| import gradio as gr | |
| from transformers import pipeline | |
| from PIL import Image | |
| # Initialize the pipeline with the image captioning model | |
| caption_pipeline = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large") | |
| def generate_caption(image): | |
| # The image is received as a PIL Image, so no need for conversion | |
| result = caption_pipeline(image) | |
| caption = result[0]["generated_text"] | |
| return caption | |
| # Setup the Gradio interface | |
| interface = gr.Interface(fn=generate_caption, | |
| inputs=gr.components.Image(type="pil", label="Upload an Image"), | |
| outputs=gr.components.Textbox(label="Generated Caption")) | |
| interface.launch() | |