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app.py
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import gradio as gr
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import os
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import shutil
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import subprocess
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# === Descarga y configuraci贸n de Roop ===
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if not os.path.exists("roop"):
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subprocess.run(["git", "clone", "https://github.com/s0md3v/roop.git"])
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os.chdir("roop")
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subprocess.run(["pip", "uninstall", "-y", "jax", "jaxlib", "numpy", "tensorflow"])
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subprocess.run(["pip", "install", "numpy==1.23.5", "jax==0.4.13", "jaxlib==0.4.13", "tensorflow==2.12.0"])
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subprocess.run(["pip", "install", "-r", "requirements.txt"])
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os.chdir("..")
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def procesar_lote(img_rostro, imgs_objetivo):
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input_dir = "roop/batch_input"
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output_dir = "roop/batch_output"
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shutil.rmtree(input_dir, ignore_errors=True)
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shutil.rmtree(output_dir, ignore_errors=True)
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os.makedirs(input_dir, exist_ok=True)
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os.makedirs(output_dir, exist_ok=True)
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rostro_path = os.path.join("roop", "RB.jpg")
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img_rostro.save(rostro_path)
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for img in imgs_objetivo:
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img.save(os.path.join(input_dir, img.name))
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comando = [
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"python", "roop/run.py",
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"--source_face", rostro_path,
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"--target_folder", input_dir,
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"--output_folder", output_dir,
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"--frame-processor", "face_swapper"
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]
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subprocess.run(comando)
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zip_path = "roop/resultado.zip"
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shutil.make_archive(zip_path.replace(".zip", ""), 'zip', output_dir)
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return zip_path
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gr.Interface(
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fn=procesar_lote,
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inputs=[
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gr.Image(label="Rostro base (RB.jpg)", type="pil"),
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gr.File(file_types=["image"], label="Im谩genes a procesar", file_count="multiple")
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],
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outputs=gr.File(label="Descargar resultados ZIP"),
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title="Intercambio de rostros por lote con Roop",
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description="Sube una imagen de rostro base y varias im谩genes objetivo. El sistema procesar谩 todas y devolver谩 un ZIP con los resultados.",
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).launch()
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