diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..87484612f67adef5b2e39375c711266b97d21ec3
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,12 @@
+# basics
+*__pycache__*
+
+# local testing
+*aitextgen*
+*scratch*
+*tmp*
+
+# gradio database files
+*gradio_db_files*
+*gradio*
+*flagged*
\ No newline at end of file
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..79ddb2cd4b09759e04191d6cd80e6fb0b10aa856
--- /dev/null
+++ b/app.py
@@ -0,0 +1,251 @@
+"""
+app.py - the main file for the app. This creates the flask app and handles the routes.
+
+"""
+
+import torch
+from transformers import pipeline
+from cleantext import clean
+from pathlib import Path
+import warnings
+import time
+import argparse
+import logging
+import gradio as gr
+import os
+import sys
+from os.path import dirname
+import nltk
+from converse import discussion
+from grammar_improve import (
+ detect_propers,
+ load_ns_checker,
+ neuspell_correct,
+ remove_repeated_words,
+ remove_trailing_punctuation,
+ build_symspell_obj,
+ symspeller,
+ fix_punct_spacing,
+)
+
+from utils import (
+ cleantxt_wrap,
+ corr,
+)
+
+nltk.download("stopwords") # TODO: find where this requirement originates from
+
+sys.path.append(dirname(dirname(os.path.abspath(__file__))))
+warnings.filterwarnings(action="ignore", message=".*gradient_checkpointing*")
+import transformers
+
+transformers.logging.set_verbosity_error()
+logging.basicConfig()
+cwd = Path.cwd()
+my_cwd = str(cwd.resolve()) # string so it can be passed to os.path() objects
+
+
+def chat(trivia_query):
+ """
+ chat - helper function that makes the whole gradio thing work.
+
+ Args:
+ trivia_query (str): the question to ask the bot
+
+ Returns:
+ [str]: the bot's response
+ """
+ history = []
+ response = ask_gpt(message=trivia_query, chat_pipe=my_chatbot)
+ history = [trivia_query, response]
+ html = ""
+ for item in history:
+ html += f"{item}
"
+
+ html += ""
+
+ return html
+
+
+def ask_gpt(
+ message: str,
+ chat_pipe,
+ speaker="person alpha",
+ responder="person beta",
+ max_len=196,
+ top_p=0.95,
+ top_k=50,
+ temperature=0.6,
+):
+ """
+
+ ask_gpt - a function that takes in a prompt and generates a response using the pipeline. This interacts the discussion function.
+
+ Parameters:
+ message (str): the question to ask the bot
+ chat_pipe (str): the chat_pipe to use for the bot (default: "pszemraj/Ballpark-Trivia-XL")
+ speaker (str): the name of the speaker (default: "person alpha")
+ responder (str): the name of the responder (default: "person beta")
+ max_len (int): the maximum length of the response (default: 128)
+ top_p (float): the top probability threshold (default: 0.95)
+ top_k (int): the top k threshold (default: 50)
+ temperature (float): the temperature of the response (default: 0.7)
+ """
+
+ st = time.perf_counter()
+ prompt = clean(message) # clean user input
+ prompt = prompt.strip() # get rid of any extra whitespace
+ in_len = len(prompt)
+ if in_len > 512:
+ prompt = prompt[-512:] # truncate to 512 chars
+ print(f"Truncated prompt to last 512 chars: started with {in_len} chars")
+ max_len = min(max_len, 512)
+
+ resp = discussion(
+ prompt_text=prompt,
+ pipeline=chat_pipe,
+ speaker=speaker,
+ responder=responder,
+ top_p=top_p,
+ top_k=top_k,
+ temperature=temperature,
+ max_length=max_len,
+ )
+ gpt_et = time.perf_counter()
+ gpt_rt = round(gpt_et - st, 2)
+ rawtxt = resp["out_text"]
+ # check for proper nouns
+ if basic_sc and not detect_propers(rawtxt):
+ cln_resp = symspeller(rawtxt, sym_checker=schnellspell)
+ elif not detect_propers(rawtxt):
+ cln_resp = neuspell_correct(rawtxt, checker=ns_checker)
+ else:
+ # no correction needed
+ cln_resp = rawtxt.strip()
+ bot_resp_a = corr(remove_repeated_words(cln_resp))
+ bot_resp = fix_punct_spacing(bot_resp_a)
+ print(f"the prompt was:\n\t{message}\nand the response was:\n\t{bot_resp}\n")
+ corr_rt = round(time.perf_counter() - gpt_et, 4)
+ print(
+ f"took {gpt_rt + corr_rt} sec to respond, {gpt_rt} for GPT, {corr_rt} for correction\n"
+ )
+ return remove_trailing_punctuation(bot_resp)
+
+
+def get_parser():
+ """
+ get_parser - a helper function for the argparse module
+ """
+ parser = argparse.ArgumentParser(
+ description="submit a question, GPT model responds"
+ )
+ parser.add_argument(
+ "-m",
+ "--model",
+ required=False,
+ type=str,
+ default="pszemraj/GPT-Converse-1pt3B-Neo-WoW-DD-17", # default model
+ help="the model to use for the chatbot on https://huggingface.co/models OR a path to a local model",
+ )
+ parser.add_argument(
+ "--basic-sc",
+ required=False,
+ default=True, # TODO: change this back to False once Neuspell issues are resolved.
