Spaces:
Sleeping
Sleeping
ayoni02
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d9fdfda
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Parent(s):
17297b3
first commit
Browse files
ap.py
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# import modules
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from flask import Flask, render_template, session, redirect, url_for
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from flask_wtf import FlaskForm
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from wtforms import SubmitField, TextAreaField
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import env_variable
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import main
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app = Flask(__name__)
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app.config['SECRET_KEY'] = env_variable.app_security_key
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class TextForm(FlaskForm):
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text = TextAreaField('Text')
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submit = SubmitField('Send')
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@app.route('/', methods=['GET', 'POST'])
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def index():
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form = TextForm()
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if form.validate_on_submit(): # Check if form is submitted
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session['text'] = form.text.data
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return redirect(url_for("moderator"))
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return render_template('home.html', form=form)
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@app.route('/moderator')
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def moderator():
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text = session['text']
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result = main.content_moderator(text)
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return render_template('moderator.html', result=result)
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if __name__ == '__main__':
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app.run(debug=True)
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app.py
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import streamlit as st
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import main
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def main():
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text = st.text_input("Enter the text to classify: ")
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if text:
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results = main.content_moderator(text)
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st.write(results)
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else:
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st.write("Please enter some text to classify")
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st.write("Waiting for input...")
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main()
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main.py
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from transformers import pipeline
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import requests as re
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labels = ['toxic', 'racist', 'gender bias', 'religious bias', 'aggressive',
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'personal attacks', 'hate speech', 'offensive language']
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# model chosen is fbbart
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model = pipeline("zero-shot-classification",
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model="facebook/bart-large-mnli")
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def zero_shot_classifier(text, model=model, labels=labels):
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text = text
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prediction = model(text, labels, multi_label=True)
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if prediction["scores"][0] < 0.75:
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return False
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else:
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return str(prediction["labels"][0])
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# catch darkweb links
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def dark_web_links(text):
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# Regular expression pattern to match common dark web link formats
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dark_web_pattern = r"(https?://)?[a-z0-9]+\.onion(/[a-zA-Z0-9]*)*"
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# Search for dark web links in the text
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matches = re.findall(dark_web_pattern, text)
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if matches:
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return True
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else:
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return False
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# catch adult content links
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def adult_content_sites(text):
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# Regular expression pattern to match common adult content websites
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adult_content_pattern = r"(https?://)?(?:www\.)?(pornhub\.com|xnxx\.com|youporn\.com|redtube\.com|etc)\b"
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# Search for adult content sites in the text
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matches = re.findall(adult_content_pattern, text)
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# If any matches are found, flag them
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if matches:
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return True
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else:
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return False
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def content_moderator(text, labels=labels, model=model, classifier=zero_shot_classifier, dkweb=dark_web_links, adult=adult_content_sites):
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if dkweb(text):
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return 'Darkweb link'
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elif adult(text):
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return 'Adult content site'
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elif classifier(text):
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return 'Flagged content'
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requirements.txt
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transformers
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torch
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streamlit
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