Update app.py

app.py

@@ -14,3 +14,57 @@ from torchcam.utils import overlay_mask
 from torchvision.transforms.functional import to_pil_image
 from sklearn.metrics.pairwise import cosine_similarity
 
+dinov2_vits14 = torch.hub.load("facebookresearch/dinov2", "dinov2_vits14")
+# Set the device to GPU if available, otherwise use CPU
+device = torch.device("cpu")
+dinov2_vits14.to(device)
+
+# Define the transformations: convert to tensor, resize, and normalize
+transform_image = T.Compose([T.ToTensor(), T.Resize(224), T.Normalize([0.5], [0.5])])
+
+model = torch.load("dress_model.pth")
+model.eval()
+
+with open('saved_dress_morph.pkl', 'rb') as f:
+    loaded_dict = pickle.load(f)
+
+def detect(image):
+    
+    size = max(image.size)
+    new_im = Image.new('RGB', (size, size), color = 0) # Create a squared black image
+    new_im.paste(image)
+
+    
+    with torch.no_grad():
+  
+  # Apply transformations to the image and move it to the appropriate device
+    image_tensor = transform_image(new_im).to(device)
+  
+  # Extract features using the DinoV2 model
+    dino_embedding = dinov2_vits14(image_tensor.unsqueeze(0)).cpu()
+    dino_numpy = dinov2_vits14(image_tensor.unsqueeze(0)).cpu().numpy()
+
+    model.eval()
+
+    with torch.no_grad():
+        outputs = model(dino_embedding)
+        pred_dress_cat = round(torch.argmax(outputs, dim = 1).tolist()[0])
+
+    pred_dress = dress_dict[pred_dress_cat]
+
+    pred_dress_s = f"Predicted Dress Category:  {pred_dress}"
+
+    cosine_sim = cosine_similarity(dino_numpy.reshape(1, -1), mean_features.reshape(1, -1)).item()
+    cosin = round(float(cosin_sim), 2)
+
+    return pred_dress_s, cosin
+
+    demo = gr.Interface(
+    fn=detect,
+    inputs=gr.Image(type="numpy", label="Upload an image"),
+    outputs=[
+             gr.Textbox(label = "Predictions"),
+             gr.Number(label="Typicality Score")]
+    title='Dress Classification')
+    
+