+ action="store_true",
+ help="turn on symspell (baseline) correction instead of the more advanced neural net models",
+ )
+
+ parser.add_argument(
+ "--verbose",
+ action="store_true",
+ default=False,
+ help="turn on verbose logging",
+ )
+ return parser
+
+
+if __name__ == "__main__":
+ args = get_parser().parse_args()
+ default_model = str(args.model)
+ model_loc = Path(default_model) # if the model is a path, use it
+ basic_sc = args.basic_sc # whether to use the baseline spellchecker
+ device = 0 if torch.cuda.is_available() else -1
+ print(f"CUDA avail is {torch.cuda.is_available()}")
+
+ my_chatbot = (
+ pipeline("text-generation", model=model_loc.resolve(), device=device)
+ if model_loc.exists() and model_loc.is_dir()
+ else pipeline("text-generation", model=default_model, device=device)
+ ) # if the model is a name, use it. stays on CPU if no GPU available
+ print(f"using model {my_chatbot.model}")
+
+ if basic_sc:
+ print("Using the baseline spellchecker")
+ schnellspell = build_symspell_obj()
+ else:
+ print("using Neuspell spell checker")
+ ns_checker = load_ns_checker(fast=False)
+
+ print(f"using model stored here: \n {model_loc} \n")
+ iface = gr.Interface(
+ chat,
+ inputs=["text"],
+ outputs="html",
+ examples_per_page=10,
+ examples=[
+ "How can you help me?",
+ "what can you do?",
+ "Hi, my name is……",
+ "Happy birthday!",
+ "I have a question, can you help me?",
+ "Do you know a joke?",
+ "Will you marry me?",
+ "Are you single?",
+ "Do you like people?",
+ "Are you part of the Matrix?",
+ "Do you have a hobby?",
+ "You’re clever",
+ "Tell me about your personality",
+ "You’re annoying",
+ "you suck",
+ "I want to speak to a human now.",
+ "Don’t you speak English?!",
+ "Are you human?",
+ "Are you a robot?",
+ "What is your name?",
+ "How old are you?",
+ "What’s your age?",
+ "What day is it today?",
+ "Who made you?",
+ "Which languages can you speak?",
+ "What is your mother’s name?",
+ "Where do you live?",
+ "What’s the weather like today?",
+ "Are you expensive?",
+ "Do you get smarter?",
+ "rate your overall satisfaction with the chatbot",
+ "How many icebergs are in the ocean?",
+ ],
+ title=f"NLP template space: {default_model} Model",
+ description=f"this space is used as a template. please copy the files in the space to your own space repo, AND THEN edit them ",
+ article="here you can add more details about your model. \n\n"
+ "**Important Notes & About:**\n\n"
+ "1. the model can take up to 60 seconds to respond sometimes, patience is a virtue.\n"
+ "2. the model started from a pretrained checkpoint, and was trained on several different datasets. Anything it says should be fact-checked before being regarded as a true statement.\n"
+ "3. Some params are still being tweaked (in the future, will be inputs) any feedback is welcome :)\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="never",
+ theme="dark",
+ )
+
+ # launch the gradio interface and start the server
+ iface.launch(
+ # prevent_thread_lock=True,
+ enable_queue=True, # also allows for dealing with multiple users simultaneously (per newer gradio version)
+ )
diff --git a/converse.py b/converse.py
new file mode 100644
index 0000000000000000000000000000000000000000..a40128010802bdb6ae9a793e52b2dbf06909e1f3
--- /dev/null
+++ b/converse.py
@@ -0,0 +1,244 @@
+"""
+ converse.py - this script has functions for handling the conversation between the user and the bot.
+
+ https://huggingface.co/docs/transformers/v4.15.0/en/main_classes/model#transformers.generation_utils.GenerationMixin.generate.no_repeat_ngram_size
+"""
+
+
+import pprint as pp
+import time
+import torch
+import transformers
+
+from grammar_improve import remove_trailing_punctuation
+
+
+def discussion(
+ prompt_text: str,
+ speaker: str,
+ responder: str,
+ pipeline,
+ timeout=45,
+ max_length=128,
+ top_p=0.95,
+ top_k=50,
+ temperature=0.7,
+ full_text=False,
+ num_return_sequences=1,
+ device=-1,
+ verbose=False,
+):
+ """
+ discussion - a function that takes in a prompt and generates a response. This function is meant to be used in a conversation loop, and is the main function for the bot.
