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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]}")
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