Delete app.py

app.py

@@ -1,465 +0,0 @@
-import gradio as gr
-import torch
-import torch.nn as nn
-from torchvision import transforms
-from PIL import Image
-import numpy as np
-
-# =========================================================
-# DEVICE
-# =========================================================
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# =========================================================
-# MODEL ARCHITECTURE
-# =========================================================
-class UNetDown(nn.Module):
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        normalize=True,
-        dropout=0.0
-    ):
-
-        super().__init__()
-
-        layers = [
-            nn.Conv2d(
-                in_channels,
-                out_channels,
-                4,
-                2,
-                1,
-                bias=False
-            )
-        ]
-
-        if normalize:
-            layers.append(
-                nn.InstanceNorm2d(out_channels)
-            )
-
-        layers.append(
-            nn.LeakyReLU(0.2)
-        )
-
-        if dropout:
-            layers.append(
-                nn.Dropout(dropout)
-            )
-
-        self.model = nn.Sequential(*layers)
-
-    def forward(self, x):
-
-        return self.model(x)
-
-class UNetUp(nn.Module):
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        dropout=0.0
-    ):
-
-        super().__init__()
-
-        layers = [
-
-            nn.ConvTranspose2d(
-                in_channels,
-                out_channels,
-                4,
-                2,
-                1,
-                bias=False
-            ),
-
-            nn.InstanceNorm2d(out_channels),
-
-            nn.ReLU(inplace=True)
-        ]
-
-        if dropout:
-            layers.append(
-                nn.Dropout(dropout)
-            )
-
-        self.model = nn.Sequential(*layers)
-
-    def forward(self, x, skip_input):
-
-        x = self.model(x)
-
-        x = torch.cat((x, skip_input), 1)
-
-        return x
-
-class Generator(nn.Module):
-
-    def __init__(
-        self,
-        in_channels=3,
-        out_channels=3
-    ):
-
-        super().__init__()
-
-        self.down1 = UNetDown(in_channels, 64, normalize=False)
-
-        self.down2 = UNetDown(64, 128)
-
-        self.down3 = UNetDown(128, 256)
-
-        self.down4 = UNetDown(256, 512, dropout=0.5)
-
-        self.down5 = UNetDown(512, 512, dropout=0.5)
-
-        self.down6 = UNetDown(512, 512, dropout=0.5)
-
-        self.down7 = UNetDown(
-            512,
-            512,
-            normalize=False,
-            dropout=0.5
-        )
-
-        self.up1 = UNetUp(512, 512, dropout=0.5)
-
-        self.up2 = UNetUp(1024, 512, dropout=0.5)
-
-        self.up3 = UNetUp(1024, 512, dropout=0.5)
-
-        self.up4 = UNetUp(1024, 256)
-
-        self.up5 = UNetUp(512, 128)
-
-        self.up6 = UNetUp(256, 64)
-
-        self.final = nn.Sequential(
-
-            nn.ConvTranspose2d(
-                128,
-                out_channels,
-                4,
-                2,
-                1
-            ),
-
-            nn.Tanh()
-        )
-
-    def forward(self, x):
-
-        d1 = self.down1(x)
-
-        d2 = self.down2(d1)
-
-        d3 = self.down3(d2)
-
-        d4 = self.down4(d3)
-
-        d5 = self.down5(d4)
-
-        d6 = self.down6(d5)
-
-        d7 = self.down7(d6)
-
-        u1 = self.up1(d7, d6)
-
-        u2 = self.up2(u1, d5)
-
-        u3 = self.up3(u2, d4)
-
-        u4 = self.up4(u3, d3)
-
-        u5 = self.up5(u4, d2)
-
-        u6 = self.up6(u5, d1)
-
-        return self.final(u6)
-
-# =========================================================
-# LOAD MODEL
-# =========================================================
-generator = Generator().to(device)
-
-generator.load_state_dict(
-    torch.load(
-        "generator_model.pth",
-        map_location=device
-    )
-)
-
-generator.eval()
-
-# =========================================================
-# STYLE TRANSFER FUNCTION
-# =========================================================
-def monet_style_transfer(
-    input_image,
-    style_strength,
-    image_quality
-):
-
-    if input_image is None:
-
-        return None, "⚠️ Please upload an image."
-
-    transform = transforms.Compose([
-
-        transforms.Resize((256, 256)),
-
-        transforms.ToTensor(),
-
-        transforms.Normalize(
-            (0.5, 0.5, 0.5),
-            (0.5, 0.5, 0.5)
-        )
-    ])
-
-    input_image = input_image.convert("RGB")
-
-    img_tensor = transform(input_image).unsqueeze(0).to(device)
-
-    with torch.no_grad():
-
-        styled = generator(img_tensor)
-
-    # =====================================================
-    # STYLE STRENGTH CONTROL
-    # =====================================================
-    styled = styled * (style_strength / 100)
-
-    styled = styled * 0.5 + 0.5
-
-    styled = styled.squeeze(0).cpu().permute(1, 2, 0).numpy()
-
-    styled = (styled * 255).clip(0, 255).astype("uint8")
-
-    output_image = Image.fromarray(styled)
-
-    result_text = f"""
-# 🎨 Monet Style Transformation Complete
-
-### Style Strength: {style_strength}%
-
-### Image Quality: {image_quality}
-
-### AI artistic rendering successfully generated.
