Update app.py

app.py

@@ -1,21 +1,40 @@
 import torch
-from transformers import AutoModel  # ajusta si usas otro framework/modelo
+import os
+import requests
 
-# Define paths to the files
-MODEL_PATH = "/uruguayai/fooocus/juggernautXL_v8Rundiffusion.safetensors"
-INTERPOSER_PATH = "/uruguayai/fooocus/xl-to-v1_interposer-v4.0.safetensors"
-VAE_PATH = "/uruguayai/fooocus/vaeapp_sd15.pt"
-EXPANSION_PATH = "/uruguayai/fooocus/fooocus_expansion.bin"
+# Definir las URLs de los archivos en el dataset de Hugging Face
+MODEL_URL = "https://huggingface.co/datasets/uruguayai/fooocus/resolve/main/juggernautXL_v8Rundiffusion.safetensors"
+INTERPOSER_URL = "https://huggingface.co/datasets/uruguayai/fooocus/resolve/main/xl-to-v1_interposer-v4.0.safetensors"
+VAE_URL = "https://huggingface.co/datasets/uruguayai/fooocus/resolve/main/vaeapp_sd15.pt"
+EXPANSION_URL = "https://huggingface.co/datasets/uruguayai/fooocus/resolve/main/fooocus_expansion.bin"
+
+# Definir las rutas locales donde se guardarán los archivos
+MODEL_PATH = "/home/user/app/juggernautXL_v8Rundiffusion.safetensors"
+INTERPOSER_PATH = "/home/user/app/xl-to-v1_interposer-v4.0.safetensors"
+VAE_PATH = "/home/user/app/vaeapp_sd15.pt"
+EXPANSION_PATH = "/home/user/app/fooocus_expansion.bin"
+
+def download_file(url, path):
+    """Descarga un archivo desde una URL si no está presente en la ruta especificada."""
+    if not os.path.exists(path):
+        response = requests.get(url)
+        with open(path, 'wb') as f:
+            f.write(response.content)
+        print(f"Downloaded {path}")
 
 def load_model():
-    # Example of loading a model; adjust based on your specific setup
-    model = torch.load(MODEL_PATH)
-    # If you have specific loading functions or modules, call them here
+    # Descargar los archivos necesarios
+    download_file(MODEL_URL, MODEL_PATH)
+    download_file(INTERPOSER_URL, INTERPOSER_PATH)
+    download_file(VAE_URL, VAE_PATH)
+    download_file(EXPANSION_URL, EXPANSION_PATH)
+
+    # Cargar el modelo; asegúrate de usar weights_only=True por razones de seguridad
+    model = torch.load(MODEL_PATH, weights_only=True)
     return model
 
 def main():
     model = load_model()
-    # Here you can add your inference logic, or serve using Gradio or FastAPI
     print("Model loaded successfully!")
 
 if __name__ == "__main__":