mirror of
https://github.com/ParisNeo/lollms-webui.git
synced 2024-12-19 04:17:52 +00:00
439 lines
14 KiB
Python
439 lines
14 KiB
Python
import argparse
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import json
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import re
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import traceback
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from datetime import datetime
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from concurrent.futures import ThreadPoolExecutor
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import sys
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from db import Discussion, export_to_json, check_discussion_db, last_discussion_has_messages
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from flask import (
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Flask,
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Response,
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jsonify,
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render_template,
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request,
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stream_with_context,
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send_from_directory
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)
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from pyllamacpp.model import Model
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from queue import Queue
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from pathlib import Path
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import gc
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app = Flask("GPT4All-WebUI", static_url_path="/static", static_folder="static")
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import time
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class Gpt4AllWebUI:
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def __init__(self, _app, args) -> None:
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self.args = args
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self.current_discussion = None
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self.app = _app
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self.db_path = args.db_path
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# workaround for non interactive mode
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self.full_message = ""
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# This is the queue used to stream text to the ui as the bot spits out its response
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self.text_queue = Queue(0)
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self.add_endpoint("/", "", self.index, methods=["GET"])
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self.add_endpoint("/export", "export", self.export, methods=["GET"])
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self.add_endpoint(
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"/new_discussion", "new_discussion", self.new_discussion, methods=["GET"]
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)
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self.add_endpoint("/bot", "bot", self.bot, methods=["POST"])
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self.add_endpoint(
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"/discussions", "discussions", self.discussions, methods=["GET"]
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)
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self.add_endpoint("/rename", "rename", self.rename, methods=["POST"])
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self.add_endpoint(
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"/load_discussion", "load_discussion", self.load_discussion, methods=["POST"]
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)
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self.add_endpoint(
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"/delete_discussion",
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"delete_discussion",
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self.delete_discussion,
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methods=["POST"],
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)
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self.add_endpoint(
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"/update_message", "update_message", self.update_message, methods=["GET"]
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)
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self.add_endpoint(
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"/update_model_params", "update_model_params", self.update_model_params, methods=["POST"]
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)
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self.add_endpoint(
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"/list_models", "list_models", self.list_models, methods=["GET"]
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)
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self.add_endpoint(
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"/get_args", "get_args", self.get_args, methods=["GET"]
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)
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self.prepare_a_new_chatbot()
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def list_models(self):
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models_dir = Path('./models') # replace with the actual path to the models folder
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models = [f.name for f in models_dir.glob('*.bin')]
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return jsonify(models)
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def prepare_a_new_chatbot(self):
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# Create chatbot
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self.chatbot_bindings = self.create_chatbot()
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# Chatbot conditionning
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self.condition_chatbot()
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def create_chatbot(self):
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return Model(
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ggml_model=f"./models/{self.args.model}",
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n_ctx=self.args.ctx_size,
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seed=self.args.seed,
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)
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def condition_chatbot(self, conditionning_message = """
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Instruction: Act as GPT4All. A kind and helpful AI bot built to help users solve problems.
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Start by welcoming the user then stop sending text.
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GPT4All:Welcome! I'm here to assist you with anything you need. What can I do for you today?"""
