predictors.py

import torch
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import nltk
import torch.nn.functional as F
import nltk
from scipy.special import softmax
import yaml
from utils import *
import joblib
from optimum.bettertransformer import BetterTransformer
import gc
from cleantext import clean
import gradio as gr
from tqdm.auto import tqdm
from transformers import pipeline
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import nltk
from nltk.tokenize import sent_tokenize
from optimum.pipelines import pipeline

with open("config.yaml", "r") as file:
    params = yaml.safe_load(file)
    
nltk.download("punkt")
nltk.download("stopwords")
device_needed = "cuda" if torch.cuda.is_available() else "cpu"
device = "cuda" if torch.cuda.is_available() else "cpu"
print('DEVICE IS :' , device)

text_bc_model_path = params["TEXT_BC_MODEL_PATH"]
text_mc_model_path = params["TEXT_MC_MODEL_PATH"]
text_quillbot_model_path = params["TEXT_QUILLBOT_MODEL_PATH"]
quillbot_labels = params["QUILLBOT_LABELS"]
mc_label_map = params["MC_OUTPUT_LABELS"]
mc_token_size = int(params["MC_TOKEN_SIZE"])
bc_token_size = int(params["BC_TOKEN_SIZE"])
bias_checker_model_name = params['BIAS_CHECKER_MODEL_PATH']
bias_corrector_model_name = params['BIAS_CORRECTOR_MODEL_PATH']
# access_token = params['HF_TOKEN']

text_bc_tokenizer = AutoTokenizer.from_pretrained(text_bc_model_path)
text_bc_model = AutoModelForSequenceClassification.from_pretrained(text_bc_model_path).to(device)
text_mc_tokenizer = AutoTokenizer.from_pretrained(text_mc_model_path)
text_mc_model = AutoModelForSequenceClassification.from_pretrained(text_mc_model_path).to(device)
quillbot_tokenizer = AutoTokenizer.from_pretrained(text_quillbot_model_path)
quillbot_model = AutoModelForSequenceClassification.from_pretrained(text_quillbot_model_path).to(device)

# proxy models for explainability
mini_bc_model_name = "polygraf-ai/bc-model"
bc_tokenizer_mini = AutoTokenizer.from_pretrained(mini_bc_model_name)
bc_model_mini = AutoModelForSequenceClassification.from_pretrained(mini_bc_model_name).to(device_needed)
mini_humanizer_model_name =  "polygraf-ai/humanizer-model"
humanizer_tokenizer_mini = AutoTokenizer.from_pretrained(mini_humanizer_model_name)
humanizer_model_mini = AutoModelForSequenceClassification.from_pretrained(mini_humanizer_model_name).to(device_needed)

bc_model_mini = BetterTransformer.transform(bc_model_mini)
humanizer_model_mini = BetterTransformer.transform(humanizer_model_mini)
text_bc_model = BetterTransformer.transform(text_bc_model)
text_mc_model = BetterTransformer.transform(text_mc_model)
quillbot_model = BetterTransformer.transform(quillbot_model)

# bias_model_checker = AutoModelForSequenceClassification.from_pretrained(bias_checker_model_name)
# tokenizer = AutoTokenizer.from_pretrained(bias_checker_model_name)
# bias_model_checker = BetterTransformer.transform(bias_model_checker, keep_original_model=False)
# bias_checker = pipeline(
#     "text-classification",
#     model=bias_checker_model_name,
#     tokenizer=bias_checker_model_name,
# )
# gc.collect()
# bias_corrector = pipeline( "text2text-generation", model=bias_corrector_model_name, accelerator="ort")

# model score calibration
iso_reg = joblib.load("isotonic_regression_model.joblib")


def split_text(text: str) -> list:
    sentences = sent_tokenize(text)
    return [[sentence] for sentence in sentences]

def correct_text(text: str, bias_checker, bias_corrector, separator: str = " ") -> tuple:
    sentence_batches = split_text(text)
    corrected_text = []
    corrections = []
    for batch in tqdm(sentence_batches, total=len(sentence_batches), desc="correcting text.."):
        raw_text = " ".join(batch)
        results = bias_checker(raw_text)
        if results[0]["label"] != "LABEL_1" or (results[0]["label"] == "LABEL_1" and results[0]["score"] < 0.9):
            corrected_batch = bias_corrector(raw_text)
            corrected_version = corrected_batch[0]["generated_text"]
            corrected_text.append(corrected_version)
            corrections.append((raw_text, corrected_version)) 
        else:
            corrected_text.append(raw_text)
    corrected_text = separator.join(corrected_text)
    return corrected_text, corrections

