This GitHub repository provides a collection of Flask web application examples that demonstrate various machine learning and natural language processing tasks, including object detection, pose detection, a BERT-based chatbot, and speech-to-text/text-to-speech functionality. These applications serve as practical demonstrations of how to integrate these capabilities into web applications using Flask, a popular Python web framework.
Flask is a lightweight and flexible Python web framework that makes it easy to build web applications. This repository showcases how to create web applications that leverage machine learning models for various tasks. The examples provided here are intended to serve as a starting point for developers interested in building web applications with these capabilities.
The Object Detection example demonstrates how to build a web application that can detect objects in uploaded images using a pre-trained deep learning model. Users can upload an image, and the application will identify and annotate objects within the image.
The Pose Detection example shows how to create a web application that can estimate human poses from images or live camera feeds. This application can be used for applications like yoga pose analysis or fitness tracking.
The BERT Chatbot example illustrates how to integrate a BERT-based chatbot into a web application. Users can interact with the chatbot by typing messages, and the chatbot will provide responses based on the context of the conversation.
The Speech-to-Text and Text-to-Speech example demonstrates how to build a web application that can convert spoken language into text and vice versa. Users can record audio or input text, and the application will perform the respective conversion.
To get started with any of the examples, follow the instructions provided in each example's respective directory. You may need to install dependencies, download pre-trained models, and configure the Flask application.
Each example in this repository includes usage instructions specific to that example. Refer to the README files in the respective example directories for details on how to run and use the application.
Contributions to this repository are welcome. If you have improvements, additional examples, or bug fixes to suggest, please open an issue or submit a pull request following the contribution guidelines outlined in the repository.
Thank you for checking out this repository! We hope you find these Flask web application examples helpful in integrating machine learning and NLP capabilities into your projects. If you have any questions or feedback, please don't hesitate to reach out.