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>
2026-04-29 01:16:12 +00:00
parent 9ec371a85f
commit f2fdc10af7
10 changed files with 1175 additions and 330 deletions
--- a/src/gui/pages/1_Deduplicator.py
+++ b/src/gui/pages/1_Deduplicator.py
@@ -0,0 +1,355 @@
+"""DataTools Deduplicator — full working tool page."""
+
+from __future__ import annotations
+
+import sys
+import tempfile
+from pathlib import Path
+
+import pandas as pd
+import streamlit as st
+
+# Ensure project root is on sys.path so `src.core` imports work
+_project_root = Path(__file__).resolve().parent.parent.parent.parent
+if str(_project_root) not in sys.path:
+    sys.path.insert(0, str(_project_root))
+
+from src.core.dedup import deduplicate, DeduplicationResult
+from src.core.io import read_file, list_sheets, detect_encoding, detect_delimiter
+from src.gui.components import (
+    apply_review_decisions,
+    config_panel,
+    match_group_card,
+    results_summary,
+)
+
+
+# ---------------------------------------------------------------------------
+# Session state defaults
+# ---------------------------------------------------------------------------
+
+_DEFAULTS = {
+    "df": None,
+    "result": None,
+    "review_decisions": {},
+    "config": None,
+    "file_name": "",
+    "sheet_names": [],
+    "detected_delimiter": ",",
+}
+for key, default in _DEFAULTS.items():
+    if key not in st.session_state:
+        st.session_state[key] = default
+
+
+# ---------------------------------------------------------------------------
+# Header
+# ---------------------------------------------------------------------------
+
+st.title("🔍 Deduplicator")
+st.caption("Find and remove duplicate rows in CSV, delimited text, and Excel files.")
+
+
+# ---------------------------------------------------------------------------
+# File upload
+# ---------------------------------------------------------------------------
+
+uploaded = st.file_uploader(
+    "Upload CSV or Excel file",
+    type=["csv", "tsv", "xlsx", "xls"],
+    help="Supports CSV, TSV, and Excel files. Encoding and delimiters are auto-detected.",
+    key="dedup_file_upload",
+)
+
+if uploaded is not None:
+    # Detect if file changed
+    if uploaded.name != st.session_state["file_name"]:
+        st.session_state["file_name"] = uploaded.name
+        st.session_state["result"] = None
+        st.session_state["review_decisions"] = {}
+
+        # Read the file
+        try:
+            suffix = Path(uploaded.name).suffix
+            with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+                tmp.write(uploaded.getvalue())
+                tmp_path = Path(tmp.name)
+
+            # Check for Excel sheets / detect delimiter
+            if suffix.lower() in (".xlsx", ".xls"):
+                st.session_state["sheet_names"] = list_sheets(tmp_path)
+                st.session_state["detected_delimiter"] = ","
+            else:
+                st.session_state["sheet_names"] = []
+                enc = detect_encoding(tmp_path)
+                st.session_state["detected_delimiter"] = detect_delimiter(tmp_path, enc)
+
+            df = read_file(tmp_path)
+            if not isinstance(df, pd.DataFrame):
+                df = pd.concat(list(df), ignore_index=True)
+
+            st.session_state["df"] = df
+            tmp_path.unlink(missing_ok=True)
+
+        except Exception as e:
+            st.error(f"Failed to read file: {e}")
+            st.session_state["df"] = None
+
+    df = st.session_state["df"]
+
+    if df is not None:
+        # Sheet selector for Excel files
+        if st.session_state["sheet_names"] and len(st.session_state["sheet_names"]) > 1:
+            sheet = st.selectbox(
+                "Select sheet",
+                st.session_state["sheet_names"],
+            )
