mirror of
https://github.com/ParisNeo/lollms-webui.git
synced 2024-12-30 09:08:51 +00:00
444 lines
15 KiB
Python
444 lines
15 KiB
Python
import argparse
|
|
import json
|
|
import re
|
|
import traceback
|
|
from datetime import datetime
|
|
from concurrent.futures import ThreadPoolExecutor
|
|
import sys
|
|
from db import DiscussionsDB, Discussion
|
|
from flask import (
|
|
Flask,
|
|
Response,
|
|
jsonify,
|
|
render_template,
|
|
request,
|
|
stream_with_context,
|
|
send_from_directory
|
|
)
|
|
from pyllamacpp.model import Model
|
|
from queue import Queue
|
|
from pathlib import Path
|
|
import gc
|
|
app = Flask("GPT4All-WebUI", static_url_path="/static", static_folder="static")
|
|
import time
|
|
|
|
class Gpt4AllWebUI:
|
|
|
|
def __init__(self, _app, args) -> None:
|
|
self.args = args
|
|
self.current_discussion = None
|
|
self.app = _app
|
|
self.db_path = args.db_path
|
|
self.db = DiscussionsDB(self.db_path)
|
|
# If the database is empty, populate it with tables
|
|
self.db.populate()
|
|
|
|
# workaround for non interactive mode
|
|
self.full_message = ""
|
|
|
|
# This is the queue used to stream text to the ui as the bot spits out its response
|
|
self.text_queue = Queue(0)
|
|
|
|
self.add_endpoint(
|
|
"/list_models", "list_models", self.list_models, methods=["GET"]
|
|
)
|
|
self.add_endpoint(
|
|
"/list_discussions", "list_discussions", self.list_discussions, methods=["GET"]
|
|
)
|
|
|
|
|
|
self.add_endpoint("/", "", self.index, methods=["GET"])
|
|
self.add_endpoint("/export_discussion", "export_discussion", self.export_discussion, methods=["GET"])
|
|
self.add_endpoint("/export", "export", self.export, methods=["GET"])
|
|
self.add_endpoint(
|
|
"/new_discussion", "new_discussion", self.new_discussion, methods=["GET"]
|
|
)
|
|
self.add_endpoint("/bot", "bot", self.bot, methods=["POST"])
|
|
self.add_endpoint("/rename", "rename", self.rename, methods=["POST"])
|
|
self.add_endpoint(
|
|
"/load_discussion", "load_discussion", self.load_discussion, methods=["POST"]
|
|
)
|
|
self.add_endpoint(
|
|
"/delete_discussion",
|
|
"delete_discussion",
|
|
self.delete_discussion,
|
|
methods=["POST"],
|
|
)
|
|
|
|
self.add_endpoint(
|
|
"/update_message", "update_message", self.update_message, methods=["GET"]
|
|
)
|
|
self.add_endpoint(
|
|
"/message_rank_up", "message_rank_up", self.message_rank_up, methods=["GET"]
|
|
)
|
|
self.add_endpoint(
|
|
"/message_rank_down", "message_rank_down", self.message_rank_down, methods=["GET"]
|
|
)
|
|
|
|
self.add_endpoint(
|
|
"/update_model_params", "update_model_params", self.update_model_params, methods=["POST"]
|
|
)
|
|
|
|
self.add_endpoint(
|
|
"/get_args", "get_args", self.get_args, methods=["GET"]
|
|
)
|
|
|
|
self.prepare_a_new_chatbot()
|
|
|
|
def list_models(self):
|
|
models_dir = Path('./models') # replace with the actual path to the models folder
|
|
models = [f.name for f in models_dir.glob('*.bin')]
|
|
return jsonify(models)
|
|
|
|
def list_discussions(self):
|
|
discussions = self.db.get_discussions()
|
|
return jsonify(discussions)
|
|
|
|
|
|
def prepare_a_new_chatbot(self):
|
|
# Create chatbot
|
|
self.chatbot_bindings = self.create_chatbot()
|
|
# Chatbot conditionning
|
|
self.condition_chatbot()
|
|
|
|
|
|
def create_chatbot(self):
|
|
return Model(
|
|
ggml_model=f"./models/{self.args.model}",
|
|
n_ctx=self.args.ctx_size,
|
|
seed=self.args.seed,
|
|
)
|
|
|
|
def condition_chatbot(self, conditionning_message = """
|
|
Instruction: Act as GPT4All. A kind and helpful AI bot built to help users solve problems.
