Create app.py

app.py

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+!pip install git+https://github.com/openai/CLIP.git
+
+from PIL import Image
+import requests
+
+from transformers import CLIPProcessor, CLIPModel
+
+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