Create app.py

app.py

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+import os
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image
+import torch.nn.functional as F
+from torchvision import transforms
+import gradio as gr
+from diffusers import StableDiffusionPipeline
+
+# Define Loss Functions (same as your original code)
+
+# Setup Stable Diffusion Pipeline
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(device)
+
+# Image transform to tensor
+transform = transforms.ToTensor()
+
+# Create a function to generate images
+def generate_images(prompt, seed=42):
+    # Set generator with a fixed seed
+    generator = torch.Generator(device).manual_seed(seed)
+    
+    # Generate image
+    output_image = pipe(prompt, generator=generator).images[0]
+    
+    # Convert to tensor
+    image_tensor = transform(output_image).to(device)
+
+    # Compute losses (similar to original code)
+    losses = {
+        "edge": edge_loss,
+        "texture": texture_loss,
+        "entropy": entropy_loss,
+        "symmetry": symmetry_loss,
+        "contrast": contrast_loss
+    }
+
+    # Save and return the image
+    result_image_path = f"generated_images/seed_{seed}/original.png"
+    output_image.save(result_image_path)
+    
+    # Return the image for display
+    return output_image
+
+# Define Gradio Interface
+interface = gr.Interface(
+    fn=generate_images,  # Function to call when user interacts with the UI
+    inputs=[
+        gr.Textbox(label="Prompt", value="A futuristic city skyline at sunset"),
+        gr.Slider(minimum=0, maximum=1000, step=1, label="Seed", value=42)
+    ],
+    outputs=gr.Image(type="pil"),
+    live=True,
+    title="Futuristic City Image Generator",
+    description="Generate images of futuristic cities with various customizable settings."
+)
+
+# Launch the interface
+interface.launch()