An application that generates descriptive captions for images using a deep learning model. The app leverages Hugging Face’s Salesforce/blip-image-captioning-large model for image-to-text transformation and is deployed on Hugging Face Spaces using Gradio.

• Features
• Installation
• Usage
• Example Code
• Deployment
• Credits

• Image Captioning: Generates captions that describe the contents of an image.
• User-Friendly Interface: Built with Gradio for an easy-to-use web interface.
• Deployed on Hugging Face: Accessible online for quick testing and demonstrations.

To run this application locally, follow these steps:

1. Clone the Repository:

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name

2. Install Dependencies: Ensure you have Python installed, then install the required libraries:

   pip install -r requirements.txt
   

1. Run the application with:

   python app.py
   

1. Here’s a quick look at the core functionality:

   from transformers import pipeline
   from PIL import Image
   import gradio as gr
   
   model = None
   
   if model == None:
       model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
   
   def captioner(image):
       img = Image.fromarray(image)
       result = model(img)[0]['generated_text']
       return result
   
   iface = gr.Interface(
       fn=captioner,
       inputs=gr.Image(),
       outputs='text'
   )
   
   iface.launch()

• The app is deployed on Hugging Face Spaces. You can try it directly by uploading images to receive captions generated by the model.

• Transformers by Hugging Face for the image captioning model.
• Gradio for the web interface.
• Hugging Face for providing an easy deployment platform through Spaces.