| import gradio as gr | |
| from transformers import pipeline | |
| from PIL import Image | |
| # Load model | |
| captioner = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning") | |
| # Inference function | |
| def generate_caption(image): | |
| result = captioner(image) | |
| return result[0]["generated_text"] | |
| # Gradio UI | |
| iface = gr.Interface( | |
| fn=generate_caption, | |
| inputs=gr.Image(type="pil"), | |
| outputs="text", | |
| title="🖼️ Image Caption Generator", | |
| description="Upload an image and the model will describe it in a sentence.", | |
| ) | |
| iface.launch() |