-"""
-
-    return output_image, result_text
-
-# =========================================================
-# CUSTOM CSS
-# =========================================================
-custom_css = """
-body {
-    background: #f5f7fb;
-    font-family: 'Segoe UI', sans-serif;
-}
-
-.gradio-container {
-    max-width: 1300px !important;
-    margin: auto;
-}
-
-.hero {
-    background: linear-gradient(135deg,#111827,#7c3aed);
-    padding: 40px;
-    border-radius: 30px;
-    color: white;
-    margin-bottom: 20px;
-}
-
-.hero h1 {
-    font-size: 52px;
-    font-weight: 800;
-    margin-bottom: 10px;
-}
-
-.hero p {
-    font-size: 18px;
-    opacity: 0.92;
-}
-
-.card {
-    background: white;
-    border-radius: 24px;
-    padding: 22px;
-    box-shadow: 0 6px 18px rgba(0,0,0,0.08);
-}
-
-button {
-    height: 60px !important;
-    border-radius: 18px !important;
-    border: none !important;
-    background: linear-gradient(135deg,#7c3aed,#6d28d9) !important;
-    color: white !important;
-    font-size: 20px !important;
-    font-weight: 700 !important;
-}
-
-button:hover {
-    background: linear-gradient(135deg,#6d28d9,#5b21b6) !important;
-}
-
-input, textarea, select {
-    border-radius: 16px !important;
-}
-
-@media (max-width: 768px){
-
-    .hero {
-        padding: 22px;
-    }
-
-    .hero h1 {
-        font-size: 32px;
-    }
-
-    .hero p {
-        font-size: 15px;
-    }
-
-    button {
-        height: 54px !important;
-        font-size: 17px !important;
-    }
-}
-"""
-
-# =========================================================
-# HERO HTML
-# =========================================================
-hero_html = """
-<div class="hero">
-
-    <h1>🎨 AI Monet Style Transfer</h1>
-
-    <p>
-        Transform your photos into Monet-inspired paintings using deep learning.
-        Modern mobile-friendly interface with smart controls and AI-powered rendering.
-    </p>
-
-</div>
-"""
-
-# =========================================================
-# INTERFACE
-# =========================================================
-with gr.Blocks(
-    css=custom_css,
-    theme=gr.themes.Soft(
-        primary_hue="violet",
-        secondary_hue="purple"
-    )
-) as demo:
-
-    gr.HTML(hero_html)
-
-    # =====================================================
-    # TOP INFO CARDS
-    # =====================================================
-    with gr.Row():
-
-        with gr.Column():
-            gr.Markdown("""
-### ⚡ Fast AI Rendering
-Generate artistic images instantly
-""")
-
-        with gr.Column():
-            gr.Markdown("""
-### 📱 Mobile Responsive
-Optimized for all screen sizes
-""")
-
-        with gr.Column():
-            gr.Markdown("""
-### 🧠 Deep Learning
-GAN-based artistic transformation
-""")
-
-    # =====================================================
-    # MAIN AREA
-    # =====================================================
-    with gr.Row():
-
-        # =================================================
-        # LEFT PANEL
-        # =================================================
-        with gr.Column(scale=1):
-
-            input_image = gr.Image(
-                type="pil",
-                label="🖼️ Upload Photo"
-            )
-
-            style_strength = gr.Slider(
-                minimum=10,
-                maximum=100,
-                value=80,
-                step=5,
-                label="🎨 Style Strength"
-            )
-
-            image_quality = gr.Dropdown(
-                choices=[
-                    "Standard",
-                    "High",
-                    "Ultra"
-                ],
-                value="High",
-                label="✨ Image Quality"
-            )
-
-            transform_button = gr.Button(
-                "🚀 Generate Monet Art",
-                variant="primary"
-            )
-
-        # =================================================
-        # RIGHT PANEL
-        # =================================================
-        with gr.Column(scale=1):
-
-            output_image = gr.Image(
-                type="pil",
-                label="🖌️ Monet Styled Image"
-            )
-
-            result_text = gr.Markdown()
-
-    # =====================================================
-    # EXAMPLES
-    # =====================================================
-    gr.Examples(
-        examples=[],
-        inputs=[input_image]
-    )
-
-    # =====================================================
-    # BUTTON ACTION
-    # =====================================================
-    transform_button.click(
-        fn=monet_style_transfer,
-        inputs=[
-            input_image,
-            style_strength,
-            image_quality
-        ],
-        outputs=[
-            output_image,
-            result_text
-        ]
-    )
-
-# =========================================================
-# LAUNCH
-# =========================================================
-demo.launch()