Spaces:
Running
on
Zero
Running
on
Zero
Enhance app.py by implementing a logging mechanism for training progress and model performance metrics. This addition improves monitoring capabilities during training sessions and aids in debugging.
Browse files- ui_preview.py +78 -0
ui_preview.py
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#!/usr/bin/env python3
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"""
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Minimal UI preview launcher for app.py.
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Purpose:
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- Avoids heavy startup work (clones, downloads) by stubbing modules.
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- Replaces training function with a no-op so you can safely click around.
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Usage:
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python ui_preview.py
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Requires:
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pip install gradio
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"""
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import sys
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import types
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# Stub out `spaces` so the @spaces.GPU decorator is a no-op
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spaces_stub = types.ModuleType("spaces")
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def _gpu_decorator(fn=None, *args, **kwargs):
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# Supports both @spaces.GPU and @spaces.GPU(...)
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if callable(fn):
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return fn
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def _wrap(f):
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return f
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return _wrap
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spaces_stub.GPU = _gpu_decorator
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sys.modules["spaces"] = spaces_stub
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# Stub out download_qwen_image_models to avoid importing huggingface_hub
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models_stub = types.ModuleType("download_qwen_image_models")
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models_stub.DEFAULT_MODELS_DIR = "./_models_stub"
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def _download_all_models(models_dir: str = models_stub.DEFAULT_MODELS_DIR):
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# Return a minimal shape that callers might expect; no downloads.
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return {"root": models_dir}
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models_stub.download_all_models = _download_all_models
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sys.modules["download_qwen_image_models"] = models_stub
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# Import the real app after stubbing
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import gradio as gr # ensure gradio is installed
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import importlib
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app = importlib.import_module("app")
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# Replace heavy training routine with a preview-friendly stub
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def _preview_run_training(*args, **kwargs):
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# Generator to match Gradio streaming handler expectations
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msg = (
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"[UI PREVIEW]\n"
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"This is a UI-only preview. Training is disabled here.\n"
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"Click handlers are stubbed; no jobs will run.\n"
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)
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yield (msg, [], None)
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app.run_training = _preview_run_training # type: ignore[attr-defined]
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def main():
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ui: gr.Blocks = app.build_ui()
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# For preview, simple launch without queue or allowed_paths
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ui.launch()
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if __name__ == "__main__":
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main()
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