Upload auto.py

auto.py

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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)