Developed a complete AI-powered PDF Assistant that enables users to upload PDF documents, automatically extract text, generate concise summaries, and ask natural-language questions about the document content.
The application is built entirely in Python with a lightweight Tkinter desktop interface and integrates a Large Language Model through the Hugging Face Inference API to provide intelligent, context-aware responses.
Key Features:
• Upload and analyze PDF documents of various sizes.
• Extract text automatically using PyPDF.
• AI-powered document summarization.
• Ask questions in natural language and receive contextual answers.
• Multi-turn conversation with chat history support.
• Context-aware prompting to reduce hallucinations by restricting responses to document content.
• Responsive desktop interface with background threading for smooth user experience.
• Error handling for invalid documents and API failures.
• Clean, modular, and maintainable Python architecture.
Technologies Used:
Python, Tkinter, PyPDF, Hugging Face API, OpenAI SDK, REST APIs, Multithreading, Object-Oriented Programming, Natural Language Processing (NLP), Large Language Models (LLMs).
This project demonstrates my ability to build production-style AI applications that combine document processing, API integration, natural language understanding, and intuitive user interfaces into a practical solution suitable for education, business, and research workflows.