def update(text: str):
    # text = clean(text, lower=False)
    # corrected_text, corrections = correct_text(text, bias_checker, bias_corrector)
    # corrections_display = "".join([f"{corr}" for orig, corr in corrections])
    # if corrections_display == "":
    #     corrections_display = text
    # return corrections_display
    return "Unavailable"

def update_main(text: str):
    # text = clean(text, lower=False)
    # corrected_text, corrections = correct_text(text, bias_checker, bias_corrector)
    # corrections_display = "\n\n".join([f"Original: {orig}\nCorrected: {corr}" for orig, corr in corrections])
    # return corrected_text, corrections_display
    return text, "Unavailable"

def split_text(text: str) -> list:
    sentences = sent_tokenize(text)
    return [[sentence] for sentence in sentences]

def get_token_length(tokenizer, sentence):
    return len(tokenizer.tokenize(sentence))

def split_text_allow_complete_sentences_nltk(text, type_det="bc"):
    sentences = sent_tokenize(text)
    chunks = []
    current_chunk = []
    current_length = 0
    if type_det == "bc":
        tokenizer = text_bc_tokenizer
        max_tokens = bc_token_size
    elif type_det == "mc":
        tokenizer = text_mc_tokenizer
        max_tokens = mc_token_size

    elif type_det == "quillbot":
        tokenizer = quillbot_tokenizer
        max_tokens = 256

    def add_sentence_to_chunk(sentence):
        nonlocal current_chunk, current_length
        sentence_length = get_token_length(tokenizer, sentence)
        if current_length + sentence_length > max_tokens:
            chunks.append((current_chunk, current_length))
            current_chunk = []
            current_length = 0
        current_chunk.append(sentence)
        current_length += sentence_length

    for sentence in sentences:
        add_sentence_to_chunk(sentence)
    if current_chunk:
        chunks.append((current_chunk, current_length))
    adjusted_chunks = []
    while chunks:
        chunk = chunks.pop(0)
        if len(chunks) > 0 and chunk[1] < max_tokens / 2:
            next_chunk = chunks.pop(0)
            combined_length = chunk[1] + next_chunk[1]
            if combined_length <= max_tokens:
                adjusted_chunks.append((chunk[0] + next_chunk[0], combined_length))
            else:
                adjusted_chunks.append(chunk)
                chunks.insert(0, next_chunk)
        else:
            adjusted_chunks.append(chunk)
    result_chunks = [" ".join(chunk[0]) for chunk in adjusted_chunks]
    return result_chunks


def predict_quillbot(text, bias_buster_selected):
    if bias_buster_selected:
        text = update(text)
    with torch.no_grad():
        quillbot_model.eval()
        tokenized_text = quillbot_tokenizer(
            text,
            padding="max_length",
            truncation=True,
            max_length=256,
            return_tensors="pt",
        ).to(device)
        output = quillbot_model(**tokenized_text)
        output_norm = softmax(output.logits.detach().cpu().numpy(), 1)[0]
        q_score = {
            "Humanized": output_norm[1].item(),
            "Original": output_norm[0].item(),
        }
        return q_score


def predict_for_explainanility(text, model_type=None):
    if model_type == "quillbot":
        cleaning = False
        max_length = 256
        model = humanizer_model_mini
        tokenizer = humanizer_tokenizer_mini
    elif model_type == "bc":
        cleaning = True
        max_length = bc_token_size
        model = bc_model_mini
        tokenizer = bc_tokenizer_mini
    else:
        raise ValueError("Invalid model type")
    with torch.no_grad():
        if cleaning:
            text = [remove_special_characters(t) for t in text]
        tokenized_text = tokenizer(
            text,
            return_tensors="pt",
            padding="max_length",
            truncation=True,
            max_length=max_length,
        ).to(device_needed)
        outputs = model(**tokenized_text)
        tensor_logits = outputs[0]
        probas = F.softmax(tensor_logits).detach().cpu().numpy()
    return probas


