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Smart Hospital Maintenance AI Assistant is a Streamlit-based Retrieval-Augmented Generation (RAG) application designed to assist hospital maintenance teams in analyzing documents and retrieving relevant information efficiently.

The application supports PDF document analysis, OCR-based image semantic search using FAISS vector database, AI-powered question answering, 3 language translation, and voice output generation. It uses sentence-transformers embeddings for document retrieval and integrates Large Language Models to provide context-aware responses based on uploaded hospital maintenance documents.

Key Features: • PDF document upload and analysis • OCR support for image-based extraction • RAG-powered question answering • Semantic search using FAISS • 3 language translation • Voice response generation • Streamlit-based interactive user interface

Tech Stack: • Python • Streamlit • FAISS • Sentence Transformers (all-MiniLM-L6-v2) • PDFPlumber • PyTesseract OCR • Groq API • Deep Translator • gTTS

This project was developed as part of the IP1 ICFAI Internship Streamlit RAG Application assignment.

Overview

This app ingests your uploaded PDF/text notes, builds a FAISS index with embeddings, retrieves relevant passages, and uses a HuggingFace chat model to answer your questions grounded in the uploaded sources.

Response Modes

Research Mode: Structured, evidence-grounded answers with source-aware wording Comparison Mode: Careful comparison of ancient ideas vs modern theories Simple Mode: Plain-language explanations for faster understanding Architecture

User Query → Retrieve from FAISS → HuggingFace Inference API → Answer Output