from utils import cosineSim, googleSearch, getSentences, parallel_scrap, matchingScore import gradio as gr from urllib.request import urlopen, Request from googleapiclient.discovery import build import requests import httpx import re from bs4 import BeautifulSoup import numpy as np from transformers import AutoTokenizer, AutoModelForSequenceClassification import asyncio from scipy.special import softmax from evaluate import load from datetime import date import nltk import os np.set_printoptions(suppress=True) def plagiarism_check( input, year_from, month_from, day_from, year_to, month_to, day_to, domains_to_skip, ): api_key = "AIzaSyCLyCCpOPLZWuptuPAPSg8cUIZhdEMVf6g" api_key = "AIzaSyCS1WQDMl1IMjaXtwSd_2rA195-Yc4psQE" api_key = "AIzaSyCB61O70B8AC3l5Kk3KMoLb6DN37B7nqIk" api_key = "AIzaSyCg1IbevcTAXAPYeYreps6wYWDbU0Kz8tg" # api_key = "AIzaSyBrx_pgb6A64wPFQXSGQRgGtukoxVV_0Fk" cse_id = "851813e81162b4ed4" sentences = getSentences(input) urlCount = {} ScoreArray = [] urlList = [] date_from = build_date(year_from, month_from, day_from) date_to = build_date(year_to, month_to, day_to) sort_date = f"date:r:{date_from}:{date_to}" # get list of URLS to check urlCount, ScoreArray = googleSearch( sentences, urlCount, ScoreArray, urlList, sort_date, domains_to_skip, api_key, cse_id, ) print("Number of URLs: ", len(urlCount)) # print("Old Score Array:\n") # print2D(ScoreArray) # Scrape URLs in list formatted_tokens = [] soups = asyncio.run(parallel_scrap(urlList)) print(len(soups)) print( "Successful scraping: " + str(len([x for x in soups if x is not None])) + "out of " + str(len(urlList)) ) # Populate matching scores for scrapped pages for i, soup in enumerate(soups): print(f"Analyzing {i+1} of {len(soups)} soups........................") if soup: page_content = soup.text for j, sent in enumerate(sentences): score = matchingScore(sent, page_content) ScoreArray[i][j] = score # ScoreArray = asyncio.run(parallel_analyze_2(soups, sentences, ScoreArray)) # print("New Score Array:\n") # print2D(ScoreArray) # Gradio formatting section sentencePlag = [False] * len(sentences) sentenceToMaxURL = [-1] * len(sentences) for j in range(len(sentences)): if j > 0: maxScore = ScoreArray[sentenceToMaxURL[j - 1]][j] sentenceToMaxURL[j] = sentenceToMaxURL[j - 1] else: maxScore = -1 for i in range(len(ScoreArray)): margin = ( 0.1 if (j > 0 and sentenceToMaxURL[j] == sentenceToMaxURL[j - 1]) else 0 ) if ScoreArray[i][j] - maxScore > margin: maxScore = ScoreArray[i][j] sentenceToMaxURL[j] = i if maxScore > 0.5: sentencePlag[j] = True if ( (len(sentences) > 1) and (sentenceToMaxURL[1] != sentenceToMaxURL[0]) and ( ScoreArray[sentenceToMaxURL[0]][0] - ScoreArray[sentenceToMaxURL[1]][0] < 0.1 ) ): sentenceToMaxURL[0] = sentenceToMaxURL[1] index = np.unique(sentenceToMaxURL) urlMap = {} for count, i in enumerate(index): urlMap[i] = count + 1 for i, sent in enumerate(sentences): formatted_tokens.append( (sent, "[" + str(urlMap[sentenceToMaxURL[i]]) + "]") ) formatted_tokens.append(("\n", None)) formatted_tokens.append(("\n", None)) formatted_tokens.append(("\n", None)) urlScore = {} for url in index: s = [ ScoreArray[url][sen] for sen in range(len(sentences)) if sentenceToMaxURL[sen] == url ] urlScore[url] = sum(s) / len(s) for ind in index: formatted_tokens.append( ( urlList[ind] + " --- Matching Score: " + str(urlScore[ind]), "[" + str(urlMap[ind]) + "]", ) ) formatted_tokens.append(("\n", None)) print(f"Formatted Tokens: {formatted_tokens}") return formatted_tokens """ AI DETECTION SECTION """ text_bc_model_path = "polygraf-ai/ai-text-bc-bert-1-4m" text_bc_tokenizer = AutoTokenizer.from_pretrained(text_bc_model_path) text_bc_model = AutoModelForSequenceClassification.from_pretrained( text_bc_model_path ) text_mc_model_path = "polygraf-ai/ai-text-mc-v5-lighter-spec" text_mc_tokenizer = AutoTokenizer.from_pretrained(text_mc_model_path) text_mc_model = AutoModelForSequenceClassification.from_pretrained( text_mc_model_path ) def remove_special_characters(text): cleaned_text = re.sub(r'[^a-zA-Z0-9\s]', '', text) return