Tabasum Shah@Tabasum

It is an innovative Streamlit-powered application designed to revolutionize document handling. It supports multi-format uploads, including PDFs, DOCX, PPTX, and XLSX, and leverages AI for advanced processing tasks like summarization, quiz generation, and content combination. The app provides an intuitive drag-and-drop interface, real-time insights through a responsive UI, and professional PDF export options. Key features include AI-powered summarization for concise content extraction, quiz generation in multiple formats (MCQs, True/False, etc.), and combining documents while maintaining structure and coherence. With a sleek, modern design enhanced by animations, the Smart Document Processor ensures a seamless user experience. It is built on cutting-edge technologies like Python libraries (ReportLab, Pandas) and Groq API for AI integration. This tool is perfect for use cases in hackathons, education, and corporate settings, automating tedious document tasks and saving valuable time. The app’s potential for real-time analytics and future enhancements like cloud integration make it a forward-thinking solution for document management. Whether you are a student, educator, or professional, the Smart Document Processor is tailored to empower you with intelligent automation and robust capabilities.

This project aims to develop a Retrieval-Augmented Generation (RAG) application designed to predict lung cancer risks using a comprehensive health dataset. The dataset includes critical features such as gender, age, smoking habits, anxiety, chronic diseases, and symptoms like coughing, wheezing, and chest pain. The RAG framework combines a robust retrieval system with generative AI to deliver precise and contextual insights. By analyzing input data, the application predicts the likelihood of lung cancer and provides evidence-based recommendations for early detection and intervention. Built on Groq and deployed via Streamlit, this solution empowers healthcare providers and individuals to make informed decisions.

The AI-Powered Space Tutor simplifies access to space data for researchers, educators, and enthusiasts. Using Groq API, OpenAI models, NASA datasets, and Tavily search, it delivers AI-driven insights and real-time space exploration knowledge through an intuitive Streamlit UI. A RAG model ensures accurate, context-aware responses, while the FastAPI backend enables seamless interaction. This project democratizes space education, making complex data more accessible and engaging. Technologies Used: Python, FastAPI, Streamlit Tavily Langchain LangGraph Unicorn Groq API (Llama 3), OpenAI (GPT-4o) NASA API, Tavily Search API Local & cloud deployment