import streamlit as st #subtitle st.markdown("version: 1.0") #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)") 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 joblib import dump, load nltk.download('wordnet') nltk.download('maxent_ne_chunker') nltk.download('words') ####### nltk.download('punkt') nltk.download('stopwords') nltk.download('averaged_perceptron_tagger') # Check if the file exists if not os.path.isfile('RandomForestClassifier.joblib'): url = 'https://jaifar.net/RandomForestClassifier.joblib' 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) with open('RandomForestClassifier.joblib', 'wb') as file: file.write(response.content) # Load the model from the file clf_loaded = load('RandomForestClassifier.joblib') # # At this point, the pickle file should exist, either it was already there, or it has been downloaded and extracted. # with open('RandomForestClassifier.pkl', 'rb') as file: # clf_loaded = pickle.load(file) 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) # 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(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) # Creates a button named 'Press me' press_me_button = st.button("Which Model Used?") if press_me_button: input_features = df['paragraph'].apply(extract_features) 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)} # Print the dictionary #st.write(prob_dict) # 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) # 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("v1.9") # # 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): # input_features = df['paragraph'].apply(extract_features_AI_vs_AI_RandomForest_88_Samples) # # try: # # predicted_llm = clf_loaded.predict(input_features) # # st.write(f"Predicted LLM: {predicted_llm[0]}") # # predicted_proba = clf_loaded.predict_proba(input_features) # # except Exception as e: # # st.write(f"An error occurred: {str(e)}") # # 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)} # # # Print the dictionary # # #st.write(prob_dict) # # # 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 # 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 # # Check if the file exists # if not os.path.isfile('RandomForestClassifier.pkl'): # # Download the zip file if it doesn't exist # url = 'https://jaifar.net/RandomForestClassifier.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 # try: # with open('RandomForestClassifier.pkl', 'wb') as file: # file.write(response.content) # except Exception as e: # st.write(f"An error occurred while writing RandomForestClassifier.pkl: {str(e)}") # try: # with open('RandomForestClassifier.pkl', 'rb') as file: # clf_loaded = pickle.load(file) # except Exception as e: # st.write(f"An error occurred while loading RandomForestClassifier.pkl: {str(e)}") # # Creates a button # press_me_button = st.button("Which Model Used?") # if press_me_button: # input_features = df['paragraph'].apply(extract_features_AI_vs_AI_RandomForest_88_Samples) # try: # predicted_llm = clf_loaded.predict(input_features) # st.write(f"Predicted LLM: {predicted_llm[0]}") # predicted_proba = clf_loaded.predict_proba(input_features) # except Exception as e: # st.write(f"An error occurred: {str(e)}") # # # 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!")