import streamlit as st
import torch
import torch.nn as nn
import torch.optim as optim
from PIL import Image
import numpy as np

# GAN model architecture
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        # TO DO: implement generator architecture

    def forward(self, z):
        # TO DO: implement generator forward pass
        pass

class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        # TO DO: implement discriminator architecture

    def forward(self, x):
        # TO DO: implement discriminator forward pass
        pass

# Floorplan generator model architecture
class FloorplanGenerator(nn.Module):
    def __init__(self):
        super(FloorplanGenerator, self).__init__()
        # TO DO: implement floorplan generator architecture

    def forward(self, img):
        # TO DO: implement floorplan generator forward pass
        pass

# Load pre-trained models
@st.cache
def load_models():
    generator = Generator()
    discriminator = Discriminator()
    floorplan_generator = FloorplanGenerator()
    # TO DO: load pre-trained model weights
    return generator, discriminator, floorplan_generator

# Streamlit app
st.title("GAN Image Generation and Floorplan App")

# Load models
generator, discriminator, floorplan_generator = load_models()

# Image generation
st.header("Image Generation")
z_dim = 100
noise = torch.randn(1, z_dim)
generated_img = generator(noise)
st.image(generated_img, caption="Generated Image")

# Floorplan generation
st.header("Floorplan Generation")
img_file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
if img_file:
    img = Image.open(img_file)
    img = np.array(img) / 255.0
    floorplan = floorplan_generator(torch.tensor(img))
    st.image(floorplan, caption="Generated Floorplan")

# Run the app
if __name__ == "__main__":
    st.write("App is running!")