diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..f6b1f326ca4ab7cf0c8798856f8fe0020ff82d58 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
index 381b653ae95c23d05dcf25f1a805cca52a757a15..fde866d029e55202ad1e89593774e21838c223eb 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
 ---
 title: Shic
-emoji: 🐠
+emoji: 📊
 colorFrom: purple
-colorTo: purple
+colorTo: green
 sdk: gradio
 sdk_version: 4.39.0
 app_file: app.py
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..3bbbadc689943da46a2432998fee68df7f16c6fb
--- /dev/null
+++ b/app.py
@@ -0,0 +1,226 @@
+import gradio as gr
+import os
+import numpy as np
+import trimesh as tm
+from src.model import DinoV2
+from src.shape_model import CSE
+from PIL import Image, ImageDraw
+import torch
+from torchvision import transforms
+import matplotlib.pyplot as plt
+import hashlib
+
+def image_hash(image):
+    """Generate a hash for an image."""
+    image_bytes = image.tobytes()
+    hash_function = hashlib.sha256()
+    hash_function.update(image_bytes)
+    return hash_function.hexdigest()
+
+
+#device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+device = torch.device('cpu')
+models = {}
+for class_name in ['bear', 'horse', 'elephant']:
+    print(f'Loading model weights for {class_name}')
+    models[class_name] = {
+        'image_encoder': DinoV2(16),
+        'cse': CSE(class_name=class_name, num_basis=64, device=device)
+    }
+    models[class_name]['image_encoder'].load_state_dict(torch.load(f'./models/weights/{class_name}.pth', map_location=device))
+    models[class_name]['cse'].load_state_dict(torch.load(f'./models/weights/{class_name}_cse.pth', map_location=device))
+    models[class_name]['cse'].functional_basis = torch.load(f'./models/weights/{class_name}_lbo.pth', map_location=device)
+    
+    models[class_name]['image_encoder'] = models[class_name]['image_encoder'].to(device)
+    models[class_name]['cse'] = models[class_name]['cse'].to(device)
+    models[class_name]['cse'].functional_basis = models[class_name]['cse'].functional_basis.to(device)
+    models[class_name]['cse'].weight_matrix = models[class_name]['cse'].weight_matrix.to(device)
+
+    models[class_name]['shape_feats'] = models[class_name]['cse']().to(device)
+
+# Convert PIL image to a format your model expects (e.g., torch.Tensor)
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+cached_features = {'bear': {}, 'horse': {}, 'elephant': {}}
+
+text_description = """
+# Demo for SHIC: Shape-Image Correspondences with no Keypoint Superivision (ECCV 2024)
+Project website: https://www.robots.ox.ac.uk/~vgg/research/shic/
+
+- **Step 1:** First select a class (now it defaults to 'bear') 
+- **Step 2:** Upload an image of an animal from that class (or select one of the provided examples)
+- **Step 3:** Click on the image (somewhere over the object) to see the image-to-shape correspondences. You can keep clicking on the input image to see new correspondences.
+
+Notes: 
+- You can click and drag to rotate the 3D shape
+- Currently the demo supports bears, horses, and elephants. Other classes coming soon!
+- Make sure you have selected the correct class for your image (It works cross-class too though!)
+"""
+
+example_images_dir = './gradio_example_images/'
+example_images_names = [
+    'bear1.png', 'bear2.png', 'bear3.png', 'winnie.png',
+    'horse1.png', 'horse2.png', 'mylittlepony.png', 'ponyta.png',
+    'elephant1.png', 'elephant2.png', 'dumbo.png', 'phanpy.png'
+]
+example_images = [os.path.join(example_images_dir, img) for img in example_images_names]
+sphere_verts_ = torch.load(f'./models/weights/sphere_verts.pth', map_location=device)
+sphere_faces_ = torch.load(f'./models/weights/sphere_faces.pth', map_location=device)
+def center_crop(img):
+    """
+    Center crops an image to the target size of 224x224.
