiscussionsDB.MSG_TYPE_NORMAL,
|
|
0,
|
|
-1
|
|
)
|
|
|
|
self.current_ai_message_id = message_id
|
|
else:
|
|
message_id = 0
|
|
return message_id
|
|
|
|
def prepare_reception(self):
|
|
self.bot_says = ""
|
|
self.full_text = ""
|
|
self.is_bot_text_started = False
|
|
|
|
def create_new_discussion(self, title):
|
|
self.current_discussion = self.db.create_discussion(title)
|
|
# Get the current timestamp
|
|
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
|
|
|
# Chatbot conditionning
|
|
self.condition_chatbot()
|
|
return timestamp
|
|
|
|
def prepare_query(self, message_id=-1):
|
|
messages = self.current_discussion.get_messages()
|
|
self.full_message_list = []
|
|
for message in messages:
|
|
if message["id"]< message_id or message_id==-1:
|
|
if message["type"]==self.db.MSG_TYPE_NORMAL:
|
|
if message["sender"]==self.personality.name:
|
|
self.full_message_list.append(self.personality.ai_message_prefix+message["content"])
|
|
else:
|
|
self.full_message_list.append(self.personality.user_message_prefix + message["content"])
|
|
else:
|
|
break
|
|
|
|
if self.personality.processor is not None:
|
|
preprocessed_prompt = self.personality.processor.process_model_input(message["content"])
|
|
else:
|
|
preprocessed_prompt = message["content"]
|
|
if preprocessed_prompt is not None:
|
|
self.full_message_list.append(self.personality.user_message_prefix+preprocessed_prompt+self.personality.link_text+self.personality.ai_message_prefix)
|
|
else:
|
|
self.full_message_list.append(self.personality.user_message_prefix+message["content"]+self.personality.link_text+self.personality.ai_message_prefix)
|
|
|
|
|
|
link_text = self.personality.link_text
|
|
|
|
if len(self.full_message_list) > self.config["nb_messages_to_remember"]:
|
|
discussion_messages = self.personality.personality_conditioning+ link_text.join(self.full_message_list[-self.config["nb_messages_to_remember"]:])
|
|
else:
|
|
discussion_messages = self.personality.personality_conditioning+ link_text.join(self.full_message_list)
|
|
|
|
return discussion_messages, message["content"]
|
|
|
|
def get_discussion_to(self, message_id=-1):
|
|
messages = self.current_discussion.get_messages()
|
|
self.full_message_list = []
|
|
for message in messages:
|
|
if message["id"]<= message_id or message_id==-1:
|
|
if message["type"]!=self.db.MSG_TYPE_CONDITIONNING:
|
|
if message["sender"]==self.personality.name:
|
|
self.full_message_list.append(self.personality.ai_message_prefix+message["content"])
|
|
else:
|
|
self.full_message_list.append(self.personality.user_message_prefix + message["content"])
|
|
|
|
link_text = self.personality.link_text
|
|
|
|
if len(self.full_message_list) > self.config["nb_messages_to_remember"]:
|
|
discussion_messages = self.personality.personality_conditioning+ link_text.join(self.full_message_list[-self.config["nb_messages_to_remember"]:])
|
|
else:
|
|
discussion_messages = self.personality.personality_conditioning+ link_text.join(self.full_message_list)
|
|
|
|
return discussion_messages # Removes the last return
|
|
|
|
|
|
def remove_text_from_string(self, string, text_to_find):
|
|
"""
|
|
Removes everything from the first occurrence of the specified text in the string (case-insensitive).
|
|
|
|
Parameters:
|
|
string (str): The original string.
|
|
text_to_find (str): The text to find in the string.
|
|
|
|
Returns:
|
|
str: The updated string.
|
|
"""
|
|
index = string.lower().find(text_to_find.lower())
|
|
|
|
if index != -1:
|
|
string = string[:index]
|
|
|
|
return string
|
|
|
|
def process_chunk(self, chunk):
|
|
print(chunk,end="", flush=True)
|
|
self.bot_says += chunk
|
|
if not self.personality.detect_antiprompt(self.bot_says):
|
|
self.socketio.emit('message', {
|
|
'data': self.bot_says,
|
|
'user_message_id':self.current_user_message_id,
|
|
'ai_message_id':self.current_ai_message_id,
|
|
'discussion_id':self.current_discussion.discussion_id
|
|
}
|
|
)
|
|
if self.cancel_gen:
|
|
print("Generation canceled")
|
|
self.process.cancel_generation()
|
|
self.cancel_gen = False
|
|
else:
|
|
self.bot_says = self.remove_text_from_string(self.bot_says, self.personality.user_message_prefix.strip())
|
|
self.process.cancel_generation()
|
|
print("The model is halucinating")
|
|
|
|
def start_message_generation(self, message, message_id):
|
|
bot_says = ""
|
|
|
|
# send the message to the bot
|
|
print(f"Received message : {message}")
|
|
if self.current_discussion:
|
|
# First we need to send the new message ID to the client
|
|
self.current_ai_message_id = self.current_discussion.add_message(
|
|
self.personality.name, "", parent = self.current_user_message_id
|
|
) # first the content is empty, but we'll fill it at the end
|
|
self.socketio.emit('infos',
|
|
{
|
|
"status":'generation_started',
|
|
"type": "input_message_infos",
|
|
"bot": self.personality.name,
|
|
"user": self.personality.user_name,
|
|
"message":message,#markdown.markdown(message),
|
|
"user_message_id": self.current_user_message_id,
|
|
"ai_message_id": self.current_ai_message_id,
|
|
}
|
|
)
|
|
|
|
# prepare query and reception
|
|
self.discussion_messages, self.current_message = self.prepare_query(message_id)
|
|
self.prepare_reception()
|
|
self.generating = True
|
|
print(">Generating message")
|
|
self.process.generate(self.discussion_messages, self.current_message, message_id, n_predict = self.config['n_predict'])
|
|
self.process.started_queue.get()
|
|
while(self.process.is_generating.value): # Simulating other commands being issued
|
|
chunk = ""
|
|
while not self.process.generation_queue.empty():
|
|
chk, tok, message_type = self.process.generation_queue.get()
|
|
chunk += chk
|
|
if chunk!="":
|
|
self.process_chunk(chunk)
|
|
|
|
print()
|
|
print("## Done ##")
|
|
print()
|
|
|
|
# Send final message
|
|
self.socketio.emit('final', {
|
|
'data': self.bot_says,
|
|
'ai_message_id':self.current_ai_message_id,
|
|
'parent':self.current_user_message_id, 'discussion_id':self.current_discussion.discussion_id
|
|
}
|
|
)
|
|
|
|
self.current_discussion.update_message(self.current_ai_message_id, self.bot_says)
|
|
self.full_message_list.append(self.bot_says)
|
|
self.cancel_gen = False
|
|
return bot_says
|
|
else:
|
|
#No discussion available
|
|
print("No discussion selected!!!")
|
|
print("## Done ##")
|
|
print()
|
|
self.cancel_gen = False
|
|
return ""
|
|
|