Update app.py

app.py

@@ -1,18 +1,15 @@
-import gradio as gr
-import spaces
-from lama_cleaner.server import main
 from typing import List
 from pydantic import BaseModel
-import torch
+from lama_cleaner.server import main
 
 class FakeArgs(BaseModel):
     host: str = "0.0.0.0"
     port: int = 7860
     model: str = 'lama'
     hf_access_token: str = ""
-    sd_enable_xformers: bool = True
+    sd_enable_xformers: bool = False
     sd_disable_nsfw: bool = False
-    sd_cpu_textencoder: bool = False
+    sd_cpu_textencoder: bool = True
     sd_controlnet: bool = False
     sd_controlnet_method: str = "control_v11p_sd15_canny"
     sd_local_model_path: str = ""
@@ -20,14 +17,14 @@ class FakeArgs(BaseModel):
     local_files_only: bool = False
     cpu_offload: bool = False
     device: str = "cuda"
-    gui: bool = True
+    gui: bool = False
     gui_size: List[int] = [1000, 1000]
     input: str = ''
     disable_model_switch: bool = True
     debug: bool = False
     no_half: bool = False
     disable_nsfw: bool = False
-    enable_xformers: bool = True
+    enable_xformers: bool = False
     enable_interactive_seg: bool = True
     interactive_seg_model: str = "vit_b"
     interactive_seg_device: str = "cuda"
@@ -42,40 +39,5 @@ class FakeArgs(BaseModel):
     model_dir: str = None
     output_dir: str = None
 
-@spaces.GPU
-def process_image(image, mask):
-    # Implement the actual image processing logic here
-    # using Lama Cleaner functionality
-    # For now, we'll just return the input image
-    return image
-
-def create_gradio_interface():
-    interface = gr.Interface(
-        fn=process_image,
-        inputs=[
-            gr.Image(type="numpy", label="Input Image"),
-            gr.Image(type="numpy", label="Mask")  # Removed 'source' and 'tool' parameters
-        ],
-        outputs=gr.Image(type="numpy", label="Result"),
-        title="Lama Cleaner with ZeroGPU",
-        description="Upload an image, upload or draw a mask for inpainting, then click Submit."
-    )
-    return interface
-
-@spaces.GPU
-def run_lama_cleaner():
-    args = FakeArgs()
-    main(args)
-
 if __name__ == "__main__":
-    if torch.cuda.is_available():
-        print("CUDA is available. Using GPU.")
-    else:
-        print("CUDA is not available. Falling back to CPU.")
-        FakeArgs.device = "cpu"
-        FakeArgs.interactive_seg_device = "cpu"
-        FakeArgs.gfpgan_device = "cpu"
-    
-    interface = create_gradio_interface()
-    interface.launch()
-    run_lama_cleaner()
+    main(FakeArgs())