The Text Summarizer Application is a robust, production-grade application designed for efficient text summarization. By leveraging the principles of modular coding, this project ensures scalability, maintainability, and ease of deployment.

• Modular Coding: Adopts a modular approach to coding, ensuring that the application is structured, maintainable, and scalable.
• Logging and Exception Handling: Includes comprehensive logging and exception handling modules to enhance the reliability and debuggability of the application.
• Deployable Docker Image: Provides a Docker image for seamless deployment, ensuring consistency across different environments.
• CI/CD Pipeline: Implements a GitHub workflow CI/CD pipeline for automated testing, building, and deployment.
• Text Summarization: Utilizes the Google Pegasus model from the Hugging Face Transformers library, fine-tuned on the Samsun dataset, to generate high-quality text summaries.
• Web Application: Features a simple web app built using Flask as the backend framework for easy user interaction.

• Hugging Face Transformers: For the Google Pegasus text summarization model.
• Samsun Dataset: For fine-tuning the model.
• Flask: For the backend framework of the web application.
• Docker: For containerizing the application.
• GitHub Actions: For setting up the CI/CD pipeline.

The main.py acts as training pipeline and src/TextSummarizer/pipeline/prediction.py acts as prediction pipeline.