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42b2b3c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | from transformers import AutoProcessor, AutoModel
from PIL import Image
from io import BytesIO
import torch
import requests
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModel.from_pretrained("openai/clip-vit-large-patch14-336").to(device)
processor = AutoProcessor.from_pretrained("openai/clip-vit-large-patch14-336")
def get_image_embedding(image_url):
try:
response = requests.get(image_url, timeout=10)
img = Image.open(BytesIO(response.content)).convert('RGB')
inputs = processor(images=img, return_tensors="pt").to(device)
with torch.no_grad():
emb = model.get_image_features(**inputs)
emb = emb / emb.norm(p=2, dim=-1, keepdim=True)
return emb[0].cpu().numpy().tolist()
except Exception as e:
print(f"Image loading failed: {e}")
return None
def get_text_embedding_clip(text_query):
inputs = processor(text=[text_query], return_tensors="pt", padding=True, truncation=True).to(device)
with torch.no_grad():
emb = model.get_text_features(**inputs)
emb = emb / emb.norm(p=2, dim=-1, keepdim=True)
return emb[0].cpu().numpy().tolist()
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