| import torch | |
| import gradio as gr | |
| from PIL import Image | |
| import scipy.io.wavfile as wavfile | |
| # Use a pipeline as a high-level helper | |
| from transformers import pipeline | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| caption_image = pipeline("image-to-text", | |
| model="Salesforce/blip-image-captioning-large", device=device) | |
| def caption_my_image(pil_image): | |
| semantics = caption_image(images=pil_image)[0]['generated_text'] | |
| return semantics | |
| demo = gr.Interface(fn=caption_my_image, | |
| inputs=[gr.Image(label="Select Image",type="pil")], | |
| outputs=[gr.Textbox(label="Image Caption")], | |
| title="@GenAILearniverse Project 8: Image Captioning", | |
| description="THIS APPLICATION WILL BE USED TO CAPTION THE IMAGE.") | |
| demo.launch() |