cleaned_text def predict_bc(model, tokenizer, text): tokens = tokenizer( text, padding=True, truncation=True, return_tensors="pt" )["input_ids"] output = model(tokens) output_norm = softmax(output.logits.detach().numpy(), 1)[0] print("BC Score: ", output_norm) bc_score = {"AI": output_norm[1].item(), "HUMAN": output_norm[0].item()} return bc_score def predict_mc(model, tokenizer, text): tokens = tokenizer( text, padding=True, truncation=True, return_tensors="pt" )["input_ids"] output = model(tokens) output_norm = softmax(output.logits.detach().numpy(), 1)[0] print("MC Score: ", output_norm) mc_score = {} label_map = ["GPT 3.5", "GPT 4", "CLAUDE", "BARD", "LLAMA 2"] for score, label in zip(output_norm, label_map): mc_score[label.upper()] = score.item() return mc_score def ai_generated_test(input, models): cleaned_text = remove_special_characters(input) bc_score = predict_bc(text_bc_model, text_bc_tokenizer, cleaned_text) mc_score = predict_mc(text_mc_model, text_mc_tokenizer, cleaned_text) sum_prob = 1 - bc_score["HUMAN"] for key, value in mc_score.items(): mc_score[key] = value * sum_prob return bc_score, mc_score # COMBINED def main( input, models, year_from, month_from, day_from, year_to, month_to, day_to, domains_to_skip, ): bc_score, mc_score = ai_generated_test(input, models) formatted_tokens = plaigiarism_check( input, year_from, month_from, day_from, year_to, month_to, day_to, domains_to_skip, ) return ( bc_score, mc_score, formatted_tokens, ) def build_date(year, month, day): return f"{year}{months[month]}{day}" # START OF GRADIO title = "Copyright Checker" months = { "January": "01", "February": "02", "March": "03", "April": "04", "May": "05", "June": "06", "July": "07", "August": "08", "September": "09", "October": "10", "November": "11", "December": "12", } with gr.Blocks() as demo: today = date.today() # dd/mm/YY d1 = today.strftime("%d/%B/%Y") d1 = d1.split("/") model_list = ["GPT 3.5", "GPT 4", "CLAUDE", "BARD", "LLAMA2"] domain_list = ["com", "org", "net", "int", "edu", "gov", "mil"] gr.Markdown( """ # Copyright Checker """ ) input_text = gr.Textbox(label="Input text", lines=5, placeholder="") with gr.Row(): with gr.Column(): only_ai_btn = gr.Button("AI Check") with gr.Column(): only_plagiarism_btn = gr.Button("Plagiarism Check") with gr.Column(): submit_btn = gr.Button("Full Check") gr.Markdown( """ ## Output """ ) with gr.Row(): models = gr.Dropdown( model_list, value=model_list, multiselect=True, label="Models to test against", ) with gr.Row(): with gr.Column(): bcLabel = gr.Label(label="Source") with gr.Column(): mcLabel = gr.Label(label="Creator") with gr.Group(): with gr.Row(): month_from = gr.Dropdown( choices=months, label="From Month", value="January", interactive=True, ) day_from = gr.Textbox(label="From Day", value="01") year_from = gr.Textbox(label="From Year", value="2000") # from_date_button = gr.Button("Submit") with gr.Row(): month_to = gr.Dropdown( choices=months, label="To Month", value=d1[1], interactive=True, ) day_to = gr.Textbox(label="To Day", value=d1[0]) year_to = gr.Textbox(label="To Year", value=d1[2]) # to_date_button = gr.Button("Submit") with gr.Row(): domains_to_skip = gr.Dropdown( domain_list, multiselect=True, label="Domain To Skip", ) with gr.Row(): with gr.Column(): sentenceBreakdown = gr.HighlightedText( label="Plagiarism Sentence Breakdown", combine_adjacent=True, color_map={ "[1]": "red", "[2]": "orange", "[3]": "yellow", "[4]": "green", }, ) submit_btn.click( fn=main, inputs=[ input_text, models, year_from, month_from, day_from, year_to, month_to, day_to, domains_to_skip, ], outputs=[ bcLabel, mcLabel, sentenceBreakdown, ], api_name="main", ) only_ai_btn.click( fn=ai_generated_test, inputs=[input_text, models], outputs=[ bcLabel, mcLabel, ], api_name="ai_check", ) only_plagiarism_btn.click( fn=plagiarism_check, inputs=[ input_text, year_from, month_from, day_from, year_to, month_to, day_to, domains_to_skip, ], outputs=[ sentenceBreakdown, ], api_name="plagiarism_check", ) date_from = "" date_to = "" demo.launch()