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| import clip | |
| import gradio as gr | |
| import os | |
| import torch | |
| from torchvision.datasets import CIFAR100 | |
| from transformers import CLIPProcessor, CLIPModel | |
| model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") | |
| processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") | |
| cifar100 = CIFAR100(root=os.path.expanduser("~/.cache"), download=True, train=False) | |
| text_inputs = [] | |
| for c in cifar100.classes: | |
| classes = "a photo of a " + c | |
| print(classes) | |
| text_inputs.append(classes) | |
| print(text_inputs) | |
| test = ["a photo of a dog", "a photo of a cat"] | |
| def send_inputs(img): | |
| inputs = processor(text=test, images=img, return_tensors="pt", padding=True) | |
| outputs = model(**inputs) | |
| logits_per_image = outputs.logits_per_image | |
| probs = logits_per_image.softmax(dim=1) | |
| result = probs.argmax(dim=1) | |
| index = result.item() | |
| print(test[index]) | |
| return test[index] | |
| if __name__ == "__main__": | |
| gr.Interface(fn=send_inputs, inputs=["image"], outputs="text").launch() | |