app.py

from flask import Flask, request
import gradio as gr
import os
import re
app = Flask(__name__)

import torch
from tqdm import tqdm
from transformers import GPT2LMHeadModel, GPT2TokenizerFast

device = 'cuda' if cuda.is_available() else 'cpu'
model_id = "gpt2"
modelgpt2 = GPT2LMHeadModel.from_pretrained(model_id).to(device)
tokenizergpt2 = GPT2TokenizerFast.from_pretrained(model_id)

def text_to_sentences(text):
    clean_text = text.replace('\n', ' ')
    return re.split(r'(?<=[^A-Z].[.?]) +(?=[A-Z])', clean_text)

def calculatePerplexity(text):
    encodings = tokenizergpt2("\n\n".join([text]), return_tensors="pt")
    max_length = modelgpt2.config.n_positions
    stride = 512
    input_ids = encodings.input_ids
    seq_len = input_ids.size(1)

    nlls = []
    prev_end_loc = 0
    for begin_loc in range(0, seq_len, stride):
        end_loc = min(begin_loc + max_length, seq_len)
        trg_len = end_loc - prev_end_loc
        target_ids = input_ids.clone()
        target_ids[:, :-trg_len] = -100

        with torch.no_grad():
            outputs = modelgpt2(input_ids, labels=target_ids)
            neg_log_likelihood = outputs.loss * trg_len

        nlls.append(neg_log_likelihood)

        prev_end_loc = end_loc
        if end_loc == seq_len:
            break

    ppl = torch.exp(torch.stack(nlls).sum() / end_loc)

    return ppl.item()
    
def calculatePerplexities(text):
    sentences = text_to_sentences(text)
    perplexities = []
    for sentence in sentences:
        perplexities.append(calculatePerplexity(sentence))
    return perplexities

demo = gr.Interface(
        fn=calculatePerplexities, 
        inputs=gr.Textbox(placeholder="Copy and paste here..."), 
        article = "Visit <a href = \"https://ai-content-detector.online/\">AI Content Detector</a> for better user experience!",
        outputs=gr.outputs.JSON(),
        interpretation="default",

demo.launch(show_api=False)