+            if sheet != st.session_state.get("_current_sheet"):
+                st.session_state["_current_sheet"] = sheet
+                suffix = Path(uploaded.name).suffix
+                with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+                    tmp.write(uploaded.getvalue())
+                    tmp_path = Path(tmp.name)
+                df = read_file(tmp_path, sheet_name=sheet)
+                if not isinstance(df, pd.DataFrame):
+                    df = pd.concat(list(df), ignore_index=True)
+                st.session_state["df"] = df
+                st.session_state["result"] = None
+                st.session_state["review_decisions"] = {}
+                tmp_path.unlink(missing_ok=True)
+
+        # Delimiter selector for CSV/TSV files
+        is_csv = Path(uploaded.name).suffix.lower() not in (".xlsx", ".xls")
+        if is_csv:
+            _DELIMITERS = {
+                "Comma (,)": ",",
+                "Tab (\\t)": "\t",
+                "Semicolon (;)": ";",
+                "Pipe (|)": "|",
+                "Other": None,
+            }
+            _DELIM_LABELS = list(_DELIMITERS.keys())
+            _DELIM_VALUES = list(_DELIMITERS.values())
+            detected = st.session_state.get("detected_delimiter", ",")
+            default_idx = _DELIM_VALUES.index(detected) if detected in _DELIM_VALUES else 0
+            chosen_label = st.selectbox(
+                "Delimiter",
+                _DELIM_LABELS,
+                index=default_idx,
+                help="Auto-detected on upload. Change if the preview looks wrong.",
+            )
+            if chosen_label == "Other":
+                custom_delim = st.text_input(
+                    "Enter delimiter character",
+                    max_chars=5,
+                    help="Enter the character(s) used to separate fields.",
+                )
+                chosen_delim = custom_delim if custom_delim else ","
+            else:
+                chosen_delim = _DELIMITERS[chosen_label]
+            if chosen_delim != st.session_state.get("_current_delimiter"):
+                st.session_state["_current_delimiter"] = chosen_delim
+                suffix = Path(uploaded.name).suffix
+                with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+                    tmp.write(uploaded.getvalue())
+                    tmp_path = Path(tmp.name)
+                df = read_file(tmp_path, delimiter=chosen_delim)
+                if not isinstance(df, pd.DataFrame):
+                    df = pd.concat(list(df), ignore_index=True)
+                st.session_state["df"] = df
+                st.session_state["result"] = None
+                st.session_state["review_decisions"] = {}
+                tmp_path.unlink(missing_ok=True)
+
+        # Preview
+        st.subheader(f"Preview: {uploaded.name}")
+        st.caption(f"{len(df)} rows, {len(df.columns)} columns")
+        st.dataframe(df.head(10), use_container_width=True)
+
+        # Advanced options
+        settings = config_panel(df)
+
+        # Apply loaded config if present
+        loaded_cfg = st.session_state.get("loaded_config")
+        if loaded_cfg is not None:
+            settings["strategies"] = loaded_cfg.to_strategies()
+            settings["survivor_rule"] = loaded_cfg.to_survivor_rule()
+            settings["date_column"] = loaded_cfg.date_column
+            settings["merge"] = loaded_cfg.merge
+            del st.session_state["loaded_config"]
+
+        # -------------------------------------------------------------------
+        # Find Duplicates button
+        # -------------------------------------------------------------------
+
+        st.divider()
+
+        if st.button("Find Duplicates", type="primary", use_container_width=True):
+            progress_bar = st.progress(0, text="Comparing rows...")
+
+            def _gui_progress(current: int, total: int) -> None:
+                if total > 0:
+                    pct = min(current / total, 1.0)
+                    progress_bar.progress(pct, text=f"Comparing rows... {current:,}/{total:,}")
+
+            with st.spinner("Running deduplication..."):
+                result = deduplicate(
+                    df,
+                    strategies=settings["strategies"],
+                    survivor_rule=settings["survivor_rule"],
+                    date_column=settings["date_column"],
+                    merge=settings["merge"],
+                    preview=False,
+                    progress_callback=_gui_progress,
+                )
+
+            progress_bar.empty()
+            st.session_state["result"] = result
+            st.session_state["review_decisions"] = {}
+
+        # -------------------------------------------------------------------
+        # Results
+        # -------------------------------------------------------------------
+
+        result: DeduplicationResult | None = st.session_state["result"]
+
+        if result is not None:
+            st.divider()
+            st.subheader("Results")
+
+            # Summary + download buttons
+            results_summary(result, df)
+
+            # Match group review
+            if result.match_groups:
+                st.divider()
+                st.subheader("Match Groups")
+
+                # Batch actions
+                def _accept_all():
+                    for g in result.match_groups:
+                        st.session_state["review_decisions"][g.group_id] = {
+                            "keep_indices": [g.survivor_index],
+                            "overrides": {},
+                        }
+
+                def _reject_all():
+                    for g in result.match_groups:
+                        st.session_state["review_decisions"][g.group_id] = {
+                            "keep_indices": list(g.row_indices),
+                            "overrides": {},
+                        }
+
+                def _clear_all():
+                    st.session_state["review_decisions"] = {}
+                    for k in list(st.session_state):
+                        if k.startswith("editor_"):
+                            del st.session_state[k]
+
+                action_left, action_mid, action_right = st.columns(3)
+                with action_left:
+                    st.button("Accept All", on_click=_accept_all)
+                with action_mid:
+                    st.button("Reject All", on_click=_reject_all)
+                with action_right:
+                    st.button("Clear Decisions", on_click=_clear_all)
+
+                # Individual group cards
+                decisions = st.session_state["review_decisions"]
+                for i, group in enumerate(result.match_groups):
+                    match_group_card(group, df, group_num=i + 1)
+
+                # Show decision summary
+                if decisions:
+                    st.divider()
+                    merged = 0
+                    customized = 0
+                    split = 0
+                    kept_all = 0
+                    for v in decisions.values():
+                        if not isinstance(v, dict):
+                            continue
+                        ki = v.get("keep_indices", [])
+                        gid_for_v = next(
+                            (gid for gid, d in decisions.items() if d is v),
+                            None,
+                        )
+                        group_size = next(
+                            (len(g.row_indices) for g in result.match_groups
+                             if g.group_id == gid_for_v),
+                            0,
+                        )
+                        if len(ki) == group_size:
+                            kept_all += 1
+                        elif len(ki) == 1:
+                            if v.get("overrides"):
+                                customized += 1
+                            else:
+                                merged += 1
+                        else:
+                            split += 1
+
+                    pending = len(result.match_groups) - len(decisions)
+                    parts = []
+                    if merged:
+                        parts.append(f"{merged} merged")
+                    if customized:
+                        parts.append(f"{customized} customized")
+                    if split:
+                        parts.append(f"{split} split")
+                    if kept_all:
+                        parts.append(f"{kept_all} kept all")
+                    parts.append(f"{pending} pending")
+                    st.caption("Decisions: " + ", ".join(parts))
+
+                    # Apply decisions and offer download
+                    if st.button(
+                        "Apply Review Decisions & Download",
+                        type="primary",
+                        use_container_width=True,
+                    ):
+                        reviewed_df, reviewed_removed = apply_review_decisions(
+                            df, result.match_groups, decisions,
+                        )
+
+                        csv_bytes = reviewed_df.to_csv(
+                            index=False
+                        ).encode("utf-8-sig")
+                        st.download_button(
+                            "Download Reviewed & Deduplicated CSV",
+                            data=csv_bytes,
+                            file_name="deduplicated_reviewed.csv",
+                            mime="text/csv",
+                            key="reviewed_download",
+                        )
+                        if not reviewed_removed.empty:
+                            removed_bytes = reviewed_removed.to_csv(
+                                index=False
+                            ).encode("utf-8-sig")
+                            st.download_button(
+                                "Download Reviewed Removed Rows",
+                                data=removed_bytes,
+                                file_name="removed_reviewed.csv",
+                                mime="text/csv",
+                                key="reviewed_removed_download",
+                            )
+
+            # Log entries
+            if result.log_entries:
+                with st.expander("Processing Log"):
+                    st.code("\n".join(result.log_entries))
+
+else:
+    # No file uploaded — show placeholder
+    st.info("Upload a file to get started.")
+
+
+# ---------------------------------------------------------------------------
+# Footer
+# ---------------------------------------------------------------------------
+
+st.divider()
+st.caption(
+    "Runs locally. Your data never leaves this computer. "
+    "| DataTools Deduplicator v3.0"
+)
--- a/src/gui/pages/2_Text_Cleaner.py
+++ b/src/gui/pages/2_Text_Cleaner.py
@@ -0,0 +1,89 @@
+"""DataTools Text Cleaner — stub page."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import streamlit as st
+
+_project_root = Path(__file__).resolve().parent.parent.parent.parent
+if str(_project_root) not in sys.path:
+    sys.path.insert(0, str(_project_root))
+
+# ---------------------------------------------------------------------------
+# Header
+# ---------------------------------------------------------------------------
+
+st.title("✂️ Text Cleaner")
+st.caption("Clean and normalize text content across your data.")
+
+st.info("This tool is under development.")
+
+# ---------------------------------------------------------------------------
+# What this tool will do
+# ---------------------------------------------------------------------------
+
+st.markdown("""
+**Features:**
+- Trim leading/trailing whitespace
+- Collapse multiple spaces into one
+- Unicode normalization (NFC/NFKC)
+- Strip non-printable / control characters
+- Remove BOM (byte order mark)
+- Normalize line endings (CRLF → LF)
+- Case conversion (upper, lower, title, sentence)
+""")
+
+st.divider()
+
+# ---------------------------------------------------------------------------
+# File upload (functional)
+# ---------------------------------------------------------------------------
+
+uploaded = st.file_uploader(
+    "Upload CSV or Excel file",
+    type=["csv", "tsv", "xlsx", "xls"],
+    help="Upload a file to preview. Processing is not yet available.",
+    key="textclean_file_upload",
+)
+
+if uploaded is not None:
+    import pandas as pd
+    try:
+        if uploaded.name.endswith((".xlsx", ".xls")):
+            df = pd.read_excel(uploaded)
+        else:
+            df = pd.read_csv(uploaded)
+        st.subheader(f"Preview: {uploaded.name}")
+        st.caption(f"{len(df)} rows, {len(df.columns)} columns")
+        st.dataframe(df.head(10), use_container_width=True)
+    except Exception as e:
+        st.error(f"Failed to read file: {e}")
+
+# ---------------------------------------------------------------------------
+# Placeholder options
+# ---------------------------------------------------------------------------
+
+st.subheader("Operations")
+
+st.checkbox("Trim whitespace", value=True, disabled=True)
+st.checkbox("Collapse multiple spaces", value=True, disabled=True)
+st.checkbox("Unicode normalization (NFC)", value=False, disabled=True)
+st.checkbox("Strip non-printable characters", value=False, disabled=True)
+st.checkbox("Remove BOM", value=False, disabled=True)
+st.checkbox("Normalize line endings", value=False, disabled=True)
+st.selectbox("Case conversion", ["None", "UPPER", "lower", "Title Case", "Sentence case"], disabled=True)
+
+st.divider()
+st.button("Clean Text", type="primary", use_container_width=True, disabled=True)
+
+# ---------------------------------------------------------------------------
+# Footer
+# ---------------------------------------------------------------------------
+
+st.divider()
+st.caption(
+    "Runs locally. Your data never leaves this computer. "
+    "| DataTools v3.0"
+)
--- a/src/gui/pages/3_Format_Standardizer.py
+++ b/src/gui/pages/3_Format_Standardizer.py
@@ -0,0 +1,86 @@
+"""DataTools Format Standardizer — stub page."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import streamlit as st
+
+_project_root = Path(__file__).resolve().parent.parent.parent.parent
+if str(_project_root) not in sys.path:
+    sys.path.insert(0, str(_project_root))
+
+# ---------------------------------------------------------------------------
+# Header
+# ---------------------------------------------------------------------------
+
+st.title("📐 Format Standardizer")
+st.caption("Standardize formats across columns for consistency.")
+
+st.info("This tool is under development.")
+
+# ---------------------------------------------------------------------------
+# What this tool will do
+# ---------------------------------------------------------------------------
+
+st.markdown("""
+**Features:**
+- Date format standardization (e.g., MM/DD/YYYY → YYYY-MM-DD)
+- Phone number formatting (E.164, national, international)
+- Currency normalization ($1,000.00 → 1000.00)
+- Name casing (JOHN DOE → John Doe)
+- Address abbreviation expansion (St. → Street, Ave. → Avenue)
+- Boolean standardization (Yes/No/Y/N/1/0 → True/False)
+""")
+
+st.divider()
+
+# ---------------------------------------------------------------------------
+# File upload (functional)
+# ---------------------------------------------------------------------------
+
+uploaded = st.file_uploader(
+    "Upload CSV or Excel file",
+    type=["csv", "tsv", "xlsx", "xls"],
+    help="Upload a file to preview. Processing is not yet available.",
+    key="fmtstd_file_upload",
+)
+
+if uploaded is not None:
+    import pandas as pd
+    try:
+        if uploaded.name.endswith((".xlsx", ".xls")):
+            df = pd.read_excel(uploaded)
+        else:
+            df = pd.read_csv(uploaded)
+        st.subheader(f"Preview: {uploaded.name}")
+        st.caption(f"{len(df)} rows, {len(df.columns)} columns")
+        st.dataframe(df.head(10), use_container_width=True)
+    except Exception as e:
+        st.error(f"Failed to read file: {e}")
+
+# ---------------------------------------------------------------------------
+# Placeholder options
+# ---------------------------------------------------------------------------
+
+st.subheader("Format Rules")
+
+st.selectbox("Date format", ["YYYY-MM-DD", "MM/DD/YYYY", "DD/MM/YYYY", "DD-Mon-YYYY"], disabled=True)
+st.selectbox("Phone format", ["E.164 (+15551234567)", "National ((555) 123-4567)", "Digits only"], disabled=True)
+st.selectbox("Currency handling", ["Strip symbols, keep number", "Normalize to 2 decimals", "Keep as-is"], disabled=True)
+st.selectbox("Name casing", ["Title Case", "UPPER", "lower", "As-is"], disabled=True)
+st.checkbox("Expand address abbreviations", value=False, disabled=True)
+
+st.divider()
+st.button("Standardize Formats", type="primary", use_container_width=True, disabled=True)
+
+# ---------------------------------------------------------------------------
+# Footer
+# ---------------------------------------------------------------------------
+
+st.divider()
+st.caption(
+    "Runs locally. Your data never leaves this computer. "
+    "| DataTools v3.0"
+)
--- a/src/gui/pages/4_Missing_Values.py
+++ b/src/gui/pages/4_Missing_Values.py
@@ -0,0 +1,102 @@
+"""DataTools Missing Value Handler — stub page."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import streamlit as st
+
+_project_root = Path(__file__).resolve().parent.parent.parent.parent
+if str(_project_root) not in sys.path:
+    sys.path.insert(0, str(_project_root))
+
+# ---------------------------------------------------------------------------
+# Header
+# ---------------------------------------------------------------------------
+
+st.title("🕳️ Missing Value Handler")
+st.caption("Detect, analyze, and handle missing values in your data.")
+
+st.info("This tool is under development.")
+
+# ---------------------------------------------------------------------------
+# What this tool will do
+# ---------------------------------------------------------------------------
+
+st.markdown("""
+**Features:**
+- Detect disguised nulls (empty strings, "N/A", "n/a", "-", "NULL", "None", etc.)
+- Missingness analysis: per-column counts, percentages, and patterns
+- Visualize missing data heatmap
+- Imputation strategies: drop rows/columns, fill with mean/median/mode, forward-fill, backward-fill
+- Custom sentinel value replacement
+- Before/after comparison
+""")
+
+st.divider()
+
+# ---------------------------------------------------------------------------
+# File upload (functional)
+# ---------------------------------------------------------------------------
+
+uploaded = st.file_uploader(
+    "Upload CSV or Excel file",
+    type=["csv", "tsv", "xlsx", "xls"],
+    help="Upload a file to preview. Processing is not yet available.",
+    key="missing_file_upload",
+)
+
+if uploaded is not None:
+    import pandas as pd
+    try:
+        if uploaded.name.endswith((".xlsx", ".xls")):
+            df = pd.read_excel(uploaded)
+        else:
+            df = pd.read_csv(uploaded)
+        st.subheader(f"Preview: {uploaded.name}")
+        st.caption(f"{len(df)} rows, {len(df.columns)} columns")
+        st.dataframe(df.head(10), use_container_width=True)
+    except Exception as e:
+        st.error(f"Failed to read file: {e}")
+
+# ---------------------------------------------------------------------------
+# Placeholder options
+# ---------------------------------------------------------------------------
+
+st.subheader("Detection Settings")
+
+st.text_input(
+    "Null patterns (comma-separated)",
+    value="N/A, n/a, NA, -, NULL, None, empty, .",
+    disabled=True,
+    help="Values to treat as missing.",
+)
+
+st.subheader("Handling Strategy")
+
+st.selectbox("Strategy", [
+    "Drop rows with any missing",
+    "Drop rows above threshold",
+    "Fill with mean (numeric)",
+    "Fill with median (numeric)",
+    "Fill with mode (categorical)",
+    "Forward-fill",
+    "Backward-fill",
+    "Custom value",
+], disabled=True)
+
+st.slider("Drop threshold (%)", 0, 100, 50, disabled=True, help="Drop rows missing more than this % of columns.")
+
+st.divider()
+st.button("Handle Missing Values", type="primary", use_container_width=True, disabled=True)
+
+# ---------------------------------------------------------------------------
+# Footer
+# ---------------------------------------------------------------------------
+
+st.divider()
+st.caption(
+    "Runs locally. Your data never leaves this computer. "
+    "| DataTools v3.0"
+)
--- a/src/gui/pages/5_Column_Mapper.py
+++ b/src/gui/pages/5_Column_Mapper.py
@@ -0,0 +1,93 @@
+"""DataTools Column Mapper — stub page."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import streamlit as st
+
+_project_root = Path(__file__).resolve().parent.parent.parent.parent
+if str(_project_root) not in sys.path:
+    sys.path.insert(0, str(_project_root))
+
+# ---------------------------------------------------------------------------
+# Header
+# ---------------------------------------------------------------------------
+
+st.title("🗂️ Column Mapper")
+st.caption("Rename columns, enforce a target schema, and coerce types.")
+
+st.info("This tool is under development.")
+
+# ---------------------------------------------------------------------------
+# What this tool will do
+# ---------------------------------------------------------------------------
+
+st.markdown("""
+**Features:**
+- Rename columns via interactive mapping table
+- Load a target schema (JSON/CSV) to auto-map columns
+- Fuzzy column name matching for automatic suggestions
+- Type coercion (string → int, string → date, etc.)
+- Drop unmapped columns or keep as-is
+- Reorder columns to match target schema
+""")
+
+st.divider()
+
+# ---------------------------------------------------------------------------
+# File upload (functional)
+# ---------------------------------------------------------------------------
+
+uploaded = st.file_uploader(
+    "Upload CSV or Excel file",
+    type=["csv", "tsv", "xlsx", "xls"],
+    help="Upload a file to preview. Processing is not yet available.",
+    key="colmap_file_upload",
+)
+
+if uploaded is not None:
+    import pandas as pd
+    try:
+        if uploaded.name.endswith((".xlsx", ".xls")):
+            df = pd.read_excel(uploaded)
+        else:
+            df = pd.read_csv(uploaded)
+        st.subheader(f"Preview: {uploaded.name}")
+        st.caption(f"{len(df)} rows, {len(df.columns)} columns")
+        st.dataframe(df.head(10), use_container_width=True)
+
+        st.subheader("Column Mapping")
+        st.caption("Map source columns to target names. (Interactive mapping coming soon.)")
+        mapping_data = pd.DataFrame({
+            "Source Column": df.columns.tolist(),
+            "Target Column": df.columns.tolist(),
+            "Type": ["auto"] * len(df.columns),
+        })
+        st.dataframe(mapping_data, use_container_width=True, hide_index=True)
+    except Exception as e:
+        st.error(f"Failed to read file: {e}")
+
+# ---------------------------------------------------------------------------
+# Placeholder options
+# ---------------------------------------------------------------------------
+
+st.subheader("Schema Options")
+
+st.file_uploader("Load target schema (JSON)", type=["json"], disabled=True, key="colmap_schema")
+st.checkbox("Drop unmapped columns", value=False, disabled=True)
+st.checkbox("Reorder to match schema", value=True, disabled=True)
+
+st.divider()
+st.button("Apply Column Mapping", type="primary", use_container_width=True, disabled=True)
+
+# ---------------------------------------------------------------------------
+# Footer
+# ---------------------------------------------------------------------------
+
+st.divider()
+st.caption(
+    "Runs locally. Your data never leaves this computer. "
+    "| DataTools v3.0"
+)
--- a/src/gui/pages/6_Outlier_Detector.py
+++ b/src/gui/pages/6_Outlier_Detector.py
@@ -0,0 +1,88 @@
+"""DataTools Outlier Detector — stub page."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import streamlit as st
+
+_project_root = Path(__file__).resolve().parent.parent.parent.parent
+if str(_project_root) not in sys.path:
+    sys.path.insert(0, str(_project_root))
+
+# ---------------------------------------------------------------------------
+# Header
+# ---------------------------------------------------------------------------
+
+st.title("📊 Outlier Detector")
+st.caption("Detect and handle outliers in numeric columns.")
+
+st.info("This tool is under development.")
+
+# ---------------------------------------------------------------------------
+# What this tool will do
+# ---------------------------------------------------------------------------
+
+st.markdown("""
+**Features:**
+- Z-score detection (configurable threshold)
+- IQR (interquartile range) detection
+- MAD (median absolute deviation) detection
+- Domain-rule violations (e.g., age < 0, price > $1M)
+- Visual outlier highlighting in data preview
+- Handling: flag only, remove, cap/winsorize to bounds
+""")
+
+st.divider()
+
+# ---------------------------------------------------------------------------
+# File upload (functional)
+# ---------------------------------------------------------------------------
+
+uploaded = st.file_uploader(
+    "Upload CSV or Excel file",
+    type=["csv", "tsv", "xlsx", "xls"],
+    help="Upload a file to preview. Processing is not yet available.",
+    key="outlier_file_upload",
+)
+
+if uploaded is not None:
+    import pandas as pd
+    try:
+        if uploaded.name.endswith((".xlsx", ".xls")):
+            df = pd.read_excel(uploaded)
+        else:
+            df = pd.read_csv(uploaded)
+        st.subheader(f"Preview: {uploaded.name}")
+        st.caption(f"{len(df)} rows, {len(df.columns)} columns")
+        st.dataframe(df.head(10), use_container_width=True)
+    except Exception as e:
+        st.error(f"Failed to read file: {e}")
+
+# ---------------------------------------------------------------------------
+# Placeholder options
+# ---------------------------------------------------------------------------
+
+st.subheader("Detection Method")
+
+st.selectbox("Method", ["Z-Score", "IQR (Interquartile Range)", "MAD (Median Absolute Deviation)"], disabled=True)
+st.slider("Z-Score threshold", 1.0, 5.0, 3.0, 0.1, disabled=True)
+st.slider("IQR multiplier", 1.0, 3.0, 1.5, 0.1, disabled=True)
+
+st.subheader("Handling")
+
+st.selectbox("Action", ["Flag only (add column)", "Remove outlier rows", "Cap / Winsorize to bounds"], disabled=True)
+
+st.divider()
+st.button("Detect Outliers", type="primary", use_container_width=True, disabled=True)
+
+# ---------------------------------------------------------------------------
+# Footer
+# ---------------------------------------------------------------------------
+
+st.divider()
+st.caption(
+    "Runs locally. Your data never leaves this computer. "
+    "| DataTools v3.0"
+)
--- a/src/gui/pages/7_Multi_File_Merger.py
+++ b/src/gui/pages/7_Multi_File_Merger.py
@@ -0,0 +1,86 @@
+"""DataTools Multi-File Merger — stub page."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import streamlit as st
+
+_project_root = Path(__file__).resolve().parent.parent.parent.parent
+if str(_project_root) not in sys.path:
+    sys.path.insert(0, str(_project_root))
+
+# ---------------------------------------------------------------------------
+# Header
+# ---------------------------------------------------------------------------
+
+st.title("📎 Multi-File Merger")
+st.caption("Combine multiple CSV and Excel files into one dataset.")
+
+st.info("This tool is under development.")