+    """
+    width, height = img.size   # Get dimensions
+    # Calculate the target size for center cropping
+    target_size = min(width, height)
+    
+    # Calculate the coordinates for center cropping
+    left = (width - target_size) // 2
+    top = (height - target_size) // 2
+    right = left + target_size
+    bottom = top + target_size
+    
+    # Perform center cropping
+    cropped_img = img.crop((left, top, right, bottom))
+    
+    return cropped_img
+
+def draw_point_on_image(image, x_, y_):
+    """Draws a red dot on a copy of the image at the specified point."""
+    # Make a copy of the image to avoid altering the original
+    image_copy = image.copy()
+    draw = ImageDraw.Draw(image_copy)
+    x, y = x_, y_  # Adjust these based on the actual structure of `point`
+    dot_radius = image.size[0] // 100
+    # Draw a red dot
+    draw.ellipse([(y-dot_radius, x-dot_radius), (y+dot_radius, x+dot_radius)], fill='red')
+    
+    return image_copy
+
+def rotate_y(vertices, angle_degrees):
+    angle_radians = np.radians(angle_degrees)
+    rotation_matrix = np.array([
+        [np.cos(angle_radians), 0, np.sin(angle_radians)],
+        [0, 1, 0],
+        [-np.sin(angle_radians), 0, np.cos(angle_radians)]
+    ])
+    
+    # Assuming vertices is a numpy array of shape (N, 3)
+    rotated_vertices = np.dot(vertices, rotation_matrix)
+    return rotated_vertices
+
+def make_final_mesh(verts, faces, similarities):
+    vert_argmax = similarities.argmax(dim=1)
+    vertex = verts[vert_argmax]
+    color=[255, 0, 0]
+
+    vertex_colors=similarities.transpose(1,0).cpu().detach().numpy()
+    # to viridis color map
+    vertex_colors = plt.cm.viridis(vertex_colors)[:, 0, :3]
+
+    num_verts_so_far = len(verts)
+
+    
+    # Create a sphere mesh
+
+    # Scale and translate the sphere to the desired location and size
+    scale_dot = 0.015  # radius of the sphere
+    translation = torch.tensor(vertex, device=device).unsqueeze(0)  # desired location
+
+    verts_sphere = sphere_verts_ * scale_dot + translation  # scale and translate vertices
+    faces_sphere = sphere_faces_ + num_verts_so_far  # faces are the same
+
+    verts_rgb_sphere = torch.tensor([color], device=device).expand(verts_sphere.shape[0], -1)[None] / 255  # [1, N, 3]
+
+
+    # verts and all sphere verts
+    # concat np arrays verts + verts_sphere.cpu().numpy() (4936,3) (2562,3)
+    all_verts = np.concatenate([verts, verts_sphere.cpu().numpy()], axis=0)
+    all_faces = np.concatenate([faces, faces_sphere.cpu().numpy()], axis=0)
+
+    all_textures = np.concatenate([vertex_colors, verts_rgb_sphere.cpu().numpy()[0]], axis=0)
+
+    return tm.Trimesh(vertices=all_verts, faces=all_faces, vertex_colors=all_textures)
+
+def process_mesh(image, class_name, x_, y_):
+    x_, y_ = x_, y_
+    h, w = image.size
+
+    x = torch.tensor(x_ * 224 / w)
+    y = torch.tensor(y_ * 224 / h)
+
+    hashed_image = image_hash(image)
+    if hashed_image in cached_features[class_name]:
+        feats = cached_features[class_name][hashed_image]
+    else:
+        
+        image_tensor = transform(image).unsqueeze(0)
+        
+        # Predict texture
+        feats = models[class_name]['image_encoder'](image_tensor.to(device))
+
+        cached_features[class_name][hashed_image] = feats
+
+    # print('feats shape', feats.shape)
+
+    sampled_feats = feats[:, :, x.long(), y.long()]
+    similarities = torch.einsum('ik, lk -> il', sampled_feats, models[class_name]['shape_feats'])
+    # normalize similarities
+    similarities = (similarities - similarities.min()) / (similarities.max() - similarities.min())
+
+    faces =  models[class_name]['cse'].shape['faces'].cpu().numpy().copy()
+    verts = models[class_name]['cse'].shape['verts'].cpu().numpy().copy()
+
+    # rotate the shape 235 
+    verts = rotate_y(verts, 145)
+
+    mesh = make_final_mesh(verts, faces, similarities)
+    # save as obj
+    mesh_path = './mesh.obj'
+    mesh.export(mesh_path)
+    
+    return mesh_path
+
+def update_output(image, class_name, evt: gr.SelectData):
+    if class_name is None:
+        class_name = 'bear'
+    # This function will be triggered when an image is clicked.