|
|
GPT4All:Welcome! I'm here to assist you with anything you need. What can I do for you today?"""
|
|
):
|
|
self.full_message += conditionning_message
|
|
if self.current_discussion is None:
|
|
if self.db.does_last_discussion_have_messages():
|
|
self.current_discussion = self.db.create_discussion()
|
|
else:
|
|
self.current_discussion = self.db.load_last_discussion()
|
|
|
|
message_id = self.current_discussion.add_message(
|
|
"conditionner", conditionning_message, DiscussionsDB.MSG_TYPE_CONDITIONNING,0
|
|
)
|
|
|
|
|
|
|
|
|
|
# self.prepare_query(conditionning_message)
|
|
# self.chatbot_bindings.generate(
|
|
# conditionning_message,
|
|
# new_text_callback=self.new_text_callback,
|
|
|
|
# n_predict=len(conditionning_message),
|
|
# temp=self.args.temp,
|
|
# top_k=self.args.top_k,
|
|
# top_p=self.args.top_p,
|
|
# repeat_penalty=self.args.repeat_penalty,
|
|
# repeat_last_n = self.args.repeat_last_n,
|
|
# seed=self.args.seed,
|
|
# n_threads=8
|
|
# )
|
|
# print(f"Bot said:{self.bot_says}")
|
|
|
|
|
|
def prepare_query(self, message):
|
|
self.bot_says = ""
|
|
self.full_text = ""
|
|
self.is_bot_text_started = False
|
|
self.current_message = message
|
|
|
|
def new_text_callback(self, text: str):
|
|
print(text, end="")
|
|
sys.stdout.flush()
|
|
self.full_text += text
|
|
if self.is_bot_text_started:
|
|
self.bot_says += text
|
|
self.full_message += text
|
|
self.text_queue.put(text)
|
|
if self.current_message in self.full_text:
|
|
self.is_bot_text_started = True
|
|
|
|
def add_endpoint(
|
|
self,
|
|
endpoint=None,
|
|
endpoint_name=None,
|
|
handler=None,
|
|
methods=["GET"],
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
self.app.add_url_rule(
|
|
endpoint, endpoint_name, handler, methods=methods, *args, **kwargs
|
|
)
|
|
|
|
def index(self):
|
|
return render_template("chat.html")
|
|
|
|
def format_message(self, message):
|
|
# Look for a code block within the message
|
|
pattern = re.compile(r"(```.*?```)", re.DOTALL)
|
|
match = pattern.search(message)
|
|
|
|
# If a code block is found, replace it with a <code> tag
|
|
if match:
|
|
code_block = match.group(1)
|
|
message = message.replace(code_block, f"<code>{code_block[3:-3]}</code>")
|
|
|
|
# Return the formatted message
|
|
return message
|
|
|
|
def export(self):
|
|
return jsonify(self.db.export_to_json())
|
|
|
|
def export_discussion(self):
|
|
return jsonify(self.full_message)
|
|
|
|
def generate_message(self):
|
|
self.generating=True
|
|
self.text_queue=Queue()
|
|
gc.collect()
|
|
|
|
self.chatbot_bindings.generate(
|
|
self.full_message,#self.current_message,
|
|
new_text_callback=self.new_text_callback,
|
|
n_predict=len(self.current_message)+self.args.n_predict,
|
|
temp=self.args.temp,
|
|
top_k=self.args.top_k,
|
|
top_p=self.args.top_p,
|
|
repeat_penalty=self.args.repeat_penalty,
|
|
repeat_last_n = self.args.repeat_last_n,
|
|
#seed=self.args.seed,
|
|
n_threads=8
|
|
)
|
|
self.generating=False
|
|
|
|
@stream_with_context
|
|
def parse_to_prompt_stream(self, message, message_id):
|
|
bot_says = ""
|
|
self.stop = False
|
|
|
|
# send the message to the bot
|
|
print(f"Received message : {message}")
|
|
# First we need to send the new message ID to the client
|
|
response_id = self.current_discussion.add_message(
|
|
"GPT4All", ""
|
|
) # first the content is empty, but we'll fill it at the end
|
|
yield (
|
|
json.dumps(