+
+ Parameters
+ ----------
+ prompt_text : str, the prompt to ask the bot, usually the user's question
+ speaker : str, the name of the person who is speaking the prompt
+ responder : str, the name of the person who is responding to the prompt
+ pipeline : transformers.Pipeline, the pipeline to use for generating the response
+ timeout : int, optional, the number of seconds to wait before timing out, by default 45
+ max_length : int, optional, the maximum number of tokens to generate, defaults to 128
+ top_p : float, optional, the top probability to use for sampling, defaults to 0.95
+ top_k : int, optional, the top k to use for sampling, defaults to 50
+ temperature : float, optional, the temperature to use for sampling, defaults to 0.7
+ full_text : bool, optional, whether to return the full text or just the generated text, defaults to False
+ num_return_sequences : int, optional, the number of sequences to return, defaults to 1
+ device : int, optional, the device to use for generation, defaults to -1 (CPU)
+ verbose : bool, optional, whether to print the generated text, defaults to False
+
+ Returns
+ -------
+ str, the generated text
+ """
+
+ p_list = [] # track conversation
+ p_list.append(speaker.lower() + ":" + "\n")
+ p_list.append(prompt_text.lower() + "\n")
+ p_list.append("\n")
+ p_list.append(responder.lower() + ":" + "\n")
+ this_prompt = "".join(p_list)
+ if verbose:
+ print("overall prompt:\n")
+ pp.pprint(this_prompt, indent=4)
+ # call the model
+ print("\n... generating...")
+ bot_dialogue = gen_response(
+ this_prompt,
+ pipeline,
+ speaker,
+ responder,
+ timeout=timeout,
+ max_length=max_length,
+ top_p=top_p,
+ top_k=top_k,
+ temperature=temperature,
+ full_text=full_text,
+ num_return_sequences=num_return_sequences,
+ device=device,
+ verbose=verbose,
+ )
+ if isinstance(bot_dialogue, list) and len(bot_dialogue) > 1:
+ bot_resp = ", ".join(bot_dialogue)
+ elif isinstance(bot_dialogue, list) and len(bot_dialogue) == 1:
+ bot_resp = bot_dialogue[0]
+ else:
+ bot_resp = bot_dialogue
+ bot_resp = " ".join(bot_resp) if isinstance(bot_resp, list) else bot_resp
+ bot_resp = bot_resp.strip()
+ # remove the last ',' '.' chars
+ bot_resp = remove_trailing_punctuation(bot_resp)
+ if verbose:
+ print("\n... bot response:\n")
+ pp.pprint(bot_resp)
+ p_list.append(bot_resp + "\n")
+ p_list.append("\n")
+
+ print("\nfinished!")
+ # return the bot response and the full conversation
+
+ return {"out_text": bot_resp, "full_conv": p_list}
+
+
+def gen_response(
+ query: str,
+ pipeline,
+ speaker: str,
+ responder: str,
+ timeout=45,
+ max_length=128,
+ top_p=0.95,
+ top_k=50,
+ temperature=0.7,
+ full_text=False,
+ num_return_sequences=1,
+ device=-1,
+ verbose=False,
+ **kwargs,
+):
+ """
+ gen_response - a function that takes in a prompt and generates a response using the pipeline. This operates underneath the discussion function.
+
+ Parameters
+ ----------
+ query : str, the prompt to ask the bot, usually the user's question
+ speaker : str, the name of the person who is speaking the prompt
+ responder : str, the name of the person who is responding to the prompt
+ pipeline : transformers.Pipeline, the pipeline to use for generating the response
+ timeout : int, optional, the number of seconds to wait before timing out, by default 45
+ max_length : int, optional, the maximum number of tokens to generate, defaults to 128
+ top_p : float, optional, the top probability to use for sampling, defaults to 0.95
+ top_k : int, optional, the top k to use for sampling, defaults to 50
+ temperature : float, optional, the temperature to use for sampling, defaults to 0.7
+ full_text : bool, optional, whether to return the full text or just the generated text, defaults to False
+ num_return_sequences : int, optional, the number of sequences to return, defaults to 1
+ device : int, optional, the device to use for generation, defaults to -1 (CPU)
+ verbose : bool, optional, whether to print the generated text, defaults to False
+
+ Returns
+ -------
+ str, the generated text
+
+ """
+
+ if max_length > 1024:
+ max_length = 1024
+ print("max_length is too large, setting to 1024")
+ st = time.perf_counter()
+
+ response = pipeline(
+ query,
+ max_length=max_length,
+ temperature=temperature,
+ top_k=top_k,
+ top_p=top_p,
+ num_return_sequences=num_return_sequences,
+ max_time=timeout,
+ return_full_text=full_text,
+ no_repeat_ngram_size=3,
+ length_penalty=0.3,
+ repetition_penalty=3.4,
+ clean_up_tokenization_spaces=True,
+ **kwargs,
+ ) # the likely better beam-less method
+ rt = round(time.perf_counter() - st, 2)
+ if verbose:
+ print(f"took {rt} sec to respond")
+
+ if verbose:
+ print("\n[DEBUG] generated:\n")
+ pp.pprint(response) # for debugging
+ # process the full result to get the ~bot response~ piece
+ this_result = str(response[0]["generated_text"]).split(
+ "\n"
+ ) # TODO: adjust hardcoded value for index to dynamic (if n>1)
+
+ bot_dialogue = consolidate_texts(
+ name_resp=responder,
+ model_resp=this_result,
+ name_spk=speaker,
+ verbose=verbose,
+ print_debug=True,