+    # evt contains the click event data, including the coordinates.
+    x_, y_ = evt.index[1], evt.index[0]
+    modified_image = draw_point_on_image(image, x_, y_)
+    mesh_path = process_mesh(image, class_name, x_, y_)
+    return modified_image, mesh_path  # Replace with the actual model path
+    
+
+with gr.Blocks() as demo:
+    # choose a class
+    gr.Markdown(text_description)
+
+
+    with gr.Row(variant="panel"):
+        with gr.Column(scale=1):
+            class_name = gr.Dropdown(choices=['bear', 'horse', 'elephant'], 
+                               label="Select a class (defaults to 'bear')")
+            input_img = gr.Image(label="Input", type="pil", width=256)
+            gr.Examples(
+                examples = example_images,
+                inputs = [input_img],
+                cache_examples=False,
+                label='Feel free to use one of our provided examples!',
+                examples_per_page=30
+            )
+        with gr.Column(scale=1):    
+            output_img = gr.Image(label="Selected Point", interactive=False, height=512)
+        with gr.Column(scale=1):
+            output = gr.Model3D(label='Pixel to Vertex Similarities', height=512)
+    
+
+
+    input_img.select(update_output, [input_img, class_name], [output_img, output])
+
+if __name__ == "__main__":
+    demo.launch(share=True)
\ No newline at end of file
diff --git a/gradio_example_images/baloo.png b/gradio_example_images/baloo.png
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diff --git a/models/shapes/elephant.obj b/models/shapes/elephant.obj
new file mode 100644
index 0000000000000000000000000000000000000000..dd025717341d165e13aae4581391b8a248150ca5
--- /dev/null
+++ b/models/shapes/elephant.obj
@@ -0,0 +1,20020 @@
+####
+#
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+#
+####
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+
+# End of File
diff --git a/models/weights/bear.pth b/models/weights/bear.pth
new file mode 100644
index 0000000000000000000000000000000000000000..5ac37e55105c4be6dbc4c4469debf4bb51d919fd
--- /dev/null
+++ b/models/weights/bear.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/models/weights/bear_cse.pth b/models/weights/bear_cse.pth
new file mode 100644
index 0000000000000000000000000000000000000000..3bcdfa3530f0b70976011373d0121f347b0498b2
--- /dev/null
+++ b/models/weights/bear_cse.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/models/weights/bear_lbo.pth b/models/weights/bear_lbo.pth
new file mode 100644
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--- /dev/null
+++ b/models/weights/bear_lbo.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/models/weights/elephant.pth b/models/weights/elephant.pth
new file mode 100644
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--- /dev/null
+++ b/models/weights/elephant.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 121594483
diff --git a/models/weights/elephant_cse.pth b/models/weights/elephant_cse.pth
new file mode 100644
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--- /dev/null
+++ b/models/weights/elephant_cse.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/models/weights/elephant_lbo.pth b/models/weights/elephant_lbo.pth
new file mode 100644
index 0000000000000000000000000000000000000000..9dbe662e29f367313cbf92ec1863215cc899a7d3
--- /dev/null
+++ b/models/weights/elephant_lbo.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/models/weights/horse.pth b/models/weights/horse.pth
new file mode 100644
index 0000000000000000000000000000000000000000..3bf6d3576f7a27bb9be749a4302344f92eb05885
--- /dev/null
+++ b/models/weights/horse.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/models/weights/horse_cse.pth b/models/weights/horse_cse.pth
new file mode 100644
