| 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() |