+ )
+ if verbose:
+ print(f"DEBUG: {bot_dialogue} was original response pre-SC")
+
+ return bot_dialogue #
+
+
+def consolidate_texts(
+ model_resp: list,
+ name_resp: str = None,
+ name_spk: str = None,
+ verbose=False,
+ print_debug=False,
+):
+ """
+ consolidate_texts - given a list with speaker name followed by speaker text, returns all consecutive values of the first speaker name
+
+ Parameters:
+ name_resp (str): the name of the person who is responding
+ model_resp (list): the list of strings to consolidate (usually from the model)
+ name_spk (str): the name of the person who is speaking
+ verbose (bool): whether to print the results
+ print_debug (bool): whether to print the debug info during looping
+
+ Returns:
+ list, a list of all the consecutive messages of the first speaker name
+ """
+ assert len(model_resp) > 0, "model_resp is empty"
+ if len(model_resp) == 1:
+ return model_resp[0]
+ name_resp = "person beta" if name_resp is None else name_resp
+ name_spk = "person alpha" if name_spk is None else name_spk
+ if verbose:
+ print("====" * 10)
+ print(f"\n[DEBUG] initial model_resp has {len(model_resp)} lines: \n\t{model_resp}")
+ print(f" the first element is \n\t{model_resp[0]} and it is {type(model_resp[0])}")
+ fn_resp = []
+
+ name_counter = 0
+ break_safe = False
+ for resline in model_resp:
+ if name_resp.lower() in resline:
+ name_counter += 1
+ break_safe = True # know the line is from bot as this line starts with the name of the bot
+ continue # don't add this line to the list
+ if name_spk.lower() in resline.lower():
+ if print_debug:
+ print(f"\nDEBUG: \n\t{resline}\ncaused the break")
+ break # the name of the speaker is in the line, so we're done
+ if any([": " in resline,":\n" in resline]) and name_resp.lower() not in resline.lower():
+ if print_debug:
+ print(f"\nDEBUG: \n\t{resline}\ncaused the break")
+ break
+ else:
+ fn_resp.append(resline)
+ break_safe = False
+ if verbose:
+ print("--" * 10)
+ print("\nthe full response is:\n")
+ print("\n".join(fn_resp))
+ print("--" * 10)
+
+ return fn_resp
diff --git a/grammar_improve.py b/grammar_improve.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfd917d84f35e6f6073277706533512e782ea263
--- /dev/null
+++ b/grammar_improve.py
@@ -0,0 +1,463 @@
+"""
+grammar_improve.py - this .py script contains functions to improve the grammar of a user's input or the models output.
+
+"""
+
+from datetime import datetime
+import os
+import pprint as pp
+from neuspell import BertChecker, SclstmChecker
+import neuspell
+import math
+from cleantext import clean
+import time
+import re
+import sys
+from symspellpy.symspellpy import SymSpell
+
+from utils import suppress_stdout
+
+
+def detect_propers(text: str):
+ """
+ detect_propers - detect if a string contains proper nouns
+
+ Args:
+ text (str): [string to be checked]
+
+ Returns:
+ [bool]: [True if string contains proper nouns]
+ """
+ pat = re.compile(r"(?:\w+['’])?\w+(?:-(?:\w+['’])?\w+)*")
+ return bool(pat.search(text))
+
+
+def fix_punct_spaces(string):
+ """
+ fix_punct_spaces - replace spaces around punctuation with punctuation. For example, "hello , there" -> "hello, there"
+
+ Parameters
+ ----------
+ string : str, required, input string to be corrected
+
+ Returns
+ -------
+ str, corrected string
+ """
+
+ fix_spaces = re.compile(r"\s*([?!.,]+(?:\s+[?!.,]+)*)\s*")
+ string = fix_spaces.sub(lambda x: "{} ".format(x.group(1).replace(" ", "")), string)
+ return string.strip()
+
+
+def split_sentences(text: str):
+ """
+ split_sentences - split a string into a list of sentences that keep their ending punctuation. powered by regex witchcraft
+
+ Args:
+ text (str): [string to be split]
+
+ Returns:
+ [list]: [list of strings]
+ """
+ return re.split(r"(? 0, "entered string for correction is empty"
+
+ if sym_checker is None:
+ # need to create a new class object. user can specify their own dictionary and bigram files
+ if verbose:
+ print("creating new SymSpell object")
+ sym_checker = build_symspell_obj(
+ edit_dist=max_dist,
+ prefix_length=prefix_length,
+ dictionary_path=dictionary_path,
+ bigram_path=bigram_path,
+ )
+ else:
+ if verbose:
+ print("using existing SymSpell object")
+ # max edit distance per lookup (per single word, not per whole input string)
+ suggestions = sym_checker.lookup_compound(
+ my_string,
+ max_edit_distance=max_dist,
+ ignore_non_words=ignore_non_words,
+ ignore_term_with_digits=True,
+ transfer_casing=True,
+ )
+
+ if verbose:
+ print(f"{len(suggestions)} suggestions found")
+ print(f"the original string is:\n\t{my_string}")
+ sug_list = [sug.term for sug in suggestions]
+ print(f"suggestions:\n\t{sug_list}\n")
+
+ if len(suggestions) < 1:
+ return clean(my_string) # no correction because no suggestions
+ else:
+ first_result = suggestions[0] # first result is the most likely
+ return first_result._term
+
+
+def build_symspell_obj(
+ edit_dist=2,
+ prefix_length=7,
+ dictionary_path=None,
+ bigram_path=None,
+):
+ """
+ build_symspell_obj [build a SymSpell object]
+
+ Args:
+ verbose (bool, optional): Defaults to False.