index 0000000000000000000000000000000000000000..fc5e07d3b9189ef426aa2a9d1fb363550e91fecc
--- /dev/null
+++ b/models/weights/horse_cse.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b760d24a8d936b2ba55543475f2c0e739ce03ab8f388f31722a248e398fc4f3d
+size 4907
diff --git a/models/weights/horse_lbo.pth b/models/weights/horse_lbo.pth
new file mode 100644
index 0000000000000000000000000000000000000000..052889c1dc8416d54bc9e7c2a1274b949f51ed79
--- /dev/null
+++ b/models/weights/horse_lbo.pth
@@ -0,0 +1,3 @@
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diff --git a/models/weights/sphere_faces.pth b/models/weights/sphere_faces.pth
new file mode 100644
index 0000000000000000000000000000000000000000..aec9c4d3d0d8ab459f39bb22ddfca02487b88ff4
--- /dev/null
+++ b/models/weights/sphere_faces.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/models/weights/sphere_verts.pth b/models/weights/sphere_verts.pth
new file mode 100644
index 0000000000000000000000000000000000000000..80d13e1dc668be6afd512682bf0e93ef941101d3
--- /dev/null
+++ b/models/weights/sphere_verts.pth
@@ -0,0 +1,3 @@
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diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ca34d1899789ddce600d960ff8cb242dc81ac045
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,10 @@
+numpy==1.24.4
+torch==1.12.0
+torchvision==0.13.0
+einops==0.6.1
+spharapy==1.1.2
+gradio==3.28.0
+Pillow==10.0.0
+matplotlib==3.7.2
+trimesh==3.23.5
+scipy==1.10.1
\ No newline at end of file
diff --git a/src/__init__.py b/src/__init__.py
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diff --git a/src/model.py b/src/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..c637e4c70f69a20423d3bc4f8cfb0616f941bc6a
--- /dev/null
+++ b/src/model.py
@@ -0,0 +1,68 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.models as models
+
+
+class LinearClassifierToken(nn.Module):
+    def __init__(self, in_channels, num_chanel=2, tokenW=32, tokenH=32):
+        super(LinearClassifierToken, self).__init__()
+        self.in_channels=in_channels
+        self.W=tokenW
+        self.H=tokenH
+        self.nc=num_chanel
+        self.conv=torch.nn.Conv2d(in_channels,num_chanel,(1,1))
+    def forward(self,x):
+        return self.conv(x.reshape(-1,self.H,self.W,self.in_channels).permute(0,3,1,2))
+        
+class DinoV2(nn.Module):
+    def __init__(self, num_class=16) -> None:
+        super().__init__()
+        self.dinov2 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14')
+        for param in self.dinov2.parameters():
+            param.requires_grad = False
+        n=512
+        self.classlayer_224 = LinearClassifierToken(in_channels=384,num_chanel=n,tokenW=16,tokenH=16)
+        self.selu = nn.SELU()
+        self.to_224 = nn.Sequential(
+            nn.Conv2d(n,n,kernel_size=5,stride=1,padding=1,bias=False),
+            nn.Upsample(scale_factor=2),
+            nn.Conv2d(n,n//2,kernel_size=3,stride=1,padding=1,bias=False),
+		    nn.BatchNorm2d(n//2),
+			nn.ReLU(inplace=True),
+            nn.Upsample(scale_factor=2),
+            nn.Conv2d(n//2,n//4,kernel_size=3,stride=1,padding=1,bias=False),
+		    nn.BatchNorm2d(n//4),
+			nn.ReLU(inplace=True),
+            nn.Upsample(scale_factor=2),
+            nn.Conv2d(n//4,n//8,kernel_size=3,stride=1,padding=1,bias=False),
+		    nn.BatchNorm2d(n//8),
+			nn.ReLU(inplace=True),
+            nn.Upsample(scale_factor=2),
+            nn.Conv2d(n//8,n//16,kernel_size=3,stride=1,padding=1,bias=False),
+			nn.ReLU(inplace=True)
+        )
+        self.conv2class = nn.Conv2d(n//16,num_class,kernel_size=3,stride=1,padding=1,bias=True)
+
+
+    def forward(self, x):
+        with torch.no_grad():
+            device = next(self.classlayer_224.parameters()).device
