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Running on Zero
Running on Zero
Update app.py
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app.py
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@@ -2091,7 +2091,7 @@ def build_app():
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gr.HTML(f'''<div class="info-note"><strong>HF sample:</strong> <code>{EXAMPLE_DATASET_REPO}</code> 路 target <code>{EXAMPLE_DATASET_TARGET}</code>. Public Space: do not upload confidential, regulated, proprietary, production customer, or PII data.</div>''')
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# Builds the modeling tab for target selection, training, comparison, and pipeline generation
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with gr.Tab("02. ML Modeling", id="model-lab"):
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with gr.Column(elem_classes=["tab-body"]):
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gr.HTML('''<div class="section-head"><div class="section-kicker">Supervised Learning</div><h2>Choose a Target Variable (Feature), Algorithm, and Evaluation Setup</h2><p>Build a preprocessing + model pipeline, evaluate it on a holdout split, compare baselines, and evaluate generated pipelines (scikit-learn pipelines).</p></div>''')
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gr.HTML(f'''<div class="info-note"><strong>HF sample:</strong> <code>{EXAMPLE_DATASET_REPO}</code> 路 target <code>{EXAMPLE_DATASET_TARGET}</code>. Public Space: do not upload confidential, regulated, proprietary, production customer, or PII data.</div>''')
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# Builds the modeling tab for target selection, training, comparison, and pipeline generation
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with gr.Tab("02. ML Modeling (Optional Fallback Models)", id="model-lab"):
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with gr.Column(elem_classes=["tab-body"]):
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gr.HTML('''<div class="section-head"><div class="section-kicker">Supervised Learning</div><h2>Choose a Target Variable (Feature), Algorithm, and Evaluation Setup</h2><p>Build a preprocessing + model pipeline, evaluate it on a holdout split, compare baselines, and evaluate generated pipelines (scikit-learn pipelines).</p></div>''')
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