app.py

import streamlit as st

#title 
st.title("Smart Detection System of AI-Generated Text Models")

#subtitle 
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 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")

df = pd.DataFrame(columns=["paragraph"])
df = df.append({"paragraph": input_paragraph}, ignore_index=True)



# 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("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]}")

    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)