+
+ Returns:
+ SymSpell: a SymSpell object
+ """
+ dictionary_path = (
+ r"symspell_rsc/frequency_dictionary_en_82_765.txt"
+ if dictionary_path is None
+ else dictionary_path
+ )
+ bigram_path = (
+ r"symspell_rsc/frequency_bigramdictionary_en_243_342.txt"
+ if bigram_path is None
+ else bigram_path
+ )
+ sym_checker = SymSpell(
+ max_dictionary_edit_distance=edit_dist + 2, prefix_length=prefix_length
+ )
+ # term_index is the column of the term and count_index is the
+ # column of the term frequency
+ sym_checker.load_dictionary(dictionary_path, term_index=0, count_index=1)
+ sym_checker.load_bigram_dictionary(bigram_path, term_index=0, count_index=2)
+
+ return sym_checker
+
+
+"""
+# if using t5b_correction to check for spelling errors, use this code to initialize the objects
+
+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+model_name = 'deep-learning-analytics/GrammarCorrector'
+# torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
+torch_device = 'cpu'
+gc_tokenizer = T5Tokenizer.from_pretrained(model_name)
+gc_model = T5ForConditionalGeneration.from_pretrained(model_name).to(torch_device)
+
+"""
+
+
+def t5b_correction(prompt: str, korrektor, verbose=False, beams=4):
+ """
+ t5b_correction - correct a string using a text2textgen pipeline model from transformers
+
+ Parameters
+ ----------
+ prompt : str, required, input prompt to be corrected
+ korrektor : transformers.pipeline, required, pipeline object
+ verbose : bool, optional, whether to print the corrected prompt. Defaults to False.
+ beams : int, optional, number of beams to use for the correction. Defaults to 4.
+
+ Returns
+ -------
+ str, corrected prompt
+ """
+
+ p_min_len = int(math.ceil(0.9 * len(prompt)))
+ p_max_len = int(math.ceil(1.1 * len(prompt)))
+ if verbose:
+ print(f"setting min to {p_min_len} and max to {p_max_len}\n")
+ gcorr_result = korrektor(
+ f"grammar: {prompt}",
+ return_text=True,
+ clean_up_tokenization_spaces=True,
+ num_beams=beams,
+ max_length=p_max_len,
+ repetition_penalty=1.3,
+ length_penalty=0.2,
+ no_repeat_ngram_size=2,
+ )
+ if verbose:
+ print(f"grammar correction result: \n\t{gcorr_result}\n")
+ return gcorr_result
+
+
+def all_neuspell_chkrs():
+ """
+ disp_neuspell_chkrs - display the neuspell checkers available
+
+ Parameters
+ ----------
+ None
+
+ Returns
+ -------
+ checker_opts - list of checkers available
+ """
+
+ checker_opts = dir(neuspell)
+ print(f"\navailable checkers:")
+
+ pp.pprint(checker_opts, indent=4, compact=True)
+
+ return checker_opts
+
+
+def load_ns_checker(customckr=None, fast=False):
+ """
+ load_ns_checker - helper function, load / "set up" a neuspell checker from huggingface transformers
+
+ Args:
+ customckr (neuspell.NeuSpell): [neuspell checker object], optional, if not provided, will load the default checker
+
+ Returns:
+ [neuspell.NeuSpell]: [neuspell checker object]
+ """
+ st = time.perf_counter()
+ # stop all printing to the console
+ with suppress_stdout():
+ if customckr is None and not fast:
+
+ checker = BertChecker(
+ pretrained=True
+ ) # load the default checker, has the best balance
+ elif customckr is None and fast:
+ checker = SclstmChecker(
+ pretrained=True
+ ) # this one is faster but not as accurate
+ else:
+ checker = customckr(pretrained=True)
+ rt_min = (time.perf_counter() - st) / 60
+ # return to standard logging level
+ print(f"\n\nloaded checker in {rt_min} minutes")
+
+ return checker
+
+
+def neuspell_correct(input_text: str, checker=None, verbose=False):
+ """
+ neuspell_correct - correct a string using neuspell.
+ note that modificaitons to the checker are needed if doing list-based corrections
+
+ Parameters
+ ----------
+ input_text : str, required, input string to be corrected
+ checker : neuspell.NeuSpell, optional, neuspell checker object. Defaults to None.
+ verbose : bool, optional, whether to print the corrected string. Defaults to False.
+
+ Returns
+ -------
+ str, corrected string
+ """
+ if isinstance(input_text, str) and len(input_text) < 4:
+ print(f"input text of {input_text} is too short to be corrected")
+ return input_text
+
+ if checker is None:
+ print("NOTE - no checker provided, loading default checker")
+ checker = SclstmChecker(pretrained=True)
+
+ corrected = checker.correct(input_text)
+ cleaned_txt = fix_punct_spaces(corrected)
+
+ if verbose:
+ print(f"neuspell correction result: \n\t{cleaned_txt}\n")
+ return cleaned_txt
+
+
+def grammarpipe(corrector, qphrase: str):
+ """
+ gramformer_correct - THE ORIGINAL ONE USED IN PROJECT AND NEEDS TO BE CHANGED.
