import streamlit as st import os import requests import pickle import pandas as pd import nltk import spacy from nltk.corpus import stopwords from nltk.tokenize import word_tokenize, sent_tokenize import numpy as np ############ from nltk.stem import WordNetLemmatizer from nltk import ne_chunk, pos_tag, word_tokenize from nltk.tree import Tree from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer nltk.download('wordnet') nltk.download('maxent_ne_chunker') nltk.download('words') ####### nltk.download('punkt') nltk.download('stopwords') nltk.download('averaged_perceptron_tagger') #version st.markdown("v1.888") # URL of the text file url = 'https://jaifar.net/text.txt' headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3', } response = requests.get(url, headers=headers) # Check if the request was successful if response.status_code == 200: # Read the content of the file content = response.text # Print the content of the file # print(content) else: # Handle the case when the request fails print('Failed to download the file.') #title st.title("Smart Detection System of AI-Generated Text Models") #subtitle st.markdown("This is a POC for Smart Detection System of AI Generated Text Models project (:blue[MSc Data Analytics]), it is a pre-trained model that detect the probablities of using any of the known LLM (chatgpt3, chatgpt4, GoogleBard, HuggingfaceChat)") #input text input_paragraph = st.text_area("Input your text here") words_counts = word_tokenize(input_paragraph) final_words = len(words_counts) st.write('Words counts: ', final_words) # Define your options options = ["AI vs AI - RandomForest - 88 Samples", "AI vs AI - Ridge - 2000 Samples", "AI vs Human"] # Create a dropdown menu with "Option 2" as the default # selected_option = st.selectbox('Select an Option', options, index=1) selected_option = st.selectbox('Select an Option', options) # Check if the file exists if not os.path.isfile('AI_vs_AI_Ridge_2000_Samples.pkl'): # Download the zip file if it doesn't exist url = 'https://jaifar.net/AI_vs_AI_Ridge_2000_Samples.pkl' headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3', } response = requests.get(url, headers=headers) # Save the file with open('AI_vs_AI_Ridge_2000_Samples.pkl', 'wb') as file2: file2.write(response.content) # df = pd.DataFrame(columns=["paragraph"]) # df = df.append({"paragraph": input_paragraph}, ignore_index=True) df = pd.DataFrame([input_paragraph], columns=["paragraph"]) # Variable to control number of words to retrieve num_words = 500 # Retrieving only the first num_words words of the paragraph input_paragraph = ' '.join(word_tokenize(input_paragraph)[:num_words]) # Extracting features def extract_features_AI_vs_AI_RandomForest_88_Samples(text): words = word_tokenize(text) sentences = sent_tokenize(text) avg_word_length = sum(len(word) for word in words if word.isalpha()) / len(words) avg_sent_length = sum(len(sent) for sent in sentences) / len(sentences) punctuation_count = len([char for char in text if char in '.,;:?!']) stopword_count = len([word for word in words if word in stopwords.words('english')]) lemmatizer = WordNetLemmatizer() lemma_count = len(set(lemmatizer.lemmatize(word) for word in words)) named_entity_count = len([chunk for chunk in ne_chunk(pos_tag(words)) if isinstance(chunk, Tree)]) tagged_words = nltk.pos_tag(words) pos_counts = nltk.FreqDist(tag for (word, tag) in tagged_words) pos_features = { 'pos_IN': pos_counts['IN'], 'pos_DT': pos_counts['DT'], 'pos_NN': pos_counts['NN'], 'pos_,': pos_counts[','], 'pos_VBZ': pos_counts['VBZ'], 'pos_WDT': pos_counts['WDT'], 'pos_TO': pos_counts['TO'], 'pos_VB': pos_counts['VB'], 'pos_VBG': pos_counts['VBG'], 'pos_.': pos_counts['.'], 'pos_JJ': pos_counts['JJ'], 'pos_NNS': pos_counts['NNS'], 'pos_RB': pos_counts['RB'], 'pos_CC': pos_counts['CC'], 'pos_VBN': pos_counts['VBN'], } features = { 'avg_word_length': avg_word_length, 'avg_sent_length': avg_sent_length, 'punctuation_count': punctuation_count, 'stopword_count': stopword_count, 'lemma_count': lemma_count, 'named_entity_count': named_entity_count, } features.update(pos_features) return pd.Series(features) # Extracting features for AI_vs_AI_Ridge_2000_Samples def extract_features_AI_vs_AI_Ridge_2000_Samples(text): words = word_tokenize(text) sentences = sent_tokenize(text) avg_word_length = sum(len(word) for word in words if word.isalpha()) / len(words) avg_sent_length = sum(len(sent) for sent in sentences) / len(sentences) punctuation_count = len([char for char in text if char in '.,;:?!']) stopword_count = len([word for word in words if word in stopwords.words('english')]) lemmatizer = WordNetLemmatizer() lemma_count = len(set(lemmatizer.lemmatize(word) for