This is simple chatbot using NLP which is implemented on Flask WebApp.

Tutorial to make a simple NLP chatbot with Intent classification, FastText, Flask, AJAX

# College-Information-Chatbot-System - A Chat-bot made using AIML (Artificial Intelligence Markup Language) and NLP to effectively answers College related queries with an added advantage that it also provides personal info. like grades, etc. with proper user authentication. - It also stores the unanswered questions in a log file for improvement by the admin. - mandatory python packages required: nltk, flask, sqlite - Run the main_with_session.py file

Overview Virtual Assistant is an application program that understands natural language voice commands or text commands and completes the tasks for users. Virtual Assistants features a human interface system, they can understand the language and meaning of what the user is saying and have built in replies. Learn from different instances so that they can have a long term human interaction. It uses artificial intelligence to learn things from different situations. Using AI they can recognize, predict and classify based on analysis. Purpose Virtual Assistant provides various services. It is ready to help wherever you are and can be deployed in your devices. Wider scope and perform users to get answers to their questions and perform tasks using voice or text commands, all in an interactive form. Precise voice and text recognition with the ability to have conversation with the users. In case of Google assistant, they recognize the voice of the user and perform the specific task. Use case Customer support: Rather of customers waiting for a long to solve an issue, the can get instant support from chatbot, Banking Chatbots: Personalized banking with an aim to improve customer satisfaction and engagement. Project support: Can send notifications for various tasks. Reminder to follow up with an action. HR assistants: Can help employees register time off, retrieve company policies, and find answers to repetitive employment questions. Teaching: Can helps teachers to create more detailed learning plans and materials. Being full-blown health assistants: Virtual assistants can do so much more than giving tips, they can often help patients apply simple treatments, remind them to take medicine, and monitor their health. Automating FAQs and administrative tasks: If there's a scenario where the customers have dozens of repetitive questions, virtual assistant is there 24/7 to answer questions from people who may be anxious to get answers. Technical support: The customer has a product technical error, in this case, asks the customer to type the error they encounter, then it generates a dynamic link to search the customer input words in the technical knowledge repositories and guide the customer through his search. Efficient Processes: Make processes more streamlined and transparent by synchronizing between functions, roles, and departments. Booking: A virtual assistant can respond to a consumer through messages, web, SMS or email and update them on the status of their existing reservation, make changes to the reservation, process related payments or refunds, send proactive notifications and provide detailed information on their itinerary. Features a. NLP Text Search : Virtual assistant concentrates on NLP and NLU. Understands the slang that is used in everyday conversation and analyses the sentiments to enhance a better set of communication. b. FAQ voice assistant : FAQ voice assistant is a voice assistant that provides a list of questions and answers relating to a particular subject. c. Conversations voice assistant : Conversations voice assistant is a voice assistant that provides conversational services based on a subject. d. Speech conversations (STT,TTS) : It provides conversational services such as speech to text and text to speech. e. Integration with Enterprise Systems : It provides administrative service to clients. Such as scheduling appointments, making phone calls, making travel arrangements, managing email accounts etc. f. Rich Conversations : Rich conversation is a conversation that can use different features such as images, videos, buttons, forms etc. a) Images:Imagescanbesentorreceivedduringconversations. b) Buttons:Buttonscanprovidedifferentfunctionalitiesasperthefeatureofthebutton. c) Videos:Videoscanbesentorreceivedduringconversations d) Forms: Forms help to give visible shape or configuration of something. Technical Requirement g. HTML5 h. JavaScript i. Python (Flask API, NLP Packages) j. MySQL k. Docker l. Git

AI-powered chatbot built with Python, NLP, and Flask — automate customer support queries with intent recognition and dynamic responses.

This repository hosts the codebase for a Financial Advisor landing page integrated with a chatbot designed to answer frequently asked financial questions. The project demonstrates the integration of front-end web development with the Python Flask framework and a chatbot powered by ML and NLP.

Purpose : Major Project Team Size : 4 Duration : 10 Months [ Oct. 1, 2021 - June 31, 2022 ] Key Skills : Rasa AI , Python , NLP , Flask , HTML , CSS , JavaScript It is a Web-based Chatbot to automate healthcare management with audio assistance. Users can get immediate medication for their symptoms and book appointments via an audio feature. In addition to text assistance, this chatbot has an audio assistance feature. This feature eliminates the restrictions that visually impaired patients face with currently available text-enabled healthcare chatbots. This voice-enabled chatbot was designed and developed using the Rasa interface for the backend, the Web Speech API, and the Talkify API for voice input and output, respectively. Title: ' TaBiB: Chatbot for Healthcare Automation with Audio Assistance using Artificial Intelligence '. The project was presented and was approved at the 6th National Conference of Science and Engineering (NCSEM), 2022. Tools used : Rasa AI, Python, NLP, Flask, Web Speech API, Talkify API, HTML, CSS in Atom Editor.

