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| import gradio as gr | |
| import os | |
| import torch | |
| from torchvision import transforms | |
| from PIL import Image | |
| 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) | |
| IMG_SIZE = 32 if torch.cuda.is_available() else 32 | |
| COMPOSED_TRANSFORMERS = transforms.Compose([ | |
| transforms.Resize(IMG_SIZE), | |
| transforms.ToTensor(), | |
| ]) | |
| NORMALIZE_TENSOR = transforms.Normalize( | |
| mean=[0.485, 0.456, 0.406], | |
| std=[0.229, 0.224, 0.225] | |
| ) | |
| def np_array_to_tensor_image(img, width=IMG_SIZE, height=IMG_SIZE, device='cpu'): | |
| image = Image.fromarray(img).convert('RGB').resize((width, height)) | |
| image = COMPOSED_TRANSFORMERS(image).unsqueeze(0) | |
| return image.to(device, torch.float) | |
| def normalize_tensor(tensor: torch.tensor) -> torch.tensor: | |
| return NORMALIZE_TENSOR(tensor) | |
| def send_inputs(img): | |
| ##img = np_array_to_tensor_image(img) | |
| ##img = normalize_tensor(img) | |
| inputs = processor(images=img, return_tensors="pt", padding=True) | |
| outputs = model(**inputs) | |
| logits_per_image = outputs.logits_per_image | |
| probs = logits_per_image.softmax(dim=1) | |
| print(probs) | |
| return probs | |
| if __name__ == "__main__": | |
| gr.Interface(fn=send_inputs, inputs=["image"], outputs="text").launch() | |