"""
deploy-as-bot\gradio_chatbot.py
A system, method for deploying to Gradio. Gradio is a basic "deploy" interface which allows for other users to test your model from a web URL. It also enables some basic functionality like user flagging for weird responses.
Note that the URL is displayed once the script is run.
Set the working directory to */deploy-as-bot in terminal before running.
"""
import os
import sys
from os.path import dirname
sys.path.append(dirname(dirname(os.path.abspath(__file__))))
import gradio as gr
import logging
import argparse
import time
import warnings
from pathlib import Path
from cleantext import clean
from transformers import pipeline
from datetime import datetime
from ai_single_response import query_gpt_model
#from gradio.networking import get_state, set_state
from flask import Flask, request, session, jsonify, abort, send_file, render_template, redirect
import nltk
nltk.download('stopwords')
warnings.filterwarnings(action="ignore", message=".*gradient_checkpointing*")
logging.basicConfig()
cwd = Path.cwd()
my_cwd = str(cwd.resolve()) # string so it can be passed to os.path() objects
def gramformer_correct(corrector, qphrase: str):
"""
gramformer_correct - correct a string using a text2textgen pipeline model from transformers
Args:
corrector (transformers.pipeline): [transformers pipeline object, already created w/ relevant model]
qphrase (str): [text to be corrected]
Returns:
[str]: [corrected text]
"""
try:
corrected = corrector(
clean(qphrase), return_text=True, clean_up_tokenization_spaces=True
)
return corrected[0]["generated_text"]
except:
print("NOTE - failed to correct with gramformer")
return clean(qphrase)
def ask_gpt(message: str, sender: str = ""):
"""
ask_gpt - queries the relevant model with a prompt message and (optional) speaker name
Args:
message (str): prompt message to respond to
sender (str, optional): speaker aka who said the message. Defaults to "".
Returns:
[str]: [model response as a string]
"""
st = time.time()
prompt = clean(message) # clean user input
prompt = prompt.strip() # get rid of any extra whitespace
if len(prompt) > 200:
prompt = prompt[-200:] # truncate
sender = clean(sender.strip())
if len(sender) > 2:
try:
prompt_speaker = clean(sender)
except:
# there was some issue getting that info, whatever
prompt_speaker = None
else:
prompt_speaker = None
resp = query_gpt_model(
folder_path=model_loc,
prompt_msg=prompt,
speaker=prompt_speaker,
kparam=150,
temp=0.75,
top_p=0.65, # optimize this with hyperparam search
)
bot_resp = gramformer_correct(corrector, qphrase=resp["out_text"])
rt = round(time.time() - st, 2)
print(f"took {rt} sec to respond")
return bot_resp
def chat(first_and_last_name, message):
"""
chat - helper function that makes the whole gradio thing work.
Args:
first_and_last_name (str or None): [speaker of the prompt, if provided]
message (str): [description]
Returns:
[str]: [returns an html string to display]
"""
history = []
response = ask_gpt(message, sender=first_and_last_name)
history.append((f"{first_and_last_name}: " + message, " GPT-Model: " + response)) #+ " [end] "))
#html = "<div class='chatbot'>"
#for user_msg, resp_msg in history:
# html += f"<div class='user_msg'>{user_msg}</div>"
# html += f"<div class='resp_msg' style='color: black'>{resp_msg}</div>"
#html += "</div>"
return history
def get_parser():
"""
get_parser - a helper function for the argparse module
Returns:
[argparse.ArgumentParser]: [the argparser relevant for this script]
"""
parser = argparse.ArgumentParser(
description="submit a message and have a 774M parameter GPT model respond"
)
parser.add_argument(
"--model",
required=False,
type=str,
# "gp2_DDandPeterTexts_774M_73Ksteps", - from GPT-Peter
default="GPT2_trivNatQAdailydia_774M_175Ksteps",
help="folder - with respect to git directory of your repo that has the model files in it (pytorch.bin + "
"config.json). No models? Run the script download_models.py",
)
parser.add_argument(
"--gram-model",
required=False,
type=str,
default="pszemraj/t5-v1_1-base-ft-jflAUG",
help="text2text generation model ID from huggingface for the model to correct grammar",
)
return parser
if __name__ == "__main__":
args = get_parser().parse_args()
default_model = str(args.model)
model_loc = cwd.parent / default_model
model_loc = str(model_loc.resolve())
gram_model = args.gram_model
print(f"using model stored here: \n {model_loc} \n")
corrector = pipeline("text2text-generation", model=gram_model, device=-1)
print("Finished loading the gramformer model - ", datetime.now())
iface = gr.Interface(
chat,
inputs=["text", "text"],
outputs="html",
title="Real-Impact English Chat Demo 英语聊天演示",
description="A basic interface with a neural network model trained on general Q&A and conversation. Treat it like a friend! 带有模型的基本界面，进行了一般问答和对话训练。 请像朋友一样与他对话！ \n first and last name 姓名 \n message 信息 \n Clear 清除 \nSubmit 确认 \n Screenshot 截屏",
article="**Important Notes & About: 重要说明 & 关于我们**\n"
"1. the model can take up to 200 seconds to respond sometimes, patience is a virtue. 该模型有时可能需要长达 60 秒的响应时间，请耐心等待。\n"
"2. entering a username is completely optional. 姓名输入是可选的。\n "
"3. the model was trained on several different datasets. Anything it says should be fact-checked before being regarded as a true statement. 该模型在几个不同的数据集上训练而成，它所说的任何内容都应该经过事实核查，然后才能被视为真实陈述。\n ",
css="""
.chatbox {display:flex;flex-direction:column}
.user_msg, .resp_msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%}
.user_msg {background-color:cornflowerblue;color:white;align-self:start}
.resp_msg {background-color:lightgray;align-self:self-end}
""",
allow_screenshot=True,
allow_flagging=False,
flagging_dir="gradio_data",
flagging_options=[
"great response",
"doesn't make sense",
"bad/offensive response",
],
enable_queue=True, # allows for dealing with multiple users simultaneously
#theme="darkhuggingface",
#server_name="0.0.0.0",
)
iface.launch(share=True)