Spaces:
Running
Running
| import streamlit as st | |
| import os | |
| import requests | |
| import pickle | |
| import pandas as pd | |
| import nltk | |
| from nltk.corpus import stopwords | |
| from nltk.tokenize import word_tokenize, sent_tokenize | |
| from nltk.stem import WordNetLemmatizer | |
| import numpy as np | |
| nltk.download('punkt') | |
| nltk.download('stopwords') | |
| nltk.download('averaged_perceptron_tagger') | |
| nltk.download('wordnet') # needed for lemmatization | |
| # Setting up Hugging Face API for NER | |
| API_URL = "https://api-inference.huggingface.co/models/spacy/en_core_web_sm" | |
| headers = {"Authorization": "Bearer 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"} | |
| def get_entities(text): | |
| data = {"inputs": text} | |
| response = requests.post(API_URL, headers=headers, json=data) | |
| entities = [item['entity_group'] for item in response.json()[0]] | |
| return len(entities) | |
| # Set up lemmatizer | |
| lemmatizer = WordNetLemmatizer() | |
| #title | |
| st.title("Smart Detection System of AI-Generated Text Models") | |
| st.markdown("## This is a POC repo for Smart Detection System of AI Generated Text Models project, it is a pre-trained model that detect the probabilities of using any of the known LLM (chatgpt3, chatgpt4, GoogleBard, HuggingfaceChat)##") | |
| # 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 | |
| with open('RandomForestClassifier.pkl', 'wb') as file: | |
| file.write(response.content) | |
| with open('RandomForestClassifier.pkl', 'rb') as file: | |
| clf_loaded = pickle.load(file) | |
| input_paragraph = st.text_area("Input your text here") | |
| df = pd.DataFrame(columns=["paragraph"]) | |
| df = df.append({"paragraph": input_paragraph}, ignore_index=True) | |
| num_words = 500 | |
| input_paragraph = ' '.join(word_tokenize(input_paragraph)[:num_words]) | |
| def extract_features(text): | |
| words = word_tokenize(text) | |
| sentences = sent_tokenize(text) | |
| avg_word_length = sum(len(word) for word in words) / 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')]) | |
| lemma_count = len(set(lemmatizer.lemmatize(word) for word in words)) | |
| named_entity_count = get_entities(text) | |
| 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) | |
| press_me_button = st.button("Press me") | |
| 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]}") | |