Create app.py

app.py

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+import gradio as gr
+from transformers import AutoProcessor, AutoModel
+
+processor = AutoProcessor.from_pretrained("flax-community/clip-rsicd-v2")
+
+model = AutoModel.from_pretrained("flax-community/clip-rsicd-v2")
+
+
+def calculate_score(image, text):
+    labels = text.split(";")
+    labels = [l.strip() for l in labels]
+    labels = list(filter(None, labels))
+    if len(labels) == 0:
+        return dict()
+    inputs = processor(text=labels, images=image, return_tensors="pt", padding=True)
+    outputs = model(**inputs)
+    logits_per_image = outputs.logits_per_image.detach().numpy()
+
+    results_dict = {
+        label: score / 100.0 for label, score in zip(labels, logits_per_image[0])
+    }
+    return results_dict
+
+
+if __name__ == "__main__":
+    cat_example = [
+        "cat.jpg",
+        "a cat stuck in a door; a cat in the air; a cat sitting; a cat standing; a cat is entering the matrix; a cat is entering the void",
+    ]
+
+    demo = gr.Interface(
+        fn=calculate_score,
+        inputs=["image", "text"],
+        outputs="label",
+        examples=[cat_example],
+        allow_flagging="never",
+        description="# CLIP Score",
+        article="Calculate the [CLIP](https://openai.com/blog/clip/) score of a given image and text",
+        cache_examples=True,
+    )
+
+    demo.launch()