This is a Gradio-based web application that generates questions from a provided paragraph. The model processes the text, tokenizes the sentences, and produces relevant questions based on the content. This is a useful tool for educators, students, and content creators who need to quickly generate questions from any given text.
- 📝 Text Analysis: Generates questions from paragraphs by analyzing sentence structure.
- 🌐 Named Entity Recognition: Recognizes names, locations, and organizations to ask relevant questions.
- 🔄 Fallback Mechanism: If the default tokenizer fails, a fallback method ensures the process continues smoothly.
- 🎯 Question Generation: Generates questions from different sentence types like statements, dates, and named entities.
- 🧠 Robust Handling: Handles a variety of text structures and content types, including complex and simple sentences.
- Input: A paragraph on a historical event.
- Output: Questions such as:
- "Who was the leader during the event?"
- "What happened in 1945?"
- "Why did the event occur?"
- Input: A paragraph in the provided text box.
- Output: A list of questions generated from the content of the paragraph.
- Gradio: A Python library to quickly create UIs for machine learning models. Used here to build the interactive interface.
- NLTK (Natural Language Toolkit): A library for working with human language data, used for sentence tokenization and part-of-speech tagging.
- Regular Expressions (Regex): Used to handle patterns and structure sentences for question generation.
- Named Entity Recognition (NER): For recognizing entities such as people, locations, and organizations in the text.