def predict_bc(model, tokenizer, text):
    with torch.no_grad():
        model.eval()
        tokens = text_bc_tokenizer(
            text,
            padding="max_length",
            truncation=True,
            max_length=bc_token_size,
            return_tensors="pt",
        ).to(device)
        output = model(**tokens)
        output_norm = softmax(output.logits.detach().cpu().numpy(), 1)[0]
        return output_norm


def predict_mc(model, tokenizer, text):
    with torch.no_grad():
        model.eval()
        tokens = text_mc_tokenizer(
            text,
            padding="max_length",
            truncation=True,
            return_tensors="pt",
            max_length=mc_token_size,
        ).to(device)
        output = model(**tokens)
        output_norm = softmax(output.logits.detach().cpu().numpy(), 1)[0]
        return output_norm


def predict_bc_scores(input):
    bc_scores = []
    samples_len_bc = len(
        split_text_allow_complete_sentences_nltk(input, type_det="bc")
    )
    segments_bc = split_text_allow_complete_sentences_nltk(input, type_det="bc")
    for i in range(samples_len_bc):
        cleaned_text_bc = remove_special_characters(segments_bc[i])
        bc_score = predict_bc(text_bc_model, text_bc_tokenizer, cleaned_text_bc)
        bc_scores.append(bc_score)
    bc_scores_array = np.array(bc_scores)
    average_bc_scores = np.mean(bc_scores_array, axis=0)
    bc_score_list = average_bc_scores.tolist()
    print(
        f"Original BC scores: AI: {bc_score_list[1]}, HUMAN: {bc_score_list[0]}"
    )
    # isotonic regression calibration
    ai_score = iso_reg.predict([bc_score_list[1]])[0]
    human_score = 1 - ai_score
    bc_score = {"AI": ai_score, "HUMAN": human_score}
    print(f"Calibration BC scores: AI: {ai_score}, HUMAN: {human_score}")
    print(f"Input Text: {cleaned_text_bc}")
    return bc_score


def predict_mc_scores(input):
    # BC SCORE
    bc_scores = []
    samples_len_bc = len(
        split_text_allow_complete_sentences_nltk(input, type_det="bc")
    )
    segments_bc = split_text_allow_complete_sentences_nltk(input, type_det="bc")
    for i in range(samples_len_bc):
        cleaned_text_bc = remove_special_characters(segments_bc[i])
        bc_score = predict_bc(text_bc_model, text_bc_tokenizer, cleaned_text_bc)
        bc_scores.append(bc_score)
    bc_scores_array = np.array(bc_scores)
    average_bc_scores = np.mean(bc_scores_array, axis=0)
    bc_score_list = average_bc_scores.tolist()
    print(
        f"Original BC scores: AI: {bc_score_list[1]}, HUMAN: {bc_score_list[0]}"
    )
    # isotonic regression calibration
    ai_score = iso_reg.predict([bc_score_list[1]])[0]
    human_score = 1 - ai_score
    bc_score = {"AI": ai_score, "HUMAN": human_score}
    print(f"Calibration BC scores: AI: {ai_score}, HUMAN: {human_score}")
    mc_scores = []
    segments_mc = split_text_allow_complete_sentences_nltk(
        input, type_det="mc"
    )
    samples_len_mc = len(
        split_text_allow_complete_sentences_nltk(input, type_det="mc")
    )
    for i in range(samples_len_mc):
        cleaned_text_mc = remove_special_characters(segments_mc[i])
        mc_score = predict_mc(
            text_mc_model, text_mc_tokenizer, cleaned_text_mc
        )
        mc_scores.append(mc_score)
    mc_scores_array = np.array(mc_scores)
    average_mc_scores = np.mean(mc_scores_array, axis=0)
    mc_score_list = average_mc_scores.tolist()
    mc_score = {}
    for score, label in zip(mc_score_list, mc_label_map):
        mc_score[label.upper()] = score

    sum_prob = 1 - bc_score["HUMAN"]
    for key, value in mc_score.items():
        mc_score[key] = value * sum_prob
    print("MC Score:", mc_score)
    if sum_prob < 0.01:
        mc_score = {}

    return mc_score