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| import pickle | |
| import torch | |
| import numpy as np | |
| import gradio as gr | |
| from nltk import word_tokenize, sent_tokenize | |
| from scipy.stats import shapiro | |
| from transformers import GPT2LMHeadModel, GPT2TokenizerFast | |
| model = GPT2LMHeadModel.from_pretrained('gpt2-large').to('cuda') | |
| tokenizer: GPT2TokenizerFast = GPT2TokenizerFast.from_pretrained('gpt2-large') | |
| with open('model.pkl', 'rb') as f: | |
| lr_model = pickle.load(f) | |
| def get_perplexity(text: str): | |
| tokens = tokenizer(text, return_tensors='pt', truncation=True, return_offsets_mapping=True) | |
| inputs = tokens.input_ids.to('cuda') | |
| targets = inputs.clone() | |
| with torch.no_grad(): | |
| outputs = model(inputs, labels=targets) | |
| labels = targets.to(outputs.logits.device) | |
| # Shift so that tokens < n predict n | |
| shift_logits = outputs.logits[..., :-1, :].contiguous() | |
| shift_labels = labels[..., 1:].contiguous() | |
| perplexities = torch.nn.functional.cross_entropy(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1), reduce=False) | |
| output = [] | |
| targets = targets.to('cpu')[0].tolist() | |
| # tokens = tokenizer.convert_ids_to_tokens(targets) | |
| offsets = tokens.offset_mapping[0].tolist() | |
| print(perplexities.to('cpu').tolist()) | |
| perplexities = perplexities.to('cpu').numpy() | |
| perplexities = perplexities / np.max(perplexities) | |
| perplexities = perplexities.tolist() | |
| print(perplexities) | |
| # output.append((text[:offsets[0][1]], 0)) | |
| # for offset, p in zip(offsets[1:], perplexities): | |
| # output.append((text[offset[0]:offset[1]], p)) | |
| # print(type(p)) | |
| output.append((text[:tokens.word_to_chars(0)[1]], 0)) | |
| for word_id, p in zip(tokens.word_ids()[1:], perplexities): | |
| if word_id == len(output): | |
| span = tokens.word_to_chars(word_id) | |
| output.append((text[span[0]:span[1]], p)) | |
| return outputs.loss, output | |
| def score_text(text): | |
| perplexity, word_perplexities = get_perplexity(text) | |
| lengths = [] | |
| for sentence in sent_tokenize(text): | |
| lengths.append(len(word_tokenize(sentence))) | |
| scores = lr_model.predict_proba([[perplexity.item(), np.mean(lengths), np.std(lengths), shapiro(lengths).pvalue if len(lengths) > 2 else 0.5]])[0] | |
| return {'Human': scores[0], 'AI': scores[1]}, word_perplexities | |
| sample_text = """ | |
| The Saturn V is a type of rocket that was developed by NASA in the 1960s to support the Apollo program, which aimed to land humans on the Moon. | |
| It remains the most powerful rocket ever built, and its five F-1 engines generated more than 7.5 million pounds of thrust at liftoff. | |
| The Saturn V was used for all of the Apollo missions to the Moon, as well as the launch of the Skylab space station. | |
| Despite its impressive capabilities, the Saturn V was only used for a brief period of time before being retired in 1973. | |
| Nevertheless, it remains a landmark achievement in the history of space exploration and a symbol of human ingenuity and determination.""" | |
| demo = gr.Interface(fn=score_text, inputs=[gr.Textbox(label="Text to score", lines=5, value=sample_text)], outputs=[gr.Label(), gr.HighlightedText()] ) | |
| demo.launch() |