chatbot

An AI-powered bilingual assistant helping citizens discover government welfare schemes they're eligible for and draft RTI requests. Uses RAG with Google Generative AI, voice input, and real-time eligibility checking for Telangana and India-wide schemes.

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

AI Chat Assistant is a conversational platform built using Streamlit, designed to integrate multiple Large Language Models (LLMs) such as Ollama (local hosting), OpenAI, and Google Gemini. The system provides a flexible interface for seamless interaction across different models while supporting customization through fine-tuned domain-specific datasets. Its architecture emphasizes scalability, multilingual support (including Indic languages), and accessibility, making it suitable for both personal productivity and enterprise use.

The AI Chat Assistant is a modern web-based conversational AI application built with Streamlit and powered by Google's Gemini 2.0 Flash model, delivering intelligent conversations through an intuitive interface. This interactive chatbot features real-time AI responses, session-based chat history management, and smart query handling for predefined responses about the creator and assistant identity. Built with Python 3.8+ and utilizing Google's Generative AI SDK, the application offers a responsive design that works seamlessly across all devices while maintaining clean code architecture with comprehensive error handling. Users can quickly get started by installing dependencies from requirements.txt, configuring their Google AI API key, and running the application with a simple streamlit command. The project includes extensive documentation covering technical implementation, contribution guidelines, and follows Python best practices under the GNU Affero General Public License v3.0, making it both a functional AI chat tool and a solid foundation for advanced conversational AI implementations.

BakBak-Bot

BakBak-Bot is an advanced conversational AI assistant built for linguistic research, machine learning training, and digital archiving purposes. The bot leverages natural language processing (NLP) to understand and respond to user queries in multiple languages, making it an ideal tool for researchers, educators, and developers working on language technologies.

Key Features Multi-language Support: Communicate in various languages Intelligent Conversations: Context-aware responses using NLP Research-Oriented: Designed for linguistic analysis and data collection Digital Archiving: Store and retrieve conversation data efficiently Extensible Architecture: Easy to customize and extend functionality

AI/ML-based tool that predicts the likelihood of diabetes using medical parameters such as glucose, BMI, age, and blood pressure.

AI Chat Assistant built using Flask and Hugging Face Transformers. Provides a responsive web interface with real-time AI-powered chat and session management.

MitraBot – Student & Community Corpus Assistant. Helps students manage timetables, exams, notes, and community contributions (proverbs, recipes, stories).

AI-powered chatbot application with React frontend and Node.js backend for interactive conversations

SwechaBot: A conversational AI chatbot built with Streamlit, Hugging Face Transformers, and Torch

A Telugu Proverbs Chatbot built with Streamlit and SQLite to explore and preserve traditional Telugu Proverbs.

Doc Bot – AI Doctor Assistant is an interactive chatbot that provides doctor-style suggestions based on user-described symptoms. Built with Streamlit for a responsive and user-friendly interface, it integrates with the Dify.ai API to generate natural language responses. This project was developed as part of an internship at Swecha Telangana.

⚠️ Disclaimer: Doc Bot is for educational and informational purposes only and does not provide professional medical advice.

CampusMate Chatbot is a smart virtual assistant designed to support students, faculty, and campus visitors. It helps users with quick access to academic information, campus services, schedules, events, and FAQs. Built with AI and integrated into a user-friendly Streamlit interface, CampusMate provides instant, reliable, and personalized responses—making campus life more efficient and engaging.

DietBot is a chatbot that helps users track meals, suggest diet plans, and promote healthy eating habits. It supports personalized recommendations and integrates with nutrition data sources.

An AI Chat Assistant project built in Python as part of Summer of AI 2025 Internship. Uses Machine Learning (Naive Bayes with Bag of Words) for intent classification.

Fitzor Bot is an open-source AI-powered fitness assistant that helps users stay healthy and active. It provides personalized workout recommendations, tracks fitness progress, answers health-related queries, and motivates users to achieve their goals. Built using Python, NLP, and chatbot frameworks, Fitzor Bot is designed for gyms, trainers, and individuals seeking smart health guidance.

The central idea of the Telugu Sahitya Sahakaari project is to create a digital, community-powered archive for Telugu literature. The project's goal is to preserve literary heritage by allowing users to upload documents, images, and text. The users are also enabled to interact with an AI chatbot and explore, research, and learn about Telugu Literature.

MindfulMate is an AI-powered mental health support application built with Streamlit. It offers a supportive chat environment, mood tracking, mental health resources, and leverages Ollama with Gemma:2b for enhanced conversational intelligence.

This project, "Ruchi: Your Culinary Assistant," is a multilingual chatbot that provides cooking advice. Built with Streamlit in Python, it uses different Large Language Models (LLMs) for English and Telugu responses to offer culturally relevant recipes and tips.

ManaBhasha AI – Telugu Q&A Chatbot ManaBhasha AI is a question-answering chatbot built for Telugu speakers. It leverages Retrieval Augmented Generation (RAG) architecture with IndicBERT embeddings to deliver accurate, context-aware answers in Telugu. This project is part of the Summer of AI 2025 Hackathon and aims to contribute to building open-source language models and tools for regional language AI.