import os
import torch
from PIL import Image
from typing import Union, Tuple
from torchvision import transforms
from transformers import AutoModelForImageSegmentation
import gradio as gr
from loadimg import load_img

# =========================================================================
# CONFIGURACIÓN DE DISPOSITIVO (CPU)
# =========================================================================
DEVICE = "cpu"

print(f"--- Cargando BiRefNet en {DEVICE.upper()} ---")

# Cargamos el modelo directamente del Hub de Hugging Face
birefnet = AutoModelForImageSegmentation.from_pretrained(
    "merve/BiRefNet", 
    trust_remote_code=True, 
    torch_dtype=torch.float32
).to(DEVICE)

birefnet.eval()
print("Modelo cargado correctamente en CPU.")

# Transformaciones necesarias para el modelo
transform_image = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])

# =========================================================================
# FUNCIONES DE PROCESAMIENTO
# =========================================================================

def process(image: Image.Image) -> Image.Image:
    """
    Aplica BiRefNet para remover el fondo de la imagen usando CPU.
    """
    image_size = image.size
    
    # 1. Preparar el tensor para la red
    input_tensor = transform_image(image).unsqueeze(0).to(DEVICE)
    
    # 2. Inferencia (Paso por la red neuronal sin almacenar gradientes)
    with torch.no_grad():
        preds = birefnet(input_tensor)[-1].sigmoid().cpu()
        
    # 3. Crear la máscara Alfa
    mask = preds[0].squeeze()
    mask_pil = transforms.ToPILImage()(mask)
    
    # 4. Ajustar máscara al tamaño original con alta calidad (LANCZOS)
    mask_final = mask_pil.resize(image_size, Image.LANCZOS)
    
    # 5. Aplicar transparencia a la imagen original
    output_image = image.copy()
    output_image.putalpha(mask_final)
    
    return output_image

def fn(image: Union[Image.Image, str]) -> Tuple[Image.Image, Image.Image]:
    """
    Función para las pestañas de Gradio (Subida de Imagen y URL).
    Devuelve la imagen original y la versión procesada para el ImageSlider.
    """
    im = load_img(image, output_type="pil")
    im = im.convert("RGB")
    origin = im.copy()
    processed_image = process(im)
    return (origin, processed_image)

def process_file(f: str) -> str:
    """
    Función para la pestaña de archivos. Guarda y devuelve la ruta del PNG.
    """
    name_path = f.rsplit(".", 1)[0] + ".png"
    im = load_img(f, output_type="pil")
    im = im.convert("RGB")
    transparent = process(im)
    transparent.save(name_path, "PNG")
    return name_path

# =========================================================================
# INTERFAZ GRADIO
# =========================================================================

slider1 = gr.ImageSlider(label="Processed Image", type="pil", format="png")
slider2 = gr.ImageSlider(label="Processed Image from URL", type="pil", format="png")

image_upload = gr.Image(label="Upload an image")
image_file_upload = gr.Image(label="Upload an image", type="filepath")
url_input = gr.Textbox(label="Paste an image URL")
output_file = gr.File(label="Output PNG File")

# Ejemplos por defecto
example_image_path = "butterfly.jpg"
url_example = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg"

# Carga segura de la imagen de ejemplo local para evitar crasheos si no se ha subido aún
try:
    chameleon = load_img(example_image_path, output_type="pil")
    examples_img = [chameleon]
    examples_file = [example_image_path]
except Exception:
    examples_img = None
    examples_file = None

tab1 = gr.Interface(fn, inputs=image_upload, outputs=slider1, examples=examples_img, api_name="image")
tab2 = gr.Interface(fn, inputs=url_input, outputs=slider2, examples=[url_example], api_name="text")
tab3 = gr.Interface(process_file, inputs=image_file_upload, outputs=output_file, examples=examples_file, api_name="png")

demo = gr.TabbedInterface(
    [tab1, tab2, tab3], 
    ["Image Upload", "URL Input", "File Output"], 
    title="Background Removal Tool (CPU Edition)"
)

if __name__ == "__main__":
    demo.launch(show_error=True)