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LLM Application

Smart Cab Finder is an AI-powered ride comparison platform that helps users compare fares, estimated arrival times, and ride options across multiple cab providers. The application includes an intelligent AI assistant for ride recommendations, supports Bring Your Own Key (BYOK) integrations, and is designed to support local AI inference through Ollama. To improve accessibility, the platform provides multilingual support in English, Hindi, and Telugu. Built using Python and Flask, the project follows secure development practices with automated testing, CI/CD pipelines, security scanning, and compliance-driven documentation.

A fully local Retrieval-Augmented Generation (RAG) chatbot built using Streamlit, Ollama, ChromaDB, and open-source LLMs like Qwen and Llama.

Project Description An AI-powered Healthcare LLM designed to assist visually impaired (blind) individuals. The system provides accessible medical guidance through natural language processing, voice interaction, and context-aware support. This project aims to enhance healthcare accessibility, improve independence, and empower blind users by bridging the gap between medical knowledge and inclusivity.

A Streamlit-based music library application that allows users to browse, search, and stream audio tracks according to thier mood and also it is an otp sign in thing where it is integrated with swecha apis. Built with Python and Streamlit, designed for fast deployment.

GramaSakshi is an AI assistant built to bridge the digital gap for villagers and provide smart assistance in healthcare. It empowers people with easy access to information, task management, and medical support tools.

OCU-AI is a smart healthcare assistant that combines Natural Language Processing (NLP) and Computer Vision to assist users in understanding medical conditions and detecting eye diseases from retinal images.

This web-based application has two core components:

Medical Q&A Chatbot

Uses Retrieval-Augmented Generation (RAG) with LangChain, Pinecone, and Phi-3 LLM.

Answers user queries using embedded medical knowledge extracted from PDF files (like textbooks or research).

Designed to explain medical terms in simple, non-technical language.

Always includes a disclaimer to consult a qualified medical professional.

Eye Disease Detection Model

Allows users to upload retina images.

Predicts diseases like:

Cataract

Glaucoma

Diabetic Retinopathy

Normal (Healthy)

Uses a Keras CNN model trained on retina datasets.

After prediction, the chatbot explains the condition using natural language.

This project bridges AI-powered document retrieval, medical imaging, and LLM-based explanation, offering a foundation for real-world smart health applications.