Build a "Spam Buster" for user generated IMAGE content based on predefined text, links identified as "known bads", based on which the image should be classified as spam or not-spam. The "known bads" can be updated anytime. The "Spam Buster" would need to analyze at image level, identify text components inside image and build appropriate logic to give a score to the image. A similar use case is employed by Facebook Ads technology to identify if the image ad contains too much text or contains any objectionable text."
We build a flask based micro-service which gives a "Spaminess score" for any uploaded image. The score is derived by aggregating the scores from two models:
- Text based spam classifier - Text is extracted from the image and a pre-trained model runs on the text to classify text in respective bucket
- Image based spam classifier - Convolution neural network which classifies image as spam or not based on the image features
Clone the repo and cd into predict folder
cd predict
pip install -r requirements.txt
python3 app.py
Clone the repo and cd into predict folder
Run the image and bash into it to start the server
docker run --rm -itd --name hack -v /home/mayur/hack/spam_buster/predict:/predict -p 5000:5000 kayush206/img_spam:v2 bash
docker exec -it hack bash
cd /predict
python3 app.py
Access the service- http://localhost:5000/spam_buster/api/v1/model
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