delete example.py

example.py

@@ -1,23 +0,0 @@
-import requests
-from PIL import Image
-from transformers import CLIPModel, CLIPProcessor
-
-model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
-processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-
-url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-image = Image.open(requests.get(url, stream=True).raw)
-
-inputs = processor(
-    text=["a photo of a cat", "a photo of a dog"],
-    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
-print(probs)