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 @@ +abcs of 10956800 +aaron and 10721728 +abbott and 7861376 +abbreviations and 13518272 +aberdeen and 7347776 +ability to 2199042048 +able to 6450132352 +abolition of 39157312 +aboriginal and 33948800 +aboriginal 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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