Delete app.py

app.py

@@ -1,100 +0,0 @@
-import streamlit as st
-from PIL import Image
-import torch
-from torchvision import transforms
-import os
-import torchvision.transforms.functional as TF
-
-
-st.set_page_config(page_title="Ghibli Style Converter", layout="centered")
-
-@st.cache_resource
-import torch.nn as nn
-
-class ConvLayer(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, stride):
-        super(ConvLayer, self).__init__()
-        reflection_padding = kernel_size // 2
-        self.layer = nn.Sequential(
-            nn.ReflectionPad2d(reflection_padding),
-            nn.Conv2d(in_channels, out_channels, kernel_size, stride),
-            nn.InstanceNorm2d(out_channels, affine=True),
-            nn.ReLU()
-        )
-
-    def forward(self, x):
-        return self.layer(x)
-
-class ResidualBlock(nn.Module):
-    def __init__(self, channels):
-        super(ResidualBlock, self).__init__()
-        self.block = nn.Sequential(
-            ConvLayer(channels, channels, 3, 1),
-            ConvLayer(channels, channels, 3, 1)
-        )
-
-    def forward(self, x):
-        return x + self.block(x)
-
-class Generator(nn.Module):
-    def __init__(self):
-        super(Generator, self).__init__()
-        self.encoder = nn.Sequential(
-            ConvLayer(3, 32, 7, 1),
-            ConvLayer(32, 64, 3, 2),
-            ConvLayer(64, 128, 3, 2),
-        )
-        self.res_blocks = nn.Sequential(*[ResidualBlock(128) for _ in range(5)])
-        self.decoder = nn.Sequential(
-            nn.Upsample(scale_factor=2),
-            ConvLayer(128, 64, 3, 1),
-            nn.Upsample(scale_factor=2),
-            ConvLayer(64, 32, 3, 1),
-            nn.ReflectionPad2d(3),
-            nn.Conv2d(32, 3, 7, 1),
-            nn.Tanh()
-        )
-
-    def forward(self, x):
-        x = self.encoder(x)
-        x = self.res_blocks(x)
-        x = self.decoder(x)
-        return x
-
-def load_model():
-    model = Generator()
-    model.load_state_dict(torch.load("model/miyazaki_hayao.pth", map_location="cpu"))
-    model.eval()
-    return model
-
-def preprocess(image):
-    image = image.convert("RGB")
-    transform = transforms.Compose([
-        transforms.Resize((256, 256)),
-        transforms.ToTensor()
-    ])
-    return transform(image).unsqueeze(0)
-
-def postprocess(tensor):
-    tensor = tensor.squeeze().detach().cpu()
-    image = TF.to_pil_image(tensor.clamp(0, 1))
-    return image
-
-st.title("🎨 Ghibli Style Image Converter")
-st.markdown("Upload your image to see it transformed into a Studio Ghibli-style artwork using AnimeGAN2!")
-
-uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
-
-if uploaded_file:
-    image = Image.open(uploaded_file)
-    st.image(image, caption="Original Image", use_column_width=True)
-
-    with st.spinner("Converting to Ghibli Style..."):
-        model = load_model()
-        input_tensor = preprocess(image)
-        with torch.no_grad():
-            output_tensor = model(input_tensor)
-        output_image = postprocess(output_tensor)
-
-    st.image(output_image, caption="Ghibli Style Image", use_column_width=True)
-    st.success("Conversion complete!")