Delete app.py

app.py

@@ -1,50 +0,0 @@
-import torch
-import gradio as gr
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
-from PIL import Image
-
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-
-# Load ControlNet
-controlnet = ControlNetModel.from_pretrained(
-    "Qistinasofea/controlnet-floorplan",
-    torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
-)
-
-# Load Pipeline
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "stable-diffusion-v1-5/stable-diffusion-v1-5",
-    controlnet=controlnet,
-    torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
-    safety_checker=None,
-)
-
-pipe = pipe.to(DEVICE)
-
-def generate(image, prompt):
-    if image is None:
-        return None
-    
-    image = image.resize((512, 512))
-    
-    result = pipe(
-        prompt=prompt,
-        image=image,
-        num_inference_steps=20,  # keep low for CPU
-        controlnet_conditioning_scale=1.0,
-    )
-    
-    return result.images[0]
-
-demo = gr.Interface(
-    fn=generate,
-    inputs=[
-        gr.Image(type="pil", label="Upload Colored Segmentation"),
-        gr.Textbox(label="Describe Your Floorplan", value="A residential floorplan with multiple rooms"),
-    ],
-    outputs=gr.Image(type="pil", label="Generated Floorplan"),
-    title="🏠 ControlNet Floorplan Generator",
-    description="Upload a colored segmentation image and describe your floorplan. Running on CPU (~30s per generation).",
-)
-
-demo.launch()