A deep learning chatbot created with Python and Flask

In this project artificial intelligence and machine learning techniques are applied innovatively to an ecommerce website introducing a new perspective to online shopping with artificial agents. This project presents an aspect of negotiating and bargaining online and features a bargain-based e-commerce website, where chatbot act as a shopkeeper and can negotiate the price of the product with a customer and a content-based recommendation system that recommends similar products based on product attributes and description rather than on likings and ratings of customer. The chatbot is developed using deep learning algorithms combined with the natural language processing techniques and recommendation system is developed using natural language processing algorithms. This project is implemented using modern tools and technologies Python, Flask and Machine learning libraries Scikit-Learn and Keras.

Learn how to use database with Rasa Chat bot. This Chatbot with Database will store the user details in Database. I have created this Chat Bot Using Rasa NLU and Rasa Core, Database used is SQlite3, and this is a Flask App. For Understanding the basics of Rasa Chatbot , please check this link: https://youtu.be/sofMxJF4CZ4 If you have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer your queries. Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching. Channel: https://www.youtube.com/channel/UCgHDngFV50KmbqF_6-K8XhA Support my channel 🙏 by LIKE ,SHARE & SUBSCRIBE Check the complete Machine Learning Playlist : https://www.youtube.com/playlist?list=PLv8Cp2NvcY8CoxylKNIYBd9ZVQ1SlFWQ3 Check the complete Deep Learning Playlist : https://www.youtube.com/playlist?list=PLv8Cp2NvcY8CaSVfCIyg5mvek8JvaD7tE Subscribe my channel: https://www.youtube.com/channel/UCgHDngFV50KmbqF_6-K8XhA Support my channel 🙏 by LIKE ,SHARE & SUBSCRIBE Contact: aarohisingla1987@gmail.com #chatbot #rasa #rasanlu #rasacore #AI #ArtificialIntelligence #DeepLearning #conersationalchatbot #Chatbotwithdatabase #spacy

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.

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

This AI-powered chatbot enhances customer experience by providing instant responses to queries about menu items, special offers, and more. Built with Python, Flask, PyTorch, and NLTK. It integrates deep learning and natural language processing to understand and respond to user queries.

A deep learning chatbot created with Python and Flask. This chatbot is used as the base code for the Nishauri android application for KeHMIS

This Project Is An Interactive AI Powered Chatbot Built With Flask And TensorFlow. It Uses A Trained Deep Learning Model To Understand User Inputs And Respond Conversationally Through A Simple And Responsive Web Interface.

Developed ‘Kaira’, a web-based chatbot using deep learning (RNN) to answer university queries during COVID-19. It features a Flask backend, intuitive frontend with speech-to-text, and achieved \~98.8% accuracy. This demo enables seamless info access and future integration with college platforms

This project combines AI-powered agricultural intelligence with deep learning–based plant disease detection and real-time weather insights. It integrates a Gemma-based chatbot (Ollama), PyTorch CNN models, and Flask to deliver explainable, farmer-friendly guidance through an interactive web interface.

 Train.ipynb : The notebook where all the training using deep learning (DL) took place. A model was saved for further use. An LSTM CNN hybrid was used for training.  App.py : The python file with flask code to axt as backend of our web application. This file also processes user text and predicts responses based on the saved model.  Index.html : The HTML part of the frontend of our web application.  Style.css : The CSS part of the frontend of our web application.  Intents.json : The datatset used for training the model. The chatbot is created using deep learning model CNN.

Chatbot written in python with a deep learning neuronal network and flask framework

AI-powered conversational chatbot using NLP and Deep Learning with a Flask-based web interface.

A Deep Learning-powered customer support chatbot built with TensorFlow, NLTK, and Flask for automated query resolution.

Plant Disease Detection System using Deep Learning (VGG16) with Flask, chatbot, admin dashboard, and real-time support system.

Deep learning–based conversational chatbot leveraging TF-IDF vectorization and neural networks, integrated with a Flask backend and React frontend.

An intelligent smart farming system built with Flask, integrating machine learning, deep learning, chatbot functionality, and weather APIs to deliver agricultural insights.

In this Python web-based project with source code, we are going to build a chatbot using deep learning and flask techniques. chatbot using Flask, NLTK, Keras, Python, etc.

"An AI-powered chatbot that detects human emotions using NLP and deep learning, and responds empathetically. Built with Python, Flask, and BERT."

My A-level project which was built using Flask and facilitated a chatbot maid that I trained with deep learning

"An AI-powered chatbot that detects human emotions using NLP and deep learning, and responds empathetically. Built with Python, Flask, and BERT."

AI-powered platform with a medical chatbot, OCR for handwritten doc uments, and drug recommendations — using React.js, Flask, and deep learning.

A deep learning chatbot created with Python and Flask, that can tell interesting facts about South Africa. Deployed to production using Kubernetes

AI Chatbot: A Python-based chatbot using NLTK and Deep Learning. It recognizes user intents, integrates APIs like weather and movie databases, and includes a web interface built with Flask and React

This project is a simple web-based chatbot powered by deep learning, designed to interact with users and provide responses based on user inputs. The backend is built with Flask, and the deep learning model is implemented using TensorFlow and Keras.

In this Python web-based project with source code, we are going to build a chatbot using deep learning and flask techniques. The chatbot will be trained on the dataset which contains categories (intents), pattern and responses.

An AI-powered mental health support chatbot built with Flask, Deep Learning, and NLP. It detects user emotions from text, provides supportive responses, coping tips, mood tracking, crisis detection, and dashboard analytics.

🚀 MindEase is a mental health chatbot web app built with Flask and a responsive frontend. It uses deep learning and NLP to understand user emotions and respond with empathy, offering support for stress, anxiety, and general mental well-being.

A conversational AI chatbot web application built using Microsoft's DialoGPT model and Flask framework. This project demonstrates the use of Natural Language Processing (NLP) and Deep Learning to create an interactive chatbot with a modern and responsive web interface. 👨‍💻 Developer: Udaya

An AI-powered chatbot built with Flask and TensorFlow/Keras. It uses a trained deep learning model (chatbot_model.h5) to classify intents from user messages and respond accordingly. The project includes a frontend (HTML, CSS, JS) for a clean chat interface and is deployment-ready with a Procfile and requirements.txt.