This project builds a machine learning pipeline to classify comments for various types of toxicity using a Bidirectional LSTM model. It includes data preprocessing, training, evaluation, and a user-friendly Gradio interface for prediction.

Kaggle : https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-small

Multi-label classification for six toxicity categories:

Preprocessing with TensorFlow's TextVectorization.

Interactive Gradio interface for testing the model.

Model evaluation with metrics such as Precision, Recall, and Accuracy.

Python 3.7+

TensorFlow 2.0+

Pandas

Numpy

Gradio

Matplotlib

Install dependencies using: pip install tensorflow pandas numpy gradio matplotlib

Text Tokenization and Padding: Utilizes TensorFlow's TextVectorization to preprocess the input text by converting words into numerical tokens and padding sequences to a fixed length.

Data Pipeline Creation: Builds an efficient data pipeline using TensorFlow's Dataset API for caching, shuffling, batching, and prefetching data to improve performance.

Embedding Layer: Converts input text into dense numerical vectors for the model to process.

Bidirectional LSTM Layer: Captures context from both forward and backward directions in the text sequence, enabling the model to understand relationships between words effectively.

Dense Layers: A series of fully connected layers to extract features and classify the input data.

Output Layer: Contains six neurons with sigmoid activation functions for multi-label classification of the toxicity categories.

Model Compilation: The model is compiled using the Adam optimizer and binary cross-entropy loss for multi-label classification.

Evaluation Metrics: The model's performance is evaluated using metrics like Precision, Recall, and Accuracy.

Interactive Interface: Implements a Gradio app to accept user inputs as text and display predictions for each of the six toxicity categories.

Real-time Prediction: Users can input any comment, and the app predicts the likelihood of each toxicity type in real-time.

--Compile the model using the Adam optimizer and binary cross-entropy loss. --Train with 70% of data, validate with 20%, and test with the remaining 10%.

--Implements a Gradio app to accept user input and display toxicity predictions.

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