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)