NLP for Medicine

Project Description

Festival AI Assistant 🇮🇳🪔 is an AI-powered cultural assistant designed to provide detailed information about Indian festivals, with a special focus on the Telugu language. The application supports translation, text-to-speech (TTS), and AI-powered matching to help users easily learn about the origin, purpose, celebrations, and cultural aspects of various festivals.

✨ Key Features 🔍 Festival Search – Look up festivals from a structured dataset. 🌐 Telugu Translation – Automatic translation of festival details into Telugu. 🔊 Voice Output – Integrated Text-to-Speech (TTS) for audio playback. 🤖 AI Matching – Uses Sentence Transformers to find the closest matching festival from user input. 📊 Data-driven – Festival information sourced from a CSV dataset. 🖥️ Interfaces – Works via CLI and Streamlit GUI. 🛠️ Tech Stack Python 🐍 pandas (data handling) Sentence Transformers (semantic search) Google Translator API (translation to Telugu) gTTS (Google Text-to-Speech) (audio output) Streamlit (interactive UI) 🎯 Objective

To create an accessible, cultural AI assistant that:

Promotes awareness of Indian festivals. Supports local languages (starting with Telugu). Engages users with text + speech output.

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.