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import streamlit as st
import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image
from io import BytesIO
import requests
button_style = """
<style>
.center-align {
display: flex;
justify-content: center;
}
</style>
"""
DEVICE = 'cuda'
@st.cache_resource
class ConvAutoencoder(nn.Module):
def __init__(self):
super().__init__()
# encoder
self.conv1 = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=4),
nn.BatchNorm2d(32),
nn.SELU()
)
self.conv2 = nn.Sequential(
nn.Conv2d(32, 8, kernel_size=2),
nn.BatchNorm2d(8),
nn.SELU()
)
self.pool = nn.MaxPool2d(2, 2, return_indices=True, ceil_mode=True) #<<<<<< Bottleneck
#decoder
# Как работает Conv2dTranspose https://github.com/vdumoulin/conv_arithmetic
self.unpool = nn.MaxUnpool2d(2, 2)
self.conv1_t = nn.Sequential(
nn.ConvTranspose2d(8, 32, kernel_size=2),
nn.BatchNorm2d(32),
nn.SELU()
)
self.conv2_t = nn.Sequential(
nn.ConvTranspose2d(32, 1, kernel_size=4),
nn.LazyBatchNorm2d(),
nn.Sigmoid()
)
def encode(self, x):
x = self.conv1(x)
x = self.conv2(x)
x, indicies = self.pool(x) # ⟸ bottleneck
return x, indicies
def decode(self, x, indicies):
x = self.unpool(x, indicies)
x = self.conv1_t(x)
x = self.conv2_t(x)
return x
def forward(self, x):
latent, indicies = self.encode(x)
out = self.decode(latent, indicies)
return out
model = ConvAutoencoder().to(DEVICE)
model.load_state_dict(torch.load('D:\Bootcamp\phase_2\streamlit\\autoend.pt'))
transform = transforms.Compose([
transforms.ToTensor(), # Преобразование изображения в тензор
# Добавьте другие необходимые преобразования, такие как нормализация, если это необходимо
])
model.eval()
image_source = st.radio("Choose the option of uploading the image of tumor:", ("File", "URL"))
if image_source == "File":
uploaded_file = st.file_uploader("Upload the image", type=["jpg", "png", "jpeg"])
if uploaded_file:
image = Image.open(uploaded_file)
else:
url = st.text_input("Enter the URL of image...")
if url:
response = requests.get(url)
image = Image.open(BytesIO(response.content))
st.markdown(button_style, unsafe_allow_html=True)
model.to('cuda')
if 'image' in locals():
st.image(image, caption="Uploaded image", use_column_width=True)
bw_image = image.convert('L')
image_tensor = transform(bw_image).unsqueeze(0)
image_tensor = image_tensor.to('cuda')
with torch.no_grad():
output = model(image_tensor)
output = transforms.ToPILImage()(output[0].cpu())
if st.button("Detect tumor", type="primary"):
st.image(output, caption="Annotated Image", use_column_width=True)
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