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README.md

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-# 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.
-
----
-
-## Project Structure
-
-```
-.
-├── app.py
-├── run.py
-├── test.py
-├── requirements.txt
-├── README.md
-├── data/
-│   ├── cellula-toxic.csv
-│   ├── cleaned.csv
-│   ├── eval.csv
-│   ├── test.csv
-│   ├── tokenizer.pkl
-│   └── train.csv
-├── models/
-│   ├── model.py
-│   ├── toxic_classifier.h5
-│   └── toxic_classifier.keras
-├── notebooks/
-│   ├── Preprocessing.ipynb
-│   └── tokenization.ipynb
-└── src/
-    ├── preprocess.py
-    └── tokenize_and_split.py
-```
-
----
-
-## 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
-
----
-
-## Setup
-
-1. **Clone the repository**
-   ```sh
-   git clone https://github.com/yourusername/toxic-classification.git
-   cd toxic-predict
-   ```
-
-2. **Install dependencies**
-   ```sh
-   pip install -r requirements.txt
-   ```
-
-3. **Prepare data**
-   - Place your data files in the `data/` directory if not already present.
-
-4. **Train the model**
-   - Use the scripts in `src/` or the Jupyter notebooks in `notebooks/` to preprocess data and train the model.
-
-5. **Run predictions**
-   - Use `app.py` or `run.py` to run inference on new data.
-
----
-
-## 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:** [email protected]  
-**Portfolio:** https://nightprincey.github.io/Portfolio/
+
+---
+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:** [email protected]  
+**Portfolio:** https://nightprincey.github.io/Portfolio/