import os
os.environ["HF_HOME"] = "/data/huggingface"
os.environ["TRANSFORMERS_CACHE"] = "/data/huggingface"
os.makedirs("/data/huggingface/hub", exist_ok=True)

import torch
from diffusers import StableDiffusionImg2ImgPipeline
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from PIL import Image


# --- Place any download or path setup here --- old 
# MODEL_ID ="runwayml/stable-diffusion-v1-5"  # Can swap for custom path if using IP-Adapter
# ADAPTER_PATH = "/workspace/.cache/huggingface/ip_adapter/ip-adapter_sd15.bin"
# ADAPTER_DIR = "/workspace/.cache/huggingface/ip_adapter"
# DEVICE = "cpu"
# MODEL_CACHE = "/workspace/.cache/huggingface"

# ---- SETTINGS ----
MODEL_ID = "runwayml/stable-diffusion-v1-5"
IPADAPTER_REPO = "h94/IP-Adapter"
IPADAPTER_WEIGHT_NAME = "ip-adapter_sd15.bin"
DEVICE = "cpu"  # Change to "cuda" if you have GPU
CACHE_DIR = os.environ.get("HF_HOME", "/data/huggingface")

# (Optional) Download IP-Adapter weights and patch pipeline if desired

# Load the model ONCE at startup, not per request!
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.float32,
    cache_dir=CACHE_DIR,
    # safety_checker=None,  # Disable for demo/testing; enable in prod
).to(DEVICE)

pipe.load_ip_adapter(
    pretrained_model_name_or_path_or_dict=IPADAPTER_REPO,
    subfolder="models",
    weight_name=IPADAPTER_WEIGHT_NAME
)

# Load vision encoder and processor for IP-Adapter embedding
vision_encoder = CLIPVisionModelWithProjection.from_pretrained(
    f"{IPADAPTER_REPO}/clip_vision_model",
    cache_dir=CACHE_DIR,
)
image_processor = CLIPImageProcessor.from_pretrained(
    f"{IPADAPTER_REPO}/clip_vision_model",
    cache_dir=CACHE_DIR,
)

def generate_sticker(input_image: Image.Image, prompt: str):
    """
    Given a user image and a prompt, generates a sticker/emoji-style portrait.
    """
    # Load the model (download if not present)
    # pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
    #     MODEL_ID,
    #     torch_dtype=torch.float32,
    #     cache_dir=MODEL_CACHE,
    #     safety_checker=None,  # Disable for demo/testing
    # ).to(DEVICE)

    # Preprocess the image (resize, etc)
    face_img = input_image.convert("RGB").resize((224, 224))
    inputs = image_processor(images=face_img, return_tensors="pt").to(DEVICE)
    with torch.no_grad():
        image_embeds = vision_encoder(**inputs).image_embeds

    # 2. Prepare image for SD pipeline
    init_image = input_image.convert("RGB").resize((512, 512))

    # Run inference (low strength for identity preservation)
    result = pipe(
        prompt=prompt,
        image=init_image,
        image_embeds=image_embeds,
        strength=0.65,
        guidance_scale=7.5,
        num_inference_steps=30
    )
    # Return the generated image (as PIL)
    return result.images[0]