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| from .ISentenceCheck import ISentenceCheck | |
| from transformers import GPT2LMHeadModel, GPT2TokenizerFast | |
| import language_tool_python | |
| import torch | |
| import nltk | |
| nltk.download('punkt') | |
| class SentenceCheck(ISentenceCheck): | |
| def __init__(self): | |
| self.tool = language_tool_python.LanguageTool('en-US') | |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| print(f"[SentenceCheck] Using device: {self.device}") | |
| self.model = GPT2LMHeadModel.from_pretrained("gpt2").to(self.device) | |
| self.tokenizer = GPT2TokenizerFast.from_pretrained("gpt2") | |
| def is_grammatically_correct(self, text): | |
| matches = self.tool.check(text) | |
| return len(matches) == 0 | |
| def is_single_word_sentence(self, text): | |
| return "nosentence" if len(text.split()) <= 1 else text | |
| def looks_meaningful(self, text): | |
| words = nltk.word_tokenize(text) | |
| english_words = [word for word in words if word.isalpha()] | |
| return len(english_words) / len(words) > 0.5 | |
| def get_perplexity(self, sentence): | |
| inputs = self.tokenizer(sentence, return_tensors="pt").to(self.device) | |
| with torch.no_grad(): | |
| outputs = self.model(**inputs, labels=inputs["input_ids"]) | |
| loss = outputs.loss | |
| return torch.exp(loss).item() | |
| def IsSentenceCorrect(self, question: str) -> bool: | |
| if self.is_single_word_sentence(question) == "nosentence": | |
| return False | |
| if not self.looks_meaningful(question): | |
| return False | |
| if not self.is_grammatically_correct(question): | |
| return False | |
| if self.get_perplexity(question) > 80: | |
| return False | |
| if len(question.split()) < 4 or len(question.split()) > 20: | |
| return False | |
| if not question.strip().endswith("?"): | |
| return False | |
| if question.split()[0].lower() not in [ | |
| "what", "how", "why", "when", "where", "is", "are", "can", | |
| "should", "could", "who", "does", "do" | |
| ]: | |
| return False | |
| return True | |