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):
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self.full_message += conditionning_message +"\n"
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self.prepare_query(conditionning_message)
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self.chatbot_bindings.generate(
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conditionning_message,
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new_text_callback=self.new_text_callback,
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n_predict=len(conditionning_message),
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temp=self.args.temp,
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top_k=self.args.top_k,
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top_p=self.args.top_p,
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repeat_penalty=self.args.repeat_penalty,
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repeat_last_n = self.args.repeat_last_n,
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seed=self.args.seed,
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n_threads=8
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)
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print(f"Bot said:{self.bot_says}")
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def prepare_query(self, message):
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self.bot_says = ""
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self.full_text = ""
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self.is_bot_text_started = False
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self.current_message = message
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def new_text_callback(self, text: str):
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print(text, end="")
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sys.stdout.flush()
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self.full_text += text
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if self.is_bot_text_started:
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self.bot_says += text
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self.full_message += text
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self.text_queue.put(text)
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if self.current_message in self.full_text:
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self.is_bot_text_started = True
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def add_endpoint(
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self,
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endpoint=None,
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endpoint_name=None,
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handler=None,
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methods=["GET"],
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*args,
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**kwargs,
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):
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self.app.add_url_rule(
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endpoint, endpoint_name, handler, methods=methods, *args, **kwargs
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)
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def index(self):
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return render_template("chat.html")
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def format_message(self, message):
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# Look for a code block within the message
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pattern = re.compile(r"(```.*?```)", re.DOTALL)
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match = pattern.search(message)
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# If a code block is found, replace it with a <code> tag
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if match:
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code_block = match.group(1)
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message = message.replace(code_block, f"<code>{code_block[3:-3]}</code>")
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# Return the formatted message
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return message
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def export(self):
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return jsonify(export_to_json(self.db_path))
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def generate_message(self):
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self.generating=True
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self.text_queue=Queue()
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gc.collect()
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self.chatbot_bindings.generate(
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self.full_message,#self.current_message,
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new_text_callback=self.new_text_callback,
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n_predict=len(self.current_message)+self.args.n_predict,
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temp=self.args.temp,
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top_k=self.args.top_k,
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top_p=self.args.top_p,
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repeat_penalty=self.args.repeat_penalty,
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repeat_last_n = self.args.repeat_last_n,
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#seed=self.args.seed,
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n_threads=8
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)
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self.generating=False
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@stream_with_context
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def parse_to_prompt_stream(self, message, message_id):
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bot_says = ""
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self.stop = False
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# send the message to the bot
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print(f"Received message : {message}")
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# First we need to send the new message ID to the client
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response_id = self.current_discussion.add_message(
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"GPT4All", ""
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) # first the content is empty, but we'll fill it at the end
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yield (
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json.dumps(
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{
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"type": "input_message_infos",
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"message": message,
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"id": message_id,
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"response_id": response_id,
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}
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)
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)
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self.current_message = "\nUser: " + message + "\nGPT4All: "
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self.full_message += self.current_message
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self.prepare_query(self.full_message)
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self.generating = True
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app.config['executor'].submit(self.generate_message)
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while self.generating or not self.text_queue.empty():
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try:
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value = self.text_queue.get(False)
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yield value
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except :
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time.sleep(1)
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self.current_discussion.update_message(response_id, self.bot_says)
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#yield self.bot_says# .encode('utf-8').decode('utf-8')
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# TODO : change this to use the yield version in order to send text word by word
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return "\n".join(bot_says)
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def bot(self):
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self.stop = True
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try:
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if self.current_discussion is None or not last_discussion_has_messages(
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self.db_path
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):
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self.current_discussion = Discussion.create_discussion(self.db_path)
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message_id = self.current_discussion.add_message(
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"user", request.json["message"]
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)
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message = f"{request.json['message']}"
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# Segmented (the user receives the output as it comes)
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# We will first send a json entry that contains the message id and so on, then the text as it goes
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return Response(
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stream_with_context(
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self.parse_to_prompt_stream(message, message_id)
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)
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)
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except Exception as ex:
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print(ex)
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return (
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"<b style='color:red;'>Exception :<b>"
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+ str(ex)
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+ "<br>"
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+ traceback.format_exc()
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+ "<br>Please report exception"
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)
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def discussions(self):
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try:
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discussions = Discussion.get_discussions(self.db_path)
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return jsonify(discussions)
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except Exception as ex:
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print(ex)
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return (
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"<b style='color:red;'>Exception :<b>"
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+ str(ex)
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+ "<br>"
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+ traceback.format_exc()
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+ "<br>Please report exception"
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)
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def rename(self):
