move installation to top

app.py

@@ -1,14 +1,5 @@
-import matplotlib.pyplot as plt
-import torch
-import torchvision.transforms as T
-from PIL import Image
-import gradio as gr
-from featup.util import norm, unnorm, pca, remove_axes
-from pytorch_lightning import seed_everything
-import os
-import requests
-import csv
-import spaces
+
+
 
 from setuptools import setup, find_packages
 from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CppExtension
@@ -17,31 +8,6 @@ setup(
     name='featup',
     version='0.1.2',
     packages=find_packages(),
-    install_requires=[
-        'torch',
-        'kornia',
-        'omegaconf',
-        'pytorch-lightning',
-        'torchvision',
-        'tqdm',
-        'torchmetrics',
-        'scikit-learn',
-        'numpy',
-        'matplotlib',
-        'timm==0.4.12',
-    ],
-    author='Mark Hamilton, Stephanie Fu',
-    author_email='markth@mit.edu, fus@berkeley.edu',
-    description='Official code for "FeatUp: A Model-Agnostic Frameworkfor Features at Any Resolution" ICLR 2024',
-    long_description=open('README.md').read(),
-    long_description_content_type='text/markdown',
-    url='https://github.com/mhamilton723/FeatUp',
-    classifiers=[
-        'Programming Language :: Python :: 3',
-        'License :: OSI Approved :: MIT License',
-        'Operating System :: OS Independent',
-    ],
-    python_requires='>=3.6',
     ext_modules=[
         CUDAExtension(
             'adaptive_conv_cuda_impl',
@@ -59,6 +25,18 @@ setup(
     }
 )
 
+import matplotlib.pyplot as plt
+import torch
+import torchvision.transforms as T
+from PIL import Image
+import gradio as gr
+from featup.util import norm, unnorm, pca, remove_axes
+from pytorch_lightning import seed_everything
+import os
+import requests
+import csv
+import spaces
+
 
 def plot_feats(image, lr, hr):
     assert len(image.shape) == len(lr.shape) == len(hr.shape) == 3