I used flask for chatbot gui and NLP for algorithm and python for backend program

The Medical Chatbot, built with Flask, integrates NLP libraries like Langchain and Hugging Face Transformers for text processing and embedding generation. Utilizing Pinecone as a vector database, it efficiently stores and retrieves data, offering users an interactive platform for medical inquiries.

This is simple chatbot using NLP which is implemented on Flask WebApp.

Tutorial Create Chatbot using NLP with Flask

Whatsapp-Chatbot-with-OpenAI-and-Twilio is a Python chatbot that uses OpenAI's GPT-3 NLP model to generate WhatsApp message responses. Built with Flask and Twilio API, this demo project showcases how to build a chatbot and can be customized. The code is on GitHub under an open-source license.

This project implements a chatbot for disease prediction and treatment recommendation using machine learning algorithms and NLP, built with Python, Flask, and ChatterBot. It uses Naive Bayes and Decision Tree algorithms to analyze user symptoms, achieving an 85% accuracy rate.

A chill, mental-health–focused AI chatbot built with Flask, Deepseek API, and NLP-powered emotion detection.

🤖 AI Chatbot – A smart bot using 🧠 NLP & 🤖 ML for human-like chats. Customizable 💬, learns from users 📈, and supports multiple platforms 🌍. Built with Python 🐍, TensorFlow ⚡, Flask 🚀, and Docker 🐳.

This project is an Ecommerce Chatbot developed to enhance customer interaction and assist with product recommendations, order queries, and FAQs. The chatbot is built using Python and Flask, integrated with LangChain for natural language processing (NLP), and Astra DB as a vector store for storing and retrieving product-related data.

This Hybrid MERN-Flask architecture project has successfully demonstrated the development of a sophisticated conversational Chatbot using NLP and Deep Learning techniques. The bot is capable of understanding and responding to a wide array of user inputs limited to MENTAL-HEALTH.

AI Chatbot using Flask and NLP

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A healthcare chatbot using Flask and Naive Bayes NLP

Chatbot with RSA encryption and decryption using Flask and NLP

Medical chatbot assistant developed with NLP, deep learning and flask framework for the web ui

An AI-powered fashion recommendation chatbot built with Flask and Qdrant, leveraging LLaMA models for NLP to extract product attributes and deliver smart, personalized search results.

This project is a modern NLP-based chatbot web application developed using Python Flask. The chatbot is designed to interact with users through a visually attractive, colorful, and responsive web interface. It uses Natural Language Processing (NLP) concepts to understand user input and generate meaningful responses in real time.

This new project will focus on building a Chatbot API with Flask and MongoDB, incorporating elements of backend development, basic AI/ML concepts (e.g., rule-based NLP), and database integration.

AI-Based Health Diagnostic Assistant 🔹 Idea: A chatbot that predicts diseases based on symptoms using ML. 🔹 Tech Stack: Python, TensorFlow, NLP, Flask. 🔹 Steps: Train a model on disease-symptom datasets. Create an AI-powered chatbot for user queries. Provide probability-based disease predictions.

A deep-learning based intent classification chatbot built using Python, TensorFlow, and Flask. The project includes NLP preprocessing, a trained model (chatbot_model.h5), an intents dataset, and a clean web interface. Users can chat with an AI assistant that responds using machine-learned intent prediction.

A web app that generates customized meal plans based on user goals (weight loss, maintenance, or gain). Integrated with an AI chatbot for dietary advice and meal suggestions, using Flask and Hugging Face's NLP models.

An intelligent educational chatbot with fine-tuned Llama model (PEFT & QLoRA) achieving 90% accuracy improvement on AI queries. Features dual audio/text input, advanced NLP via Sentence-Transformers, and an intuitive Flask-based web interface that increased user engagement by 40%.

An intelligent medical chatbot that provides accurate, context-aware health information using advanced NLP and retrieval-augmented generation. Built with Flask, Pinecone, and Groq's LLM technology. Key Features: Instant medical Q&A Secure user authentication Chat history tracking Professional-grade health information