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from fastai.vision.all import *
import gradio as gr
import requests
import base64
from bs4 import BeautifulSoup
import os
# Load the trained model
learn = load_learner('nsfw_model.pkl')
labels = learn.dls.vocab
def analyze(url):
"""Analyzer function that classifies the images found at the given URL"""
# Make sure URL starts with http or https
# TODO: confirm that the url points to a web page, and not some resource.
# Regex could be useful here
if not url.startswith(('http://','https://')):
url = 'http://'+url
safety = 'safe' # our return variable
# Extract html and all img tags
html = requests.get(url)
soup = BeautifulSoup(html.text, "html.parser")
img_elements = soup.find_all("img")
# Save all src urls that we can clearly tell are img urls.
# A better approach would be to use regex here
srcs = []
for img in img_elements:
for v in img.attrs.values():
if isinstance(v, str):
if v.lower().endswith(('jpg', 'png', 'gif', 'jpeg')):
srcs.append(v)
# Get the images from the urls and classify
# If there is a single unsafe image, report it.
for src_url in srcs:
try:
img_data = requests.get(src_url).content
temp = 'temp.' + src_url.lower().split('.')[-1]
with open(temp, 'wb') as handler:
handler.write(img_data)
is_nsfw,_,probs = learn.predict(PILImage.create(temp))
os.remove(temp)
if is_nsfw == "unsafe_searches":
safety = 'NOT safe'
return safety
except Exception as e:
pass
return safety
title = "Website Safety Analyzer"
description = "**The internet is not safe for children**. Even if we know the 'bad' sites, social media is hard to regulate. \n"+\
"This is step one in an attempt to solve that. An image classifier that audits every image at a URL. \n"+\
"In this iteration, I classify sites with sexually explicit content as **'NOT safe'**. \n\n"+\
"There is a long way to go with NLP for profanity, cyber-bullying, as well as CV for violence, substance abuse, etc. \n"+\
"Another step will be to convert this into a browser extension/add-on. \n"+\
"I welcome any help on this. 🙂"
examples = ['pornhub.com', 'cnn.com', 'xvideos.com', 'www.pinterest.com']
enable_queue=True
iface = gr.Interface(
fn=analyze,
inputs="text",
outputs="text",
title=title,
description=description,
examples=examples,
)
iface.launch(enable_queue=enable_queue) |