Upload 12 files
Browse files- .claude/settings.local.json +7 -0
- __pycache__/app.cpython-311.pyc +0 -0
- app.py +13 -0
- requirements.txt +0 -1
.claude/settings.local.json
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@@ -0,0 +1,7 @@
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{
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"permissions": {
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"allow": [
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"Bash(python *)"
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]
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}
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}
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__pycache__/app.cpython-311.pyc
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Binary files a/__pycache__/app.cpython-311.pyc and b/__pycache__/app.cpython-311.pyc differ
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app.py
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@@ -8,6 +8,11 @@ for every prediction.
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from pathlib import Path
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import gradio as gr
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import matplotlib
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matplotlib.use("Agg")
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@@ -83,6 +88,14 @@ def shap_figure(sv_ac, sv_sg, pred):
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return fig
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def predict(d0, d200, d300, d450, d600, d900, d1200, d1500, d1800,
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h_ac, h_base, h_subbase, explain):
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d = [d0, d200, d300, d450, d600, d900, d1200, d1500, d1800]
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from pathlib import Path
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try:
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import spaces # ZeroGPU: must be imported before torch
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except ImportError: # local run / CPU Space without the spaces package
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spaces = None
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import gradio as gr
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import matplotlib
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matplotlib.use("Agg")
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return fig
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def _gpu(fn):
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"""ZeroGPU hardware refuses to start without a @spaces.GPU function.
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Inference itself runs on CPU in <1 s (SHAP ~3 s), so the short duration
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just satisfies the check while keeping queue priority high."""
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return spaces.GPU(duration=30)(fn) if spaces is not None else fn
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@_gpu
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def predict(d0, d200, d300, d450, d600, d900, d1200, d1500, d1800,
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h_ac, h_base, h_subbase, explain):
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d = [d0, d200, d300, d450, d600, d900, d1200, d1500, d1800]
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requirements.txt
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--extra-index-url https://download.pytorch.org/whl/cpu
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torch>=2.2
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numpy>=1.24
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shap>=0.45
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torch>=2.2
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numpy>=1.24
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shap>=0.45
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