| import gradio as gr | |
| import torch | |
| import clip | |
| from PIL import Image | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model, preprocess = clip.load("ViT-B/32", device=device) | |
| def process_image_and_text(image, text): | |
| text = text.split(",") | |
| image = Image.fromarray(image) | |
| image = preprocess(image).unsqueeze(0).to(device) | |
| text_tokens = clip.tokenize(text).to(device) | |
| with torch.no_grad(): | |
| image_features = model.encode_image(image) | |
| print(image_features.size()) | |
| text_features = model.encode_text(text_tokens) | |
| logits_per_image, logits_per_text = model(image, text_tokens) | |
| probs = logits_per_image.softmax(dim=-1) | |
| return probs.cpu().numpy()[0] | |
| demo = gr.Interface(fn=process_image_and_text, inputs=['image', 'text'], outputs="text") | |
| demo.launch() |