|
|
{
|
|
"type": "input_message_infos",
|
|
"message": message,
|
|
"id": message_id,
|
|
"response_id": response_id,
|
|
}
|
|
)
|
|
)
|
|
|
|
self.current_message = "\nUser: " + message + "\nGPT4All: "
|
|
self.full_message += self.current_message
|
|
self.prepare_query(self.full_message)
|
|
self.generating = True
|
|
app.config['executor'].submit(self.generate_message)
|
|
while self.generating or not self.text_queue.empty():
|
|
try:
|
|
value = self.text_queue.get(False)
|
|
yield value
|
|
except :
|
|
time.sleep(1)
|
|
|
|
|
|
|
|
self.current_discussion.update_message(response_id, self.bot_says)
|
|
#yield self.bot_says# .encode('utf-8').decode('utf-8')
|
|
# TODO : change this to use the yield version in order to send text word by word
|
|
|
|
return "\n".join(bot_says)
|
|
|
|
def bot(self):
|
|
self.stop = True
|
|
|
|
if self.current_discussion is None:
|
|
if self.db.does_last_discussion_have_messages():
|
|
self.current_discussion = self.db.create_discussion()
|
|
else:
|
|
self.current_discussion = self.db.load_last_discussion()
|
|
|
|
message_id = self.current_discussion.add_message(
|
|
"user", request.json["message"]
|
|
)
|
|
message = f"{request.json['message']}"
|
|
|
|
# Segmented (the user receives the output as it comes)
|
|
# We will first send a json entry that contains the message id and so on, then the text as it goes
|
|
return Response(
|
|
stream_with_context(
|
|
self.parse_to_prompt_stream(message, message_id)
|
|
)
|
|
)
|
|
|
|
def rename(self):
|
|
data = request.get_json()
|
|
title = data["title"]
|
|
self.current_discussion.rename(title)
|
|
return "renamed successfully"
|
|
|
|
def restore_discussion(self, full_message):
|
|
self.chatbot_bindings.generate(
|
|
full_message,
|
|
new_text_callback=self.new_text_callback,
|
|
n_predict=0,#len(full_message),
|
|
temp=self.args.temp,
|
|
top_k=self.args.top_k,
|
|
top_p=self.args.top_p,
|
|
repeat_penalty= self.args.repeat_penalty,
|
|
repeat_last_n = self.args.repeat_last_n,
|
|
n_threads=8
|
|
)
|
|
|
|
def load_discussion(self):
|
|
data = request.get_json()
|
|
discussion_id = data["id"]
|
|
self.current_discussion = Discussion(discussion_id, self.db)
|
|
messages = self.current_discussion.get_messages()
|
|
|
|
self.full_message = ""
|
|
for message in messages:
|
|
self.full_message += message['sender'] + ": " + message['content'] + "\n"
|
|
app.config['executor'].submit(self.restore_discussion, self.full_message)
|
|
|
|
return jsonify(messages)
|
|
|
|
def delete_discussion(self):
|
|
data = request.get_json()
|
|
discussion_id = data["id"]
|
|
self.current_discussion = Discussion(discussion_id, self.db)
|
|
self.current_discussion.delete_discussion()
|
|
self.current_discussion = None
|
|
return jsonify({})
|
|
|
|
def update_message(self):
|
|
discussion_id = request.args.get("id")
|
|
new_message = request.args.get("message")
|
|
self.current_discussion.update_message(discussion_id, new_message)
|
|
return jsonify({"status": "ok"})
|
|
|
|
def message_rank_up(self):
|
|
discussion_id = request.args.get("id")
|
|
new_rank = self.current_discussion.message_rank_up(discussion_id)
|
|
return jsonify({"new_rank": new_rank})
|
|
|
|
def message_rank_down(self):
|
|
discussion_id = request.args.get("id")
|
|
new_rank = self.current_discussion.message_rank_down(discussion_id)
|
|
return jsonify({"new_rank": new_rank})
|
|
|
|
def new_discussion(self):
