title	app_file	sdk	sdk_version
generative-ai-showcase	app.py	gradio	3.45.2

All-in-One Generative AI Showcase

Overview

Welcome to the All-in-One Generative AI Showcase! This project demonstrates a variety of generative AI tasks, including Text Summarization, Named Entity Recognition, Image Captioning, and Image Generation. Built with Python and Gradio, it serves as an interactive platform to explore the capabilities of state-of-the-art machine learning models.

Features

• Text Summarization: Condense lengthy articles into short, informative summaries.
• Named Entity Recognition: Identify and categorize entities in a given text.
• Image Captioning: Generate textual descriptions for uploaded images.
• Image Generation: Create images from text prompts using Stable Diffusion techniques.

Motivation

This project was developed as a fun practice endeavor to deepen my expertise in Generative AI and to create a tool that can be useful for both developers and non-developers alike. It also serves as a portfolio piece that highlights my skills in machine learning, natural language processing, and computer vision.

Technologies Used

• Python
• Gradio
• Hugging Face Transformers
• DiffusionPipeline (for Stable Diffusion)

How to Run

1. Clone this repository:

   bash

   git clone https://github.com/YourUsername/all-in-one-generative-ai-showcase.git

2. Navigate into the project directory and install dependencies:

   cd /generative-ai-showcase
   pip install -r requirements.txt

3. Run the application:

   python3 -m app

4. Open your web browser and go to http://localhost:7860 to interact with the application.

Future Improvements

• Adding more generative tasks. Chatbots are next!
• Improving the UI/UX design to make the application more visually appealing and intuitive to use.
• Adding more model options for each task to give users more choices.
• Implementing user authentication for personalized experiences.
• Incorporating real-time updates for live data processing.

Author

Daniel Efting https://www.linkedin.com/in/danielefting/

Acknowledgments

Special thanks to the Hugging Face team for their incredible Transformers library and to the Gradio team for their intuitive UI library for machine learning.

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