test app

app.py

@@ -1,10 +1,45 @@
 import gradio as gr
+import os
+import clip
+import torch
+from torchvision import transforms
+from PIL import Image
+from torchvision.datasets import CIFAR100
 
+model, preprocess = clip.load('ViT-B/32', "cpu")
 
-def send_inputs(inputs):
-    print(inputs)
-    return "Hello"
+
+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)
+    result = model(img)
+    return result
 
 
 if __name__ == "__main__":
-    gr.Interface(fn=send_inputs, inputs=["text"], outputs="image").launch()
+    gr.Interface(fn=send_inputs, inputs=["image"], outputs="text").launch()