|
|
title = request.args.get("title")
|
|
self.current_discussion = self.db.create_discussion(title)
|
|
# Get the current timestamp
|
|
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
|
|
|
app.config['executor'].submit(self.prepare_a_new_chatbot)
|
|
# Return a success response
|
|
return json.dumps({"id": self.current_discussion.discussion_id, "time": timestamp})
|
|
|
|
def update_model_params(self):
|
|
data = request.get_json()
|
|
model = str(data["model"])
|
|
if self.args.model != model:
|
|
print("New model selected")
|
|
self.args.model = model
|
|
self.prepare_a_new_chatbot()
|
|
|
|
self.args.n_predict = int(data["nPredict"])
|
|
self.args.seed = int(data["seed"])
|
|
|
|
self.args.temp = float(data["temp"])
|
|
self.args.top_k = int(data["topK"])
|
|
self.args.top_p = float(data["topP"])
|
|
self.args.repeat_penalty = int(data["repeatPenalty"])
|
|
self.args.repeat_last_n = int(data["repeatLastN"])
|
|
|
|
print("Parameters changed to:")
|
|
print(f"\tTemperature:{self.args.temp}")
|
|
print(f"\tNPredict:{self.args.n_predict}")
|
|
print(f"\tSeed:{self.args.seed}")
|
|
print(f"\top_k:{self.args.top_k}")
|
|
print(f"\top_p:{self.args.top_p}")
|
|
print(f"\trepeat_penalty:{self.args.repeat_penalty}")
|
|
print(f"\trepeat_last_n:{self.args.repeat_last_n}")
|
|
return jsonify({"status":"ok"})
|
|
|
|
|
|
def get_args(self):
|
|
return jsonify(self.args)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser(description="Start the chatbot Flask app.")
|
|
parser.add_argument(
|
|
"-s", "--seed", type=int, default=0, help="Force using a specific model."
|
|
)
|
|
|
|
parser.add_argument(
|
|
"-m", "--model", type=str, default="gpt4all-lora-quantized-ggml.bin", help="Force using a specific model."
|
|
)
|
|
parser.add_argument(
|
|
"--temp", type=float, default=0.1, help="Temperature parameter for the model."
|
|
)
|
|
parser.add_argument(
|
|
"--n_predict",
|
|
type=int,
|
|
default=256,
|
|
help="Number of tokens to predict at each step.",
|
|
)
|
|
parser.add_argument(
|
|
"--top_k", type=int, default=40, help="Value for the top-k sampling."
|
|
)
|
|
parser.add_argument(
|
|
"--top_p", type=float, default=0.95, help="Value for the top-p sampling."
|
|
)
|
|
parser.add_argument(
|
|
"--repeat_penalty", type=float, default=1.3, help="Penalty for repeated tokens."
|
|
)
|
|
parser.add_argument(
|
|
"--repeat_last_n",
|
|
type=int,
|
|
default=64,
|
|
help="Number of previous tokens to consider for the repeat penalty.",
|
|
)
|
|
parser.add_argument(
|
|
"--ctx_size",
|
|
type=int,
|
|
default=512,#2048,
|
|
help="Size of the context window for the model.",
|
|
)
|
|
parser.add_argument(
|
|
"--debug",
|
|
dest="debug",
|
|
action="store_true",
|
|
help="launch Flask server in debug mode",
|
|
)
|
|
parser.add_argument(
|
|
"--host", type=str, default="localhost", help="the hostname to listen on"
|
|
)
|
|
parser.add_argument("--port", type=int, default=9600, help="the port to listen on")
|
|
parser.add_argument(
|
|
"--db_path", type=str, default="database.db", help="Database path"
|
|
)
|
|
parser.set_defaults(debug=False)
|
|
args = parser.parse_args()
|
|
|
|
executor = ThreadPoolExecutor(max_workers=2)
|
|
app.config['executor'] = executor
|
|
|
|
bot = Gpt4AllWebUI(app, args)
|
|
|
|
if args.debug:
|
|
app.run(debug=True, host=args.host, port=args.port)
|
|
else:
|
|
app.run(host=args.host, port=args.port)
|