background_edit.py

import os

# ── before you set the env var ──
hf_home = "/data/.cache/huggingface"
yolo_cfg = "/data/ultralytics"

# create the folders (and any parents) if they don’t already exist
os.makedirs(hf_home, exist_ok=True)
os.makedirs(yolo_cfg, exist_ok=True)

# now point HF and YOLO at them
os.environ["HF_HOME"]         = hf_home
os.environ["YOLO_CONFIG_DIR"] = yolo_cfg

from ultralytics import YOLO
import numpy as np
import torch
from PIL import Image
import cv2
from diffusers import StableDiffusionXLInpaintPipeline
from utils import pil_to_cv2, cv2_to_pil
import gradio as gr  # ✅ Needed for error handling

INPAINT_SIZE = 1024

# Load clothing model
clothing_model = YOLO("deepfashion2_yolov8s-seg.pt")

# ✅ Load models once
yolo = YOLO("yolov8x-seg.pt")


inpaint_pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
    "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
    torch_dtype=torch.float16,
    use_safetensors=True,
    use_auth_token=os.getenv("HF_TOKEN")
).to("cuda")

def run_background_removal_and_inpaint(image_path, prompt, negative_prompt, guidance_scale=10):
    if not image_path or not os.path.isfile(image_path):
        raise gr.Error("No valid image found. Please run Step 1 first.")

    image = Image.open(image_path).convert("RGB")
    img_cv = pil_to_cv2(image)
    results = yolo(img_cv)

    if not results or not results[0].masks or len(results[0].masks.data) == 0:
        raise gr.Error("No subject detected in the image. Please upload a clearer photo.")

    mask = results[0].masks.data[0].cpu().numpy()
    binary = (mask > 0.5).astype(np.uint8)
    background_mask = 1 - binary
    kernel = np.ones((15, 15), np.uint8)
    dilated = cv2.dilate(background_mask, kernel, iterations=1)
    inpaint_mask = (dilated * 255).astype(np.uint8)

    mask_pil = cv2_to_pil(inpaint_mask).resize((INPAINT_SIZE, INPAINT_SIZE)).convert("L")
    img_pil = image.resize((INPAINT_SIZE, INPAINT_SIZE)).convert("RGB")

    result = inpaint_pipe(
        prompt=prompt,
        negative_prompt=negative_prompt or "",
        image=img_pil,
        mask_image=mask_pil,
        guidance_scale=guidance_scale,
        num_inference_steps=40
    ).images[0]

    return result

def run_clothing_inpaint(image, prompt, negative_prompt, guidance):
    try:
        print("[INFO] Step 3: Clothing segmentation and inpainting...", flush=True)

        img_cv = np.array(image.convert("RGB"))[..., ::-1]  # PIL → OpenCV BGR
        h, w = img_cv.shape[:2]

        # Segment clothing
        results = clothing_model(img_cv)
        masks = results[0].masks.data.cpu().numpy()
        if len(masks) == 0:
            raise gr.Error("No clothing detected. Try a different image.")

        mask = masks[0]
        resized_mask = cv2.resize(mask, (w, h), interpolation=cv2.INTER_NEAREST)
        binary_mask = (resized_mask > 0.5).astype(np.uint8) * 255
        mask_pil = Image.fromarray(binary_mask).convert("L").resize((INPAINT_SIZE, INPAINT_SIZE))

        # Resize input image
        resized_image = image.convert("RGB").resize((INPAINT_SIZE, INPAINT_SIZE))

        # Inpaint clothing
        result = inpaint_pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            image=resized_image,
            mask_image=mask_pil,
            guidance_scale=guidance,
            num_inference_steps=50
        ).images[0]

        return result, ""
    
    except gr.Error as e:
        return None, f"🛑 {str(e)}"
    except Exception as e:
        traceback.print_exc()
        return None, f"❌ Unexpected Error: {type(e).__name__}: {str(e)}"