lollms-webui/pyGpt4All/api.py

######
# Project       : GPT4ALL-UI
# File          : api.py
# Author        : ParisNeo with the help of the community
# Supported by Nomic-AI
# Licence       : Apache 2.0
# Description   : 
# A simple api to communicate with gpt4all-ui and its models.
######
import gc
import sys
from datetime import datetime
from pyGpt4All.db import DiscussionsDB
from pathlib import Path
import importlib
from pyaipersonality import AIPersonality

__author__ = "parisneo"
__github__ = "https://github.com/nomic-ai/gpt4all-ui"
__copyright__ = "Copyright 2023, "
__license__ = "Apache 2.0"

class GPT4AllAPI():
    def __init__(self, config:dict, personality:AIPersonality, config_file_path:str) -> None:
        self.config = config
        self.personality = personality
        self.config_file_path = config_file_path
        self.cancel_gen = False

        # Keeping track of current discussion and message
        self.current_discussion = None
        self.current_message_id = 0

        self.db_path = config["db_path"]

        # Create database object
        self.db = DiscussionsDB(self.db_path)

        # If the database is empty, populate it with tables
        self.db.populate()

        # This is used to keep track of messages 
        self.full_message_list = []

        # Select backend
        self.BACKENDS_LIST = {f.stem:f for f in Path("backends").iterdir() if f.is_dir()  and f.stem!="__pycache__"}

        self.backend =self.load_backend(self.BACKENDS_LIST[self.config["backend"]])

        # Build chatbot
        self.chatbot_bindings = self.create_chatbot()
        print("Chatbot created successfully")
        # generation status
        self.generating=False

    def load_backend(self, backend_path):

        # define the full absolute path to the module
        absolute_path = backend_path.resolve()

        # infer the module name from the file path
        module_name = backend_path.stem

        # use importlib to load the module from the file path
        loader = importlib.machinery.SourceFileLoader(module_name, str(absolute_path/"__init__.py"))
        backend_module = loader.load_module()
        backend_class = getattr(backend_module, backend_module.backend_name)
        return backend_class

    def create_chatbot(self):
        return self.backend(self.config)
    
    def condition_chatbot(self, conditionning_message):
        if self.current_discussion is None:
            self.current_discussion = self.db.load_last_discussion()
        
        message_id = self.current_discussion.add_message(
            "conditionner", 
            conditionning_message, 
            DiscussionsDB.MSG_TYPE_CONDITIONNING,
            0,
            0
        )
        self.current_message_id = message_id
        if self.personality.welcome_message!="":
            if self.personality.welcome_message!="":
                message_id = self.current_discussion.add_message(
                    self.personality.name, self.personality.welcome_message, 
                    DiscussionsDB.MSG_TYPE_NORMAL,
                    0,
                    self.current_message_id
                )
        
            self.current_message_id = message_id
        return message_id

    def prepare_reception(self):
        self.bot_says = ""
        self.full_text = ""
        self.is_bot_text_started = False
        #self.current_message = message

    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(self.personality.personality_conditioning)
        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_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)
        
        discussion_messages += link_text + self.personality.ai_message_prefix
        return discussion_messages # Removes the last return

    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 new_text_callback(self, text: str):
        if self.cancel_gen:
            return False
        print(text, end="")
        sys.stdout.flush()
        
        self.bot_says += text
        if not self.personality.detect_antiprompt(self.bot_says):
            self.socketio.emit('message', {'data': self.bot_says})
            if self.cancel_gen:
                print("Generation canceled")
                self.cancel_gen = False
                return False
            else:
                return True
        else:
            self.bot_says = self.remove_text_from_string(self.bot_says, self.personality.user_message_prefix.strip())
            print("The model is halucinating")
            return False
        
    def generate_message(self):
        self.generating=True
        gc.collect()
        total_n_predict = self.config['n_predict']
        print(f"Generating {total_n_predict} outputs... ")
        print(f"Input text : {self.discussion_messages}")
        self.chatbot_bindings.generate(
            self.discussion_messages,
            new_text_callback=self.new_text_callback,
            n_predict=total_n_predict,
            temp=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']
        )
        self.generating=False