Create app.py

app.py

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+"""
+
+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.
+
+"""
+from utils import remove_trailing_punctuation, DisableLogger
+import os
+import sys
+from os.path import dirname
+
+# add the path to the script to the sys.path
+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 transformers import pipeline
+from datetime import datetime
+from ai_single_response import query_gpt_model
+
+logging.basicConfig(
+ filename=f"LOGFILE-{Path(__file__).stem}.log",
+ filemode="a",
+ format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+ level=logging.INFO,
+)
+
+with DisableLogger():
+ from cleantext import clean
+
+warnings.filterwarnings(action="ignore", message=".*gradient_checkpointing*")
+
+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
+ ) # fallback is to return the cleaned up version of the message
+
+
+def ask_gpt(message: str, sender: str = ""):
+ """
+ ask_gpt - queries the relevant model with a prompt message and (optional) speaker name.
+ nnote this version is modified w.r.t gradio local server deploy
+
+ 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) > 100:
+ prompt = prompt[:100] # truncate
+ sender = clean(sender.strip())
+ if len(sender) > 2:
+ try:
+ prompt_speaker = clean(sender)
+ except:
+ prompt_speaker = None # fallback
+ else:
+ prompt_speaker = None # fallback
+
+ resp = query_gpt_model(
+ folder_path=model_loc,
+ prompt_msg=prompt,
+ speaker=prompt_speaker,
+ kparam=150, # top k responses
+ temp=0.75, # temperature
+ top_p=0.65, # nucleus sampling
+ )
+ bot_resp = gramformer_correct(
+ corrector, qphrase=resp["out_text"]
+ ) # correct grammar
+ bot_resp = remove_trailing_punctuation(
+ bot_resp
+ ) # remove trailing punctuation to seem more natural
+ 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 = gr.get_state() or []
+ response = ask_gpt(message, sender=first_and_last_name)
+ history.append(("You: " + message, " GPT-Model: " + response + " [end] "))
+ gr.set_state(history) # save the history
+ html = ""
+ for user_msg, resp_msg in history:
+ html += f"{user_msg}"
+ html += f"{resp_msg}"
+ html += ""
+ return html
+
+
+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="host a chatbot on gradio",
+ )
+ parser.add_argument(
+ "--model",
+ required=False,
+ type=str,
+ default="GPT2_trivNatQAdailydia_774M_175Ksteps", # folder name of model
+ 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="prithivida/grammar_error_correcter_v1", # huggingface model
+ 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
+
+ # init items for the pipeline
+ iface = gr.Interface(
+ chat,
+ inputs=["text", "text"],
+ outputs="html",
+ title=f"GPT-Chatbot Demo: {default_model} Model",
+ description=f"A basic interface with a GPT2-based model, specifically {default_model}. Treat it like a friend!",
+ article="**Important Notes & About:**\n"
+ "1. the model can take up to 60 seconds to respond sometimes, patience is a virtue.\n"
+ "2. entering a username is completely optional.\n"
+ "3. the model started from a pretrained checkpoint, and was trained on several different datasets. Anything it says sshould be fact-checked before being regarded as a true statement.\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=True, # allow users to flag responses as inappropriate
+ 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",
+ )
+
+ corrector = pipeline("text2text-generation", model=gram_model, device=-1)
+ print("Finished loading the gramformer model - ", datetime.now())
+ print(f"using model stored here: \n {model_loc} \n")
+
+ # launch the gradio interface and start the server
+ iface.launch(share=True)