Michael
904356f4e8
Adds a contextual Help button on each detail page, right of the title. Clicking it opens a Streamlit popover with a one-shot how-to: when to use, numbered steps, before→after examples, and an optional one-line tip. Designed to be scannable — no paragraph prose. Implementation: - New ``render_tool_header(tool_id)`` helper in components replaces the bare ``st.title(...) + st.caption(...)`` block on each of the 11 tool pages. Title in the wide column, popover in a narrow right column; caption sits on its own line beneath. - Help content is one markdown blob per tool stored in i18n under ``tools.<id>.help_md`` (en + es). Editors can tweak copy without touching Python. - ``help.button_label`` and ``help.missing_body`` keys added to both packs for the popover trigger and the empty-tool fallback. All 11 tool pages now use the same header pattern — including the PDF Extractor and Reconciler which previously had hardcoded title/ caption pairs. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
100 lines
3.1 KiB
Python
100 lines
3.1 KiB
Python
"""DataTools Find Unusual Values — stub page."""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import streamlit as st
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_project_root = Path(__file__).resolve().parent.parent.parent.parent
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if str(_project_root) not in sys.path:
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sys.path.insert(0, str(_project_root))
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from src.gui.components import (
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back_to_home_link,
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render_sticky_footer,
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render_tool_header,
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hide_streamlit_chrome,
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require_feature_or_render_upgrade,
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)
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from src.i18n import t
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from src.license import FeatureFlag
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hide_streamlit_chrome()
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render_sticky_footer()
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back_to_home_link()
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from src.audit import log_page_open
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log_page_open("6_Outlier_Detector")
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require_feature_or_render_upgrade(FeatureFlag.OUTLIER_DETECTOR)
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# ---------------------------------------------------------------------------
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# Header
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# ---------------------------------------------------------------------------
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render_tool_header("06_outlier_detector")
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st.info("This tool is under development.")
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# ---------------------------------------------------------------------------
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# What this tool will do
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# ---------------------------------------------------------------------------
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st.markdown("""
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**Features:**
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- Z-score detection (configurable threshold)
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- IQR (interquartile range) detection
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- MAD (median absolute deviation) detection
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- Domain-rule violations (e.g., age < 0, price > $1M)
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- Visual outlier highlighting in data preview
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- Handling: flag only, remove, cap/winsorize to bounds
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""")
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st.divider()
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# ---------------------------------------------------------------------------
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# File upload (functional)
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# ---------------------------------------------------------------------------
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uploaded = st.file_uploader(
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"Import CSV or Excel file",
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type=["csv", "tsv", "xlsx", "xls"],
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help="Import a file to preview. Processing is not yet available.",
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key="outlier_file_upload",
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)
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if uploaded is not None:
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import pandas as pd
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try:
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if uploaded.name.endswith((".xlsx", ".xls")):
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df = pd.read_excel(uploaded)
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else:
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df = pd.read_csv(uploaded)
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st.subheader(f"Preview: {uploaded.name}")
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st.caption(f"{len(df)} rows, {len(df.columns)} columns")
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st.dataframe(df.head(10), width="stretch")
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except Exception as e:
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from src.core.errors import format_for_user
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st.error(
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f"**Could not read `{uploaded.name}`**\n\n"
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f"```\n{format_for_user(e)}\n```"
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)
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# ---------------------------------------------------------------------------
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# Placeholder options
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# ---------------------------------------------------------------------------
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st.subheader("Detection Method")
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st.selectbox("Method", ["Z-Score", "IQR (Interquartile Range)", "MAD (Median Absolute Deviation)"], disabled=True)
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st.slider("Z-Score threshold", 1.0, 5.0, 3.0, 0.1, disabled=True)
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st.slider("IQR multiplier", 1.0, 3.0, 1.5, 0.1, disabled=True)
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st.subheader("Handling")
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st.selectbox("Action", ["Flag only (add column)", "Remove outlier rows", "Cap / Winsorize to bounds"], disabled=True)
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st.divider()
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st.button("Detect Outliers", type="primary", width="stretch", disabled=True)
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