TextVerse: A Streamlit Web Application for Advanced Analysis of PDF and Image Files with and without Language Models

This research paper presents a novel approach to text and PDF analysis through the development of a Streamlit web application. The application offers two main modes of analysis: text analysis without a Language Model (LLM) and text analysis with LLM.In the former mode, users can upload various file formats such as PDFs, PNGs, JPGs, and JPEGs, with support for text extraction and Optical Character Recognition (OCR) for images. Keywords are extracted using the YAKE keyword extractor.In the latter mode, advanced analysis is enabled through LLM utilization, allowing users to input prompts for content summarization and extraction of key points and keywords from PDF files.The application leverages several libraries including Streamlit, PIL, Pytesseract, pdf_extract, yake, pdf2image, and google.generativeai. Instructions for running the application are provided. This paper discusses the architecture, implementation details, and potential applications of the developed system, highlighting its flexibility, usability, and potential impact in various domains requiring text and document analysis