word in words)) named_entity_count = len([chunk for chunk in ne_chunk(pos_tag(words)) if isinstance(chunk, Tree)]) tagged_words = nltk.pos_tag(words) pos_counts = nltk.FreqDist(tag for (word, tag) in tagged_words) pos_features = { 'pos_IN': pos_counts['IN'], 'pos_DT': pos_counts['DT'], 'pos_NN': pos_counts['NN'], 'pos_,': pos_counts[','], 'pos_VBZ': pos_counts['VBZ'], 'pos_WDT': pos_counts['WDT'], 'pos_TO': pos_counts['TO'], 'pos_VB': pos_counts['VB'], 'pos_PRP': pos_counts['PRP'], 'pos_VBP': pos_counts['VBP'], 'pos_VBG': pos_counts['VBG'], 'pos_.': pos_counts['.'], 'pos_JJ': pos_counts['JJ'], 'pos_NNS': pos_counts['NNS'], 'pos_RB': pos_counts['RB'], 'pos_PRP$': pos_counts['PRP$'], 'pos_CC': pos_counts['CC'], 'pos_MD': pos_counts['MD'], 'pos_VBN': pos_counts['VBN'], 'pos_NNP': pos_counts['NNP'], } features = { 'avg_word_length': avg_word_length, 'avg_sent_length': avg_sent_length, 'punctuation_count': punctuation_count, 'stopword_count': stopword_count, 'lemma_count': lemma_count, 'named_entity_count': named_entity_count, } # features.update(pos_features) features = pd.concat([features, pd.DataFrame(pos_features, index=[0])], axis=1) return pd.Series(features) # Function from Code(2) def add_vectorized_features(df): vectorizer = CountVectorizer() tfidf_vectorizer = TfidfVectorizer() X_bow = vectorizer.fit_transform(df['paragraph']) X_tfidf = tfidf_vectorizer.fit_transform(df['paragraph']) df_bow = pd.DataFrame(X_bow.toarray(), columns=vectorizer.get_feature_names_out()) df_tfidf = pd.DataFrame(X_tfidf.toarray(), columns=tfidf_vectorizer.get_feature_names_out()) df = pd.concat([df, df_bow, df_tfidf], axis=1) return df # Function define AI_vs_AI_RandomForest_88_Samples def AI_vs_AI_RandomForest_88_Samples(df): try: # Check if the file exists if not os.path.isfile('AI_vs_AI_RandomForest_88_Samples.pkl'): # Download the zip file if it doesn't exist url = 'https://jaifar.net/AI_vs_AI_RandomForest_88_Samples.pkl' headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3', } response = requests.get(url, headers=headers) if response.status_code != 200: raise Exception("Failed to download the file") # Save the file with open('AI_vs_AI_RandomForest_88_Samples.pkl', 'wb') as file: file.write(response.content) # At this point, the pickle file should exist, either it was already there, or it has been downloaded and extracted. with open('AI_vs_AI_RandomForest_88_Samples.pkl', 'rb') as file: clf_loaded = pickle.load(file) input_features = df['paragraph'].apply(extract_features_AI_vs_AI_RandomForest_88_Samples) predicted_llm = clf_loaded.predict(input_features) st.write(f"Predicted LLM: {predicted_llm[0]}") predicted_proba = clf_loaded.predict_proba(input_features) probabilities = predicted_proba[0] labels = clf_loaded.classes_ # Create a mapping from old labels to new labels label_mapping = {1: 'gpt3', 2: 'gpt4', 3: 'googlebard', 4: 'huggingface'} # Apply the mapping to the labels new_labels = [label_mapping[label] for label in labels] # Create a dictionary that maps new labels to probabilities prob_dict = {k: v for k, v in zip(new_labels, probabilities)} # Convert probabilities to percentages and sort the dictionary in descending order prob_dict = {k: f'{v*100:.2f}%' for k, v in sorted(prob_dict.items(), key=lambda item: item[1], reverse=True)} # Create a progress bar and a bar chart for each LLM for llm, prob in prob_dict.items(): st.write(llm + ': ' + prob) st.progress(float(prob.strip('%'))/100) return except Exception as e: st.write(f"An error occurred: {str(e)}") def AI_vs_AI_Ridge_2000_Samples(df): # At this point, the pickle file should exist, either it was already there, or it has been downloaded and extracted. with open('AI_vs_AI_Ridge_2000_Samples.pkl', 'rb') as file2: clf_loaded = pickle.load(file2) input_features = df['paragraph'].apply(extract_features_AI_vs_AI_Ridge_2000_Samples) # Here, input_features is a DataFrame, not a Series input_features = pd.concat(input_features.values, ignore_index=True) # Add new vectorized features df = add_vectorized_features(df) # Concatenate input_features and df along columns final_features = pd.concat([input_features, df], axis=1) predicted_llm = clf_loaded.predict(final_features) st.write(f"Predicted LLM: {predicted_llm[0]}") return # Creates a button press_me_button = st.button("Which Model Used?") if press_me_button: # Use the selected option to control the flow of your application if selected_option == "AI vs AI - RandomForest - 88 Samples": AI_vs_AI_RandomForest_88_Samples(df) elif selected_option == "AI vs AI - Ridge - 2000 Samples": AI_vs_AI_Ridge_2000_Samples(df) elif selected_option == "AI vs Human": st.write("You selected AI vs Human!")