+ Idea is to 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]
+ """
+ if isinstance(qphrase, str) and len(qphrase) < 4:
+ print(f"input text of {qphrase} is too short to be corrected")
+ return qphrase
+ try:
+ corrected = corrector(
+ clean(qphrase), return_text=True, clean_up_tokenization_spaces=True
+ )
+ return corrected[0]["generated_text"]
+ except Exception as e:
+ print(f"NOTE - failed to correct with grammarpipe:\n {e}")
+ return clean(qphrase)
+
+
+def DLA_correct(qphrase: str):
+ """
+ DLA_correct - an "overhead" function to call correct_grammar() on a string, allowing for each newline to be corrected individually
+
+ Args:
+ qphrase (str): [string to be corrected]
+
+ Returns:
+ str, the list of the corrected strings joined under " "
+ """
+ if isinstance(qphrase, str) and len(qphrase) < 4:
+ print(f"input text of {qphrase} is too short to be corrected")
+ return qphrase
+
+ sentences = split_sentences(qphrase)
+ if len(sentences) == 1:
+ corrected = correct_grammar(sentences[0])
+ return corrected
+ else:
+ full_cor = []
+ for sen in sentences:
+ corr_sen = correct_grammar(clean(sen))
+ full_cor.append(corr_sen)
+ return " ".join(full_cor)
+
+
+def correct_grammar(
+ input_text: str,
+ tokenizer,
+ model,
+ n_results: int = 1,
+ beams: int = 8,
+ temp=1,
+ uniq_ngrams=2,
+ rep_penalty=1.5,
+ device="cpu",
+):
+ """
+ correct_grammar - correct a string using a text2textgen pipeline model from transformers.
+ This function is an alternative to the t5b_correction function.
+
+ Parameters
+ ----------
+ input_text : str, required, input string to be corrected
+ tokenizer : transformers.T5Tokenizer, required, tokenizer object, already created w/ relevant model
+ model : transformers.T5ForConditionalGeneration, required, model object, already created w/ relevant model
+ n_results : int, optional, number of results to return. Defaults to 1.
+ beams : int, optional, number of beams to use for the correction. Defaults to 8.
+ temp : int, optional, temperature to use for the correction. Defaults to 1.
+ uniq_ngrams : int, optional, number of ngrams to use for the correction. Defaults to 2.
+ rep_penalty : float, optional, penalty to use for the correction. Defaults to 1.5.
+ device : str, optional, device to use for the correction. Defaults to 'cpu'.
+
+ Returns
+ -------
+ str, corrected string (or list of strings if n_results > 1)
+ """
+ st = time.perf_counter()
+
+ if len(input_text) < 5:
+ return input_text
+ max_length = min(int(math.ceil(len(input_text) * 1.2)), 128)
+ batch = tokenizer(
+ [input_text],
+ truncation=True,
+ padding="max_length",
+ max_length=max_length,
+ return_tensors="pt",
+ ).to(device)
+ translated = model.generate(
+ **batch,
+ max_length=max_length,
+ min_length=min(10, len(input_text)),
+ no_repeat_ngram_size=uniq_ngrams,
+ repetition_penalty=rep_penalty,
+ num_beams=beams,
+ num_return_sequences=n_results,
+ temperature=temp,
+ )
+
+ tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
+ rt_min = (time.perf_counter() - st) / 60
+ print(f"\n\ncorrected in {rt_min} minutes")
+
+ if isinstance(tgt_text, list):
+ return tgt_text[0]
+ else:
+ return tgt_text
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..daedfc07560d3a2339247075bd3cce3c3242deda
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,16 @@
+transformers>=4.12.5
+sentencepiece>=0.1.96
+tqdm>=4.43.0
+symspellpy>=6.7.0
+requests>=2.24.0
+gradio>=2.4.6
+natsort>=7.1.1
+pandas>=1.3.0
+aitextgen>=0.5.2
+clean-text>=0.5.0
+openwa>=1.3.16
+python-telegram-bot>=13.0
+webwhatsapi>=2.0.5
+Flask>=2.0.2
+nltk>=3.6.6
+neuspell>=1.0.0
\ No newline at end of file
diff --git a/symspell_rsc/frequency_bigramdictionary_en_243_342.txt b/symspell_rsc/frequency_bigramdictionary_en_243_342.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c7cfb005b460aa1e4b3462111ffaf08861bd4ffd
--- /dev/null
+++ b/symspell_rsc/frequency_bigramdictionary_en_243_342.txt
@@ -0,0 +1,242342 @@
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diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a30c0c783aecf560cdafca260440da8d1657f294
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,385 @@
+"""
+ utils - general utility functions for loading, saving, and manipulating data
+"""
+
+import os
+from pathlib import Path
+import pprint as pp
+import re
+import shutil # zipfile formats
+from datetime import datetime
+from os.path import basename
+from os.path import getsize, join
+
+import requests
+from cleantext import clean
+from natsort import natsorted
+from symspellpy import SymSpell
+import pandas as pd
+from tqdm.auto import tqdm
+
+
+from contextlib import contextmanager
+import sys
+import os
+
+
+@contextmanager
+def suppress_stdout():
+ """
+ suppress_stdout - suppress stdout for a given block of code. credit to https://newbedev.com/how-to-suppress-console-output-in-python
+ """
+ with open(os.devnull, "w") as devnull:
+ old_stdout = sys.stdout
+ sys.stdout = devnull
+ try:
+ yield
