initial commit

app.py

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+import gradio as gr
+from transformers import CLIPProcessor, CLIPModel
+
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch16")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16")
+
+
+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__":
+    demo = gr.Interface(fn=calculate_score, inputs=["image", "text"], outputs="label")
+    demo.launch()

requirements.txt

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+gradio
+transformers