predictors.py

import requests
import httpx
import torch
import re
from bs4 import BeautifulSoup
import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import asyncio
from evaluate import load
from datetime import date
import nltk
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
import plotly.graph_objects as go
import torch.nn.functional as F
import nltk
from unidecode import unidecode
import time
from scipy.special import softmax
import yaml
import os
from utils import *

with open("config.yaml", "r") as file:
    params = yaml.safe_load(file)
nltk.download("punkt")
nltk.download("stopwords")
device = "cuda" if torch.cuda.is_available() else "cpu"
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"]
text_1on1_models = params["TEXT_1ON1_MODEL"]
quillbot_labels = params["QUILLBOT_LABELS"]
mc_label_map = params["MC_OUTPUT_LABELS"]
text_1on1_label_map = params["1ON1_OUTPUT_LABELS"]
mc_token_size = int(params["MC_TOKEN_SIZE"])
bc_token_size = int(params["BC_TOKEN_SIZE"])
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)
# tokenizers_1on1 = {}
# models_1on1 = {}
# for model in text_1on1_models:
#     tokenizers_1on1[model] = AutoTokenizer.from_pretrained(model)
#     models_1on1[model] = AutoModelForSequenceClassification.from_pretrained(
#         model
#     ).to(device)


def split_text_allow_complete_sentences_nltk(
    text,
    max_length=256,
    tolerance=30,
    min_last_segment_length=100,
    type_det="bc",
):
    sentences = nltk.sent_tokenize(text)
    segments = []
    current_segment = []
    current_length = 0
    if type_det == "bc":
        tokenizer = text_bc_tokenizer
        max_length = bc_token_size
    elif type_det == "mc":
        tokenizer = text_mc_tokenizer
        max_length = mc_token_size
    for sentence in sentences:
        tokens = tokenizer.tokenize(sentence)
        sentence_length = len(tokens)

        if current_length + sentence_length <= max_length + tolerance - 2:
            current_segment.append(sentence)
            current_length += sentence_length
        else:
            if current_segment:
                encoded_segment = tokenizer.encode(
                    " ".join(current_segment),
                    add_special_tokens=True,
                    max_length=max_length + tolerance,
                    truncation=True,
                )
                segments.append((current_segment, len(encoded_segment)))
            current_segment = [sentence]
            current_length = sentence_length

    if current_segment:
        encoded_segment = tokenizer.encode(
            " ".join(current_segment),
            add_special_tokens=True,
            max_length=max_length + tolerance,
            truncation=True,
        )
        segments.append((current_segment, len(encoded_segment)))

    final_segments = []
    for i, (seg, length) in enumerate(segments):
        if i == len(segments) - 1:
            if length < min_last_segment_length and len(final_segments) > 0:
                prev_seg, prev_length = final_segments[-1]
                combined_encoded = tokenizer.encode(
                    " ".join(prev_seg + seg),
                    add_special_tokens=True,
                    max_length=max_length + tolerance,
                    truncation=True,
                )
                if len(combined_encoded) <= max_length + tolerance:
                    final_segments[-1] = (prev_seg + seg, len(combined_encoded))
                else:
                    final_segments.append((seg, length))
            else:
                final_segments.append((seg, length))
        else:
            final_segments.append((seg, length))

    decoded_segments = []
    encoded_segments = []
    for seg, _ in final_segments:
        encoded_segment = tokenizer.encode(
            " ".join(seg),
            add_special_tokens=True,
            max_length=max_length + tolerance,
            truncation=True,
        )
        decoded_segment = tokenizer.decode(encoded_segment)
        decoded_segments.append(decoded_segment)
    return decoded_segments


def predict_quillbot(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_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_mc_scores(input):
    bc_scores = []
    mc_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()
    bc_score = {"AI": bc_score_list[1], "HUMAN": bc_score_list[0]}
    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
    if sum_prob < 0.01:
        mc_score = {}

    return mc_score


def predict_bc_scores(input):
    bc_scores = []
    mc_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()
    bc_score = {"AI": bc_score_list[1], "HUMAN": bc_score_list[0]}
    return bc_score


def predict_1on1(model, tokenizer, text):
    with torch.no_grad():
        model.eval()
        tokens = 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_1on1_combined(input):
    predictions = []
    for i, model in enumerate(text_1on1_models):
        predictions.append(
            predict_1on1(models_1on1[model], tokenizers_1on1[model], input)[1]
        )
    return predictions


def predict_1on1_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()
    bc_score = {"AI": bc_score_list[1], "HUMAN": bc_score_list[0]}

    # MC 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_1on1_combined(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)
    normalized_mc_scores = average_mc_scores / np.sum(average_mc_scores)
    mc_score_list = normalized_mc_scores.tolist()
    mc_score = {}
    for score, label in zip(mc_score_list, text_1on1_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
    if sum_prob < 0.01:
        mc_score = {}

    return mc_score