+            features = self.dinov2.forward_features(x.to(device))['x_norm_patchtokens']
+        x = self.selu(self.classlayer_224(features))
+        x = self.to_224(x)
+        x = self.conv2class(x)
+        return x
+    
+if __name__ == '__main__':
+    model = DinoV2(16)
+    model = model.to("cuda")
+
+    example_input = torch.rand(1, 3, 224, 224)
+
+    # Run the model
+    output = model(example_input)
+
+    # Print the output shape
+    print(output.shape)
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diff --git a/src/shape_model.py b/src/shape_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbb473a746938276ad81b51a85e7893040707086
--- /dev/null
+++ b/src/shape_model.py
@@ -0,0 +1,35 @@
+import torch
+import torch.nn as nn
+from src.shape_utils import load_shape_with_lbo
+
+
+
+class CSE(nn.Module):
+    def __init__(self, class_name, num_basis=64, skip_first=True, dim=16, num_vert=None, barebones=False, device=torch.device('cuda'), rand_init=False):
+        super(CSE, self).__init__()
+        
+        self.shape = load_shape_with_lbo(class_name, num_basis, skip_first)
+
+        if barebones:
+            return
+        self.functional_basis=None
+        
+        # Create a parameter tensor for the D x Q matrix, initialized randomly
+        if not rand_init:
+            self.weight_matrix = nn.Parameter(torch.zeros(num_basis, dim, requires_grad=True))
+        else:
+            self.weight_matrix = nn.Parameter(torch.randn(num_basis, dim, requires_grad=True))
+
+        self.to(device)
+        self.nns = None
+        self.num_vert = num_vert
+    
+    def forward(self):
+        output = torch.matmul(self.functional_basis, self.weight_matrix)
+
+        if self.num_vert is not None:
+            output_tmp = torch.zeros((self.num_vert, output.shape[1])).to(output.device)
+            output_tmp[:output.shape[0], :] = output
+            output = output_tmp
+        return output
+    
diff --git a/src/shape_utils.py b/src/shape_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9db5d2ded6711ebd9cf4219b6394ffe09e000ba
--- /dev/null
+++ b/src/shape_utils.py
@@ -0,0 +1,60 @@
+import os
+import pickle
+import numpy as np
+import sys
+import torch
+import trimesh
+from spharapy import trimesh as tm
+from spharapy import spharabasis as sb
+
+
+CLASS_TO_OBJ = {
+    'bear': 'bear.obj',
+    'cow': 'cow.obj',
+    'elephant': 'elephant.obj',
+    'giraffe': 'giraffe.obj',
+    'horse': 'horse.obj',
+    'sheep': 'sheep.obj',
+    'zebra': 'zebra.obj',
+}
+
+def load_obj_densepose(class_name):
+    root = 'models/shapes'
+    obj_path = os.path.join(root, CLASS_TO_OBJ[class_name])
+    mesh = trimesh.load_mesh(obj_path)
+
+    # Access vertices and faces
+    verts = mesh.vertices
+    faces = mesh.faces
+    return torch.tensor(verts), torch.tensor(faces)
+
+def center_verts(verts):
+    # find center such that it is at max-min/2
+    center = (verts.max(0)[0] + verts.min(0)[0]) / 2
+    verts = verts - center
+    return verts
+
+def normalize_verts(verts):
+    # find scale such that max-min = 1
+    scale = (verts.max(0)[0] - verts.min(0)[0]).max()
+    verts = verts / scale
+    return verts
+
+
+def load_shape_with_lbo(class_name='cat', topk=64, skip_first=True): 
+
+    obj_raw = load_obj_densepose(class_name)
+
+    verts_raw = obj_raw[0]
+    verts = center_verts(verts_raw)
+    verts = normalize_verts(verts)
+    scale = (verts.max(0)[0] - verts.min(0)[0]).max()
+
+    faces = obj_raw[1]
+
+    trimesh = tm.TriMesh(faces, verts)
+
+    eigenfunctions=None
+    eigenvalues=None
+
+    return {'eigenfunctions': eigenfunctions, 'eigenvalues': eigenvalues, 'faces': obj_raw[1], 'verts': verts, 'trimesh': trimesh, 'scale': scale}
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