app.py

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
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.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)

# 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) / 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)