Spaces:
Running
Running
Commit ·
9cac8ab
1
Parent(s): 2cc2d33
Fix HuggingFace Spaces deployment configuration
Browse files- app.py +3 -3
- config.py +0 -5
- gradio_ui.py +5 -1
- pipeline.py +1 -1
app.py
CHANGED
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@@ -25,7 +25,7 @@ logger.info("Importing UI builder...")
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from gradio_ui import build_ui
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logger.info("Importing configuration...")
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from config import WEIGHTS_PATH, OUTPUT_DIR
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def main():
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@@ -45,8 +45,8 @@ def main():
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logger.info("UI built successfully!")
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logger.info("Launching application...")
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-
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-
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logger.info("Application started!")
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from gradio_ui import build_ui
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logger.info("Importing configuration...")
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from config import WEIGHTS_PATH, OUTPUT_DIR
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def main():
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logger.info("UI built successfully!")
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logger.info("Launching application...")
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# HuggingFace Spaces automatically handles server configuration
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app.queue().launch()
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logger.info("Application started!")
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config.py
CHANGED
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@@ -14,8 +14,3 @@ DEFAULT_CONF_THRESHOLD = 0.001
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CONTRAST_ENHANCEMENT = 1.2
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SHARPNESS_ENHANCEMENT = 1.5
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SERVER_NAME = "0.0.0.0"
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SERVER_PORT = 7860
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SHARE = True
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SHOW_ERROR = True
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CONTRAST_ENHANCEMENT = 1.2
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SHARPNESS_ENHANCEMENT = 1.5
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gradio_ui.py
CHANGED
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@@ -20,7 +20,11 @@ def build_ui(out_dir: str, model, device: str) -> gr.Blocks:
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css, fullscreen_js = load_assets()
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pipeline = functools.partial(run_pipeline, out_dir, model, device, IMAGE_SIZE)
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with gr.Blocks(
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gr.Markdown("# Multi-View 3D Reconstruction (MV3DR)")
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with gr.Row():
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css, fullscreen_js = load_assets()
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pipeline = functools.partial(run_pipeline, out_dir, model, device, IMAGE_SIZE)
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with gr.Blocks(
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title="Multi-View 3D Reconstruction (MV3DR)",
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css=css,
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theme=gr.themes.Base()
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) as app:
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gr.Markdown("# Multi-View 3D Reconstruction (MV3DR)")
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with gr.Row():
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pipeline.py
CHANGED
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@@ -17,7 +17,7 @@ from config import IMAGE_SIZE, DEFAULT_ITERATIONS
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def run_pipeline(outdir: str, model: torch.nn.Module, device: str, img_size: int,
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filelist: List[str], niter: int, as_pc: bool, refinement: bool,
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clean_depth: bool = True) -> Tuple[Optional[object], Optional[str], List]:
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if not filelist or len(filelist) == 0:
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return None, None, []
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def run_pipeline(outdir: str, model: torch.nn.Module, device: str, img_size: int,
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filelist: List[str], niter: int, as_pc: bool, refinement: bool,
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clean_depth: bool = True) -> Tuple[Optional[object], Optional[str], List]:
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if not filelist or len(filelist) == 0:
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return None, None, []
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