Update app.py

app.py

@@ -0,0 +1,85 @@
+import gradio as gr
+import torch
+from diffusers import StableDiffusionImg2ImgPipeline
+from PIL import Image
+
+MODEL_ID = "runwayml/stable-diffusion-v1-5"
+
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float16
+).to("cuda")
+
+pipe.safety_checker = None  # optional, speeds things up
+
+def redesign_room(
+    image,
+    style,
+    colors,
+    lighting,
+    strength
+):
+    prompt = f"""
+Redesign the interior of this room while keeping the same layout,
+walls, windows, doors, and camera angle.
+
+Interior style: {style}
+Color palette: {colors}
+Lighting: {lighting}
+
+Photorealistic interior design, realistic shadows,
+interior photography, high detail.
+"""
+
+    image = image.convert("RGB").resize((512, 512))
+
+    result = pipe(
+        prompt=prompt,
+        image=image,
+        strength=strength,
+        guidance_scale=7.5,
+        num_inference_steps=30
+    ).images[0]
+
+    return result
+
+with gr.Blocks(title="Room Redesign AI") as demo:
+    gr.Markdown("## 🏠 AI Room Redesign (Image-to-Image)")
+
+    with gr.Row():
+        image_input = gr.Image(label="Upload Room Image", type="pil")
+
+    with gr.Row():
+        style = gr.Dropdown(
+            ["Modern", "Luxury", "Scandinavian", "Minimal", "Japanese"],
+            value="Modern",
+            label="Interior Style"
+        )
+        colors = gr.Textbox(
+            value="white, beige, wood",
+            label="Color Palette"
+        )
+        lighting = gr.Textbox(
+            value="warm ambient lighting",
+            label="Lighting"
+        )
+
+    strength = gr.Slider(
+        minimum=0.2,
+        maximum=0.6,
+        value=0.35,
+        step=0.05,
+        label="Redesign Strength (lower = preserve structure)"
+    )
+
+    output = gr.Image(label="Redesigned Room")
+
+    btn = gr.Button("Redesign Room")
+
+    btn.click(
+        redesign_room,
+        inputs=[image_input, style, colors, lighting, strength],
+        outputs=output
+    )
+
+demo.launch()