---
language: en
tags:
- toxic-content
- text-classification
- keras
- tensorflow
- deep-learning
- safety
- multiclass
license: mit
datasets:
- custom
metrics:
- accuracy
- f1
pipeline_tag: text-classification
model-index:
- name: Toxic_Classification
  results: []
---


# Toxic-Predict

Toxic-Predict is a machine learning project developed as part of the Cellula Internship, focused on safe and responsible multi-modal toxic content moderation. It classifies text queries and image descriptions into nine toxicity categories such as "Safe", "Violent Crimes", "Non-Violent Crimes", "Unsafe", and others. The project leverages deep learning (Keras/TensorFlow), NLP preprocessing, and benchmarking with modern transformer models to build and evaluate a robust multi-class toxic content classifier.

---

## 🚩 Project Context

This project is part of the **Cellula Internship** proposal:  
**"Safe and Responsible Multi-Modal Toxic Content Moderation"**  
The goal is to build a dual-stage moderation pipeline for both text and images, combining hard guardrails (Llama Guard) and soft classification (DistilBERT/Deep Learning) for nuanced, policy-compliant moderation.

---


## Features

- Dual-stage moderation: hard filter (Llama Guard) + soft classifier (DistilBERT/CNN/LSTM)
- Data cleaning, preprocessing, and label encoding
- Tokenization and sequence padding for text data
- Deep learning and transformer-based models for multi-class toxicity classification
- Evaluation metrics: classification report and confusion matrix
- Jupyter notebooks for data exploration and model development
- Streamlit web app for demo and deployment

---

---

## Usage

- **Preprocessing and Tokenization:**  
  See `notebooks/Preprocessing.ipynb` and `notebooks/tokenization.ipynb` for step-by-step data cleaning, splitting, and tokenization.
- **Model Training:**  
  Model architecture and training code are in `models/model.py`.
- **Inference:**  
  Load the trained model (`models/toxic_classifier.h5` or `.keras`) and tokenizer (`data/tokenizer.pkl`) to predict toxicity categories for new samples.

---

## Data

- CSV files with columns: `query`, `image descriptions`, `Toxic Category`, and `Toxic Category Encoded`.
- Data splits: `train.csv`, `eval.csv`, `test.csv`, and `cleaned.csv` for processed data.
- 9 categories: Safe, Violent Crimes, Elections, Sex-Related Crimes, Unsafe, Non-Violent Crimes, Child Sexual Exploitation, Unknown S-Type, Suicide & Self-Harm.

---

## Model

- Deep learning model built with Keras (TensorFlow backend).
- Multi-class classification with label encoding for toxicity categories.
- Benchmarking with PEFT-LoRA DistilBERT and baseline CNN/LSTM.

---

## Evaluation

- Classification report and confusion matrix are generated for model evaluation.
- See the evaluation steps in `notebooks/Preprocessing.ipynb`.

---

language: en

## 🤗 Hugging Face Inference

This model is available on the Hugging Face Hub: [NightPrince/Toxic_Classification](https://huggingface.co/NightPrince/Toxic_Classification)

### Inference API Usage

You can use the Hugging Face Inference API or widget with two fields:

- `text`: The main query or post text
- `image_desc`: The image description (if any)

**Example (Python):**

```python
from huggingface_hub import InferenceClient
client = InferenceClient("NightPrince/Toxic_Classification")
result = client.text_classification({
    "text": "This is a dangerous post",
    "image_desc": "Knife shown in the image"
})
print(result)  # {'label': 'toxic', 'score': 0.98}
```

### Custom Pipeline Details

- The model uses a custom `pipeline.py` for multi-input inference.
- The output is a dictionary with the predicted `label` (class name) and `score` (confidence).
- Class names are mapped using `label_map.json`.

**Files in the repo:**
- `pipeline.py` (custom inference logic)
- `tokenizer.json` (Keras tokenizer)
- `label_map.json` (class code to name mapping)
- TensorFlow SavedModel files (`saved_model.pb`, `variables/`)

**Requirements:**
```
tensorflow
keras
numpy
```

---

---

## 📚 Resources

- [Cellula Internship Project Proposal](#)  
- [BLIP: Bootstrapped Language-Image Pre-training](https://github.com/salesforce/BLIP)
- [Llama Guard](https://llama.meta.com/llama-guard/)
- [DistilBERT](https://huggingface.co/distilbert-base-uncased)
- [Streamlit](https://streamlit.io/)

---

## License

MIT License

---

**Author:** Yahya Muhammad Alnwsany  
**Contact:** yahyaalnwsany39@gmail.com  
**Portfolio:** https://nightprincey.github.io/Portfolio/