ryaaa/waawwaaw

from PIL import Image import requests

from transformers import CLIPProcessor, CLIPModel

model = CLIPModel.from_pretrained("geolocal/StreetCLIP") processor = CLIPProcessor.from_pretrained("geolocal/StreetCLIP")

url = "https://huggingface.co/geolocal/StreetCLIP/resolve/main/sanfrancisco.jpeg" image = Image.open(requests.get(url, stream=True).raw)

choices = ["San Jose", "San Diego", "Los Angeles", "Las Vegas", "San Francisco"] inputs = processor(text=choices, images=image, return_tensors="pt", padding=True)

outputs = model(**inputs) logits_per_image = outputs.logits_per_image # this is the image-text similarity score probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities