feat: refactor GUI to multi-page Streamlit app with 9 tool pages
Convert single-page deduplicator into a multi-page suite. Home page shows tool card grid. Deduplicator extracted to its own page (fully working). 8 stub pages added for Text Cleaner, Format Standardizer, Missing Values, Column Mapper, Outlier Detector, Multi-File Merger, Validator & Reporter, and Pipeline Runner — each with functional file upload and coming-soon UI. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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95
src/gui/pages/9_Pipeline_Runner.py
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95
src/gui/pages/9_Pipeline_Runner.py
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"""DataTools Pipeline Runner — 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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# ---------------------------------------------------------------------------
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# Header
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# ---------------------------------------------------------------------------
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st.title("⚙️ Pipeline Runner")
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st.caption("Chain tools in sequence and pass output between steps automatically.")
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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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- Select tools to run in sequence
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- Recommended order: Text Cleaner → Format Standardizer → Missing Values → Deduplicator → Validator
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- Each step's output feeds into the next step's input
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- Per-step configuration overrides
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- Progress tracking across all steps
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- Final combined report
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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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"Upload CSV or Excel file",
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type=["csv", "tsv", "xlsx", "xls"],
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help="Upload a file to preview. Processing is not yet available.",
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key="pipeline_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), use_container_width=True)
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except Exception as e:
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st.error(f"Failed to read file: {e}")
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# ---------------------------------------------------------------------------
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# Pipeline steps (checklist)
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# ---------------------------------------------------------------------------
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st.subheader("Pipeline Steps")
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st.caption("Select tools to include in the pipeline (recommended order):")
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st.checkbox("1. Text Cleaner", value=True, disabled=True)
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st.checkbox("2. Format Standardizer", value=True, disabled=True)
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st.checkbox("3. Missing Value Handler", value=True, disabled=True)
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st.checkbox("4. Column Mapper", value=False, disabled=True)
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st.checkbox("5. Outlier Detector", value=False, disabled=True)
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st.checkbox("6. Deduplicator", value=True, disabled=True)
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st.checkbox("7. Multi-File Merger", value=False, disabled=True)
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st.checkbox("8. Validator & Reporter", value=True, disabled=True)
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st.subheader("Pipeline Configuration")
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st.selectbox("On error", ["Stop pipeline", "Skip step and continue", "Prompt for decision"], disabled=True)
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st.checkbox("Generate combined report at end", value=True, disabled=True)
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st.divider()
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st.button("Run Pipeline", type="primary", use_container_width=True, disabled=True)
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# ---------------------------------------------------------------------------
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# Footer
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# ---------------------------------------------------------------------------
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st.divider()
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st.caption(
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"Runs locally. Your data never leaves this computer. "
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"| DataTools v3.0"
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)
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