+ finally:
+ sys.stdout = old_stdout
+
+
+def remove_string_extras(mytext):
+ # removes everything from a string except A-Za-z0-9 .,;
+ return re.sub(r"[^A-Za-z0-9 .,;]+", "", mytext)
+
+
+def corr(s):
+ # adds space after period if there isn't one
+ # removes extra spaces
+ return re.sub(r"\.(?! )", ". ", re.sub(r" +", " ", s))
+
+
+def get_timestamp():
+ # get timestamp for file names
+ return datetime.now().strftime("%b-%d-%Y_t-%H")
+
+
+def print_spacer(n=1):
+ """print_spacer - print a spacer line"""
+ print("\n -------- " * n)
+
+
+def fast_scandir(dirname: str):
+ """
+ fast_scandir [an os.path-based means to return all subfolders in a given filepath]
+
+ """
+
+ subfolders = [f.path for f in os.scandir(dirname) if f.is_dir()]
+ for dirname in list(subfolders):
+ subfolders.extend(fast_scandir(dirname))
+ return subfolders # list
+
+
+def create_folder(directory: str):
+ # you will never guess what this does
+ os.makedirs(directory, exist_ok=True)
+
+
+def chunks(lst: list, n: int):
+ """
+ chunks - Yield successive n-sized chunks from lst
+ Args: lst (list): list to be chunked
+ n (int): size of chunks
+
+ """
+
+ for i in range(0, len(lst), n):
+ yield lst[i : i + n]
+
+
+def chunky_pandas(my_df, num_chunks: int = 4):
+ """
+ chunky_pandas [split dataframe into `num_chunks` equal chunks, return each inside a list]
+
+ Args:
+ my_df (pd.DataFrame)
+ num_chunks (int, optional): Defaults to 4.
+
+ Returns:
+ list: a list of dataframes
+ """
+ n = int(len(my_df) // num_chunks)
+ list_df = [my_df[i : i + n] for i in range(0, my_df.shape[0], n)]
+
+ return list_df
+
+
+def load_dir_files(
+ directory: str, req_extension=".txt", return_type="list", verbose=False
+):
+ """
+ load_dir_files - an os.path based method of returning all files with extension `req_extension` in a given directory and subdirectories
+
+ Args:
+
+
+ Returns:
+ list or dict: an iterable of filepaths or a dict of filepaths and their respective filenames
+ """
+ appr_files = []
+ # r=root, d=directories, f = files
+ for r, d, f in os.walk(directory):
+ for prefile in f:
+ if prefile.endswith(req_extension):
+ fullpath = os.path.join(r, prefile)
+ appr_files.append(fullpath)
+
+ appr_files = natsorted(appr_files)
+
+ if verbose:
+ print("A list of files in the {} directory are: \n".format(directory))
+ if len(appr_files) < 10:
+ pp.pprint(appr_files)
+ else:
+ pp.pprint(appr_files[:10])
+ print("\n and more. There are a total of {} files".format(len(appr_files)))
+
+ if return_type.lower() == "list":
+ return appr_files
+ else:
+ if verbose:
+ print("returning dictionary")
+
+ appr_file_dict = {}
+ for this_file in appr_files:
+ appr_file_dict[basename(this_file)] = this_file
+
+ return appr_file_dict
+
+
+def URL_string_filter(text):
+ """
+ URL_string_filter - filter out nonstandard "text" characters
+
+ """
+ custom_printable = (
+ "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ._"
+ )
+
+ filtered = "".join((filter(lambda i: i in custom_printable, text)))
+
+ return filtered
+
+
+def getFilename_fromCd(cd):
+ """getFilename_fromCd - get the filename from a given cd str"""
+ if not cd:
+ return None
+ fname = re.findall("filename=(.+)", cd)
+ if len(fname) > 0:
+ output = fname[0]
+ elif cd.find("/"):
+ possible_fname = cd.rsplit("/", 1)[1]
+ output = URL_string_filter(possible_fname)
+ else:
+ output = None
+ return output
+
+
+def get_zip_URL(
+ URLtoget: str,
+ extract_loc: str = None,
+ file_header: str = "dropboxexport_",
+ verbose: bool = False,
+):
+ """get_zip_URL - download a zip file from a given URL and extract it to a given location"""
+
+ r = requests.get(URLtoget, allow_redirects=True)
+ names = getFilename_fromCd(r.headers.get("content-disposition"))
+ fixed_fnames = names.split(";") # split the multiple results
+ this_filename = file_header + URL_string_filter(fixed_fnames[0])
+
+ # define paths and save the zip file
+ if extract_loc is None:
+ extract_loc = "dropbox_dl"
+ dl_place = join(os.getcwd(), extract_loc)
+ create_folder(dl_place)
+ save_loc = join(os.getcwd(), this_filename)
+ open(save_loc, "wb").write(r.content)
+ if verbose:
+ print("downloaded file size was {} MB".format(getsize(save_loc) / 1000000))
+
+ # unpack the archive
+ shutil.unpack_archive(save_loc, extract_dir=dl_place)
+ if verbose:
+ print("extracted zip file - ", datetime.now())
+ x = load_dir_files(dl_place, req_extension="", verbose=verbose)
+
+ # remove original
+ try:
+ os.remove(save_loc)
+ del save_loc
+ except Exception:
+ print("unable to delete original zipfile - check if exists", datetime.now())
+
+ print("finished extracting zip - ", datetime.now())
+
+ return dl_place
+
+
+def merge_dataframes(data_dir: str, ext=".xlsx", verbose=False):
+ """
+ merge_dataframes - given a filepath, loads and attempts to merge all files as dataframes
+
+ Args:
+ data_dir (str): [root directory to search in]
+ ext (str, optional): [anticipate file extension for the dataframes ]. Defaults to '.xlsx'.
