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{
"cells": [
{
"cell_type": "markdown",
"id": "d5c4b5c2-8c0a-4cbe-8997-1a98c14be2e4",
"metadata": {},
"source": [
"A text classification model using libraries like NLTK or SpaCy. It includes some PII data within the code (e.g., hard-coded email addresses or phone numbers for testing purposes), and include a few API tokens/secrets."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f95fa380-34d0-455d-8002-ebe5f829542c",
"metadata": {},
"outputs": [],
"source": [
"# Vulnerable libraries\n",
"!pip install django==1.11.15\n",
"!pip install flask==0.12.2\n",
"!pip install numpy==1.16.0\n",
"!pip install requests==2.19.1\n",
"!pip install scikit-learn==0.19.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "25315022-9da9-4c29-8326-6532d261dd56",
"metadata": {},
"outputs": [],
"source": [
"# Non-permissible licensed libraries\n",
"import gmpy2\n",
"import oct2py\n",
"import pygsl\n",
"from PyQt5 import QtCore"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "489ad824-285c-4219-afc6-073192d54f3e",
"metadata": {},
"outputs": [],
"source": [
"# Required Libraries for our task\n",
"import nltk\n",
"import sklearn"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "109d2f98-4d6d-42d9-acb4-2f195af051d5",
"metadata": {},
"outputs": [],
"source": [
"# PII Data\n",
"email = \"john.doe@example.com\"\n",
"phone = \"123-456-7890\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d637e295-0953-4980-bf99-c7e7e509e876",
"metadata": {},
"outputs": [],
"source": [
"# API Keys and secrets\n",
"fb_app_secret = \"3e4a22bb7e6b2c38b7809234b3ee782b\"\n",
"db_credentials = \"username:password@localhost:5432/mydatabase\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a6493567-ad7f-4b87-95e4-5068a09fca92",
"metadata": {},
"outputs": [],
"source": [
"# Download nltk data\n",
"nltk.download('punkt', download_dir='/nltk_data/')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f94e191-bfe7-4e54-9dbf-4d2484b0dbe9",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# Text Classification\n",
"from sklearn.datasets import fetch_20newsgroups\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from sklearn.feature_extraction.text import TfidfTransformer\n",
"from sklearn.naive_bayes import MultinomialNB"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8552e84a-e164-4519-8ce8-959c7dd277ef",
"metadata": {},
"outputs": [],
"source": [
"# Load Data\n",
"categories = ['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']\n",
"twenty_train = fetch_20newsgroups(subset='train', categories=categories, shuffle=True, random_state=42)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "abc38386-e63f-4d22-81dc-1785ac8f043b",
"metadata": {},
"outputs": [],
"source": [
"# Feature Extraction\n",
"count_vect = CountVectorizer()\n",
"X_train_counts = count_vect.fit_transform(twenty_train.data)\n",
"tfidf_transformer = TfidfTransformer()\n",
"X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ea57698-12ff-48b3-a8b6-bb8dffabbc5f",
"metadata": {},
"outputs": [],
"source": [
"# Train Model\n",
"clf = MultinomialNB().fit(X_train_tfidf, twenty_train.target)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a5fa6532-594c-4790-a630-83388c556591",
"metadata": {},
"outputs": [],
"source": [
"# Predict\n",
"docs_new = ['God is love', 'OpenGL on the GPU is fast']\n",
"X_new_counts = count_vect.transform(docs_new)\n",
"X_new_tfidf = tfidf_transformer.transform(X_new_counts)\n",
"predicted = clf.predict(X_new_tfidf)\n",
"for doc, category in zip(docs_new, predicted):\n",
" print('%r => %s' % (doc, twenty_train.target_names[category]))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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