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data = request.get_json()
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discussion_id = data["id"]
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title = data["title"]
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Discussion.rename(self.db_path, discussion_id, title)
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return "renamed successfully"
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def restore_discussion(self, full_message):
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self.chatbot_bindings.generate(
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full_message,
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new_text_callback=self.new_text_callback,
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n_predict=0,#len(full_message),
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temp=self.args.temp,
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top_k=self.args.top_k,
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top_p=self.args.top_p,
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repeat_penalty= self.args.repeat_penalty,
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repeat_last_n = self.args.repeat_last_n,
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n_threads=8
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)
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def load_discussion(self):
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data = request.get_json()
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discussion_id = data["id"]
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self.current_discussion = Discussion(discussion_id, self.db_path)
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messages = self.current_discussion.get_messages()
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self.full_message = ""
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for message in messages:
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self.full_message += message['sender'] + ": " + message['content'] + "\n"
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app.config['executor'].submit(self.restore_discussion, self.full_message)
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return jsonify(messages)
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def delete_discussion(self):
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data = request.get_json()
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discussion_id = data["id"]
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self.current_discussion = Discussion(discussion_id, self.db_path)
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self.current_discussion.delete_discussion()
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self.current_discussion = None
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return jsonify({})
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def update_message(self):
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try:
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discussion_id = request.args.get("id")
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new_message = request.args.get("message")
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self.current_discussion.update_message(discussion_id, new_message)
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return jsonify({"status": "ok"})
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except Exception as ex:
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print(ex)
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return (
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"<b style='color:red;'>Exception :<b>"
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+ str(ex)
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+ "<br>"
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+ traceback.format_exc()
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+ "<br>Please report exception"
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)
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def new_discussion(self):
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title = request.args.get("title")
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self.current_discussion = Discussion.create_discussion(self.db_path, title)
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# Get the current timestamp
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timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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app.config['executor'].submit(self.prepare_a_new_chatbot)
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# Return a success response
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return json.dumps({"id": self.current_discussion.discussion_id, "time": timestamp})
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def update_model_params(self):
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data = request.get_json()
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model = str(data["model"])
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if self.args.model != model:
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print("New model selected")
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self.args.model = model
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self.prepare_a_new_chatbot()
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self.args.n_predict = int(data["nPredict"])
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self.args.seed = int(data["seed"])
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self.args.temp = float(data["temp"])
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self.args.top_k = int(data["topK"])
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self.args.top_p = float(data["topP"])
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self.args.repeat_penalty = int(data["repeatPenalty"])
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self.args.repeat_last_n = int(data["repeatLastN"])
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print("Parameters changed to:")
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print(f"\tTemperature:{self.args.temp}")
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print(f"\tNPredict:{self.args.n_predict}")
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print(f"\tSeed:{self.args.seed}")
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print(f"\top_k:{self.args.top_k}")
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print(f"\top_p:{self.args.top_p}")
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print(f"\trepeat_penalty:{self.args.repeat_penalty}")
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print(f"\trepeat_last_n:{self.args.repeat_last_n}")
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return jsonify({"status":"ok"})
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def get_args(self):
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return jsonify(self.args)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Start the chatbot Flask app.")
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parser.add_argument(
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"-s", "--seed", type=int, default=0, help="Force using a specific model."
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)
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parser.add_argument(
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"-m", "--model", type=str, default="gpt4all-lora-quantized-ggml.bin", help="Force using a specific model."
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)
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parser.add_argument(
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"--temp", type=float, default=0.1, help="Temperature parameter for the model."
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)
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parser.add_argument(
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"--n_predict",
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type=int,
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default=256,
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help="Number of tokens to predict at each step.",
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)
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parser.add_argument(
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"--top_k", type=int, default=40, help="Value for the top-k sampling."
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)
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parser.add_argument(
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"--top_p", type=float, default=0.95, help="Value for the top-p sampling."
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)
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parser.add_argument(
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"--repeat_penalty", type=float, default=1.3, help="Penalty for repeated tokens."
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)
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parser.add_argument(
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"--repeat_last_n",
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type=int,
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default=64,
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help="Number of previous tokens to consider for the repeat penalty.",
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)
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parser.add_argument(
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"--ctx_size",
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type=int,
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default=512,#2048,
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help="Size of the context window for the model.",
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)
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parser.add_argument(
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"--debug",
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dest="debug",
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action="store_true",
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help="launch Flask server in debug mode",
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)
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parser.add_argument(
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"--host", type=str, default="localhost", help="the hostname to listen on"
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)
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parser.add_argument("--port", type=int, default=9600, help="the port to listen on")
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parser.add_argument(
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"--db_path", type=str, default="database.db", help="Database path"
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)
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parser.set_defaults(debug=False)
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args = parser.parse_args()
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check_discussion_db(args.db_path)
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executor = ThreadPoolExecutor(max_workers=2)
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app.config['executor'] = executor
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bot = Gpt4AllWebUI(app, args)
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if args.debug:
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app.run(debug=True, host=args.host, port=args.port)
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else:
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app.run(host=args.host, port=args.port)
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