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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 clip(image, text):
text = text.split(",")
image = preprocess(image).unsqueeze(0).to(device)
text = clip.tokenize(text).to(device)
with torch.no_grad():
image_features = model.encode_image(image)
text_features = model.encode_text(text)
logits_per_image, logits_per_text = model(image, text)
probs = logits_per_image.softmax(dim=-1).cpu().numpy()
return probs
demo = gr.Interface(fn=clip, inputs=["text", "image"], outputs="text")
demo.launch() |