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| import streamlit as st | |
| #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!") | |