+
+ Returns:
+ pd.DataFrame(): merged dataframe of all files
+ """
+
+ src = Path(data_dir)
+ src_str = str(src.resolve())
+ mrg_df = pd.DataFrame()
+
+ all_reports = load_dir_files(directory=src_str, req_extension=ext, verbose=verbose)
+
+ failed = []
+
+ for df_path in tqdm(all_reports, total=len(all_reports), desc="joining data..."):
+
+ try:
+ this_df = pd.read_excel(df_path).convert_dtypes()
+
+ mrg_df = pd.concat([mrg_df, this_df], axis=0)
+ except Exception:
+ short_p = os.path.basename(df_path)
+ print(
+ f"WARNING - file with extension {ext} and name {short_p} could not be read."
+ )
+ failed.append(short_p)
+
+ if len(failed) > 0:
+ print("failed to merge {} files, investigate as needed")
+
+ if verbose:
+ pp.pprint(mrg_df.info(True))
+
+ return mrg_df
+
+
+def download_URL(url: str, file=None, dlpath=None, verbose=False):
+ """
+ download_URL - download a file from a URL and show progress bar
+
+ Parameters
+ ----------
+ url : str
+ URL to download
+ file : [type], optional
+ [description], by default None
+ dlpath : [type], optional
+ [description], by default None
+ verbose : bool, optional
+ [description], by default False
+
+ Returns
+ -------
+ str - path to the downloaded file
+ """
+
+ if file is None:
+ if "?dl=" in url:
+ # is a dropbox link
+ prefile = url.split("/")[-1]
+ filename = str(prefile).split("?dl=")[0]
+ else:
+ filename = url.split("/")[-1]
+
+ file = clean(filename)
+ if dlpath is None:
+ dlpath = Path.cwd() # save to current working directory
+ else:
+ dlpath = Path(dlpath) # make a path object
+
+ r = requests.get(url, stream=True, allow_redirects=True)
+ total_size = int(r.headers.get("content-length"))
+ initial_pos = 0
+ dl_loc = dlpath / file
+ with open(str(dl_loc.resolve()), "wb") as f:
+ with tqdm(
+ total=total_size,
+ unit="B",
+ unit_scale=True,
+ desc=file,
+ initial=initial_pos,
+ ascii=True,
+ ) as pbar:
+ for ch in r.iter_content(chunk_size=1024):
+ if ch:
+ f.write(ch)
+ pbar.update(len(ch))
+
+ if verbose:
+ print(f"\ndownloaded {file} to {dlpath}\n")
+
+ return str(dl_loc.resolve())
+
+
+def dl_extract_zip(
+ URLtoget: str,
+ extract_loc: str = None,
+ file_header: str = "TEMP_archive_dl_",
+ verbose: bool = False,
+):
+ """
+ dl_extract_zip - generic function to download a zip file and extract it
+
+ Parameters
+ ----------
+ URLtoget : str
+ zip file URL to download
+ extract_loc : str, optional
+ directory to extract zip to , by default None
+ file_header : str, optional
+ [description], by default "TEMP_archive_dl_"
+ verbose : bool, optional
+ [description], by default False
+
+ Returns
+ -------
+ str - path to the downloaded and extracted folder
+ """
+
+ extract_loc = Path(extract_loc)
+ extract_loc.mkdir(parents=True, exist_ok=True)
+
+ save_loc = download_URL(
+ url=URLtoget, file=f"{file_header}.zip", dlpath=None, verbose=verbose
+ )
+
+ shutil.unpack_archive(save_loc, extract_dir=extract_loc)
+
+ if verbose:
+ print("extracted zip file - ", datetime.now())
+ x = load_dir_files(extract_loc, req_extension="", verbose=verbose)
+
+ # remove original
+ try:
+ os.remove(save_loc)
+ del save_loc
+ except Exception:
+ print("unable to delete original zipfile - check if exists", datetime.now())
+
+ if verbose:
+ print("finished extracting zip - ", datetime.now())
+
+ return extract_loc
+
+
+def cleantxt_wrap(ugly_text, all_lower=False):
+ """
+ cleantxt_wrap - applies the clean function to a string.
+
+ Args:
+ ugly_text (str): [string to be cleaned]
+
+ Returns:
+ [str]: [cleaned string]
+ """
+ if isinstance(ugly_text, str) and len(ugly_text) > 0:
+ return clean(ugly_text, lower=all_lower)
+ else:
+ return ugly_text