Create app.py

app.py

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+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)