app.py

import gradio as gr
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from transformers import DPTFeatureExtractor, DPTForDepthEstimation
import torch
from PIL import Image
import numpy as np
import cv2

# Detect GPU or fallback to CPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load ControlNet model (depth conditioning)
controlnet = ControlNetModel.from_pretrained(
    "lllyasviel/sd-controlnet-depth", torch_dtype=torch.float32
).to(device)

# Load Stable Diffusion with ControlNet
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5",
    controlnet=controlnet,
    torch_dtype=torch.float32
).to(device)

# Load depth estimation model
depth_model = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device)
depth_processor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")

def generate(input_image, prompt):
    # Convert image to RGB
    image = input_image.convert("RGB")

    # Prepare depth inputs
    inputs = depth_processor(images=image, return_tensors="pt").to(device)

    with torch.no_grad():
        outputs = depth_model(**inputs)
        depth = outputs.predicted_depth.squeeze().cpu().numpy()

    # Normalize to 0–255 and convert to grayscale PIL
    depth = cv2.normalize(depth, None, 0, 255, norm_type=cv2.NORM_MINMAX)
    depth_image = Image.fromarray(depth.astype(np.uint8))

    # Run image generation
    result = pipe(
        prompt=prompt,
        image=depth_image,
        height=512,   # You can reduce this if slow
        width=512,
        num_inference_steps=10
    ).images[0]

    return result

# Gradio interface
gr.Interface(
    fn=generate,
    inputs=[
        gr.Image(type="pil", label="Upload Room Image"),
        gr.Textbox(label="Enter Interior Style Prompt", placeholder="e.g. modern Japanese living room"),
    ],
    outputs=gr.Image(type="pil", label="Generated Room"),
    title="🛋️ AI Room Redesign (ControlNet + Depth)",
    description="Upload a room image and get a redesigned version based on your style prompt using ControlNet Depth.",
).launch()