| import gradio as gr | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| from PIL import Image | |
| import torch | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = BlipForConditionalGeneration.from_pretrained("./").to(device) | |
| processor = BlipProcessor.from_pretrained("./") | |
| def generate_caption(image): | |
| inputs = processor(images=image, return_tensors="pt").to(device) | |
| with torch.no_grad(): | |
| generated_ids = model.generate(**inputs) | |
| caption = processor.decode(generated_ids[0], skip_special_tokens=True) | |
| return caption | |
| interface = gr.Interface(fn=generate_caption, inputs="image", outputs="text") | |
| interface.launch() | |