+
+# ---------------------------------------------------------------------------
+# What this tool will do
+# ---------------------------------------------------------------------------
+
+st.markdown("""
+**Features:**
+- Upload multiple CSV/Excel files at once
+- Automatic schema alignment (matching columns by name)
+- Append mode: stack files vertically (union)
+- Join mode: merge files on shared key columns
+- Handle mismatched columns (fill missing with nulls or drop)
+- Source file tracking column
+""")
+
+st.divider()
+
+# ---------------------------------------------------------------------------
+# Multi-file upload (functional)
+# ---------------------------------------------------------------------------
+
+uploaded_files = st.file_uploader(
+    "Upload CSV or Excel files",
+    type=["csv", "tsv", "xlsx", "xls"],
+    accept_multiple_files=True,
+    help="Upload multiple files to preview. Processing is not yet available.",
+    key="merger_file_upload",
+)
+
+if uploaded_files:
+    import pandas as pd
+    for f in uploaded_files:
+        try:
+            if f.name.endswith((".xlsx", ".xls")):
+                df = pd.read_excel(f)
+            else:
+                df = pd.read_csv(f)
+            st.subheader(f"Preview: {f.name}")
+            st.caption(f"{len(df)} rows, {len(df.columns)} columns — Columns: {', '.join(df.columns[:10])}{'...' if len(df.columns) > 10 else ''}")
+            st.dataframe(df.head(5), use_container_width=True)
+        except Exception as e:
+            st.error(f"Failed to read {f.name}: {e}")
+
+# ---------------------------------------------------------------------------
+# Placeholder options
+# ---------------------------------------------------------------------------
+
+st.subheader("Merge Strategy")
+
+st.selectbox("Mode", ["Append (stack vertically)", "Join on key columns", "Schema alignment (smart merge)"], disabled=True)
+st.selectbox("Mismatched columns", ["Fill with null", "Drop non-shared columns", "Error"], disabled=True)
+st.checkbox("Add source filename column", value=True, disabled=True)
+
+st.divider()
+st.button("Merge Files", type="primary", use_container_width=True, disabled=True)
+
+# ---------------------------------------------------------------------------
+# Footer
+# ---------------------------------------------------------------------------
+
+st.divider()
+st.caption(
+    "Runs locally. Your data never leaves this computer. "
+    "| DataTools v3.0"
+)
--- a/src/gui/pages/8_Validator_Reporter.py
+++ b/src/gui/pages/8_Validator_Reporter.py
@@ -0,0 +1,93 @@
+"""DataTools Validator & Reporter — stub page."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import streamlit as st
+
+_project_root = Path(__file__).resolve().parent.parent.parent.parent
+if str(_project_root) not in sys.path:
+    sys.path.insert(0, str(_project_root))
+
+# ---------------------------------------------------------------------------
+# Header
+# ---------------------------------------------------------------------------
+
+st.title("✅ Validator & Reporter")
+st.caption("Validate data against rules and generate quality reports.")
+
+st.info("This tool is under development.")
+
+# ---------------------------------------------------------------------------
+# What this tool will do
+# ---------------------------------------------------------------------------
+
+st.markdown("""
+**Features:**
+- Column-level validation rules (not null, unique, regex pattern, range, enum)
+- Cross-column validation (e.g., start_date < end_date)
+- Data quality score per column and overall
+- Generate PDF quality report
+- Generate Excel report with flagged rows highlighted
+- Summary dashboard: pass/fail counts, severity breakdown
+""")
+
+st.divider()
+
+# ---------------------------------------------------------------------------
+# File upload (functional)
+# ---------------------------------------------------------------------------
+
+uploaded = st.file_uploader(
+    "Upload CSV or Excel file",
+    type=["csv", "tsv", "xlsx", "xls"],
+    help="Upload a file to preview. Processing is not yet available.",
+    key="validator_file_upload",
+)
+
+if uploaded is not None:
+    import pandas as pd
+    try:
+        if uploaded.name.endswith((".xlsx", ".xls")):
+            df = pd.read_excel(uploaded)
+        else:
+            df = pd.read_csv(uploaded)
+        st.subheader(f"Preview: {uploaded.name}")
+        st.caption(f"{len(df)} rows, {len(df.columns)} columns")
+        st.dataframe(df.head(10), use_container_width=True)
+    except Exception as e:
+        st.error(f"Failed to read file: {e}")
+
+# ---------------------------------------------------------------------------
+# Placeholder options
+# ---------------------------------------------------------------------------
+
+st.subheader("Validation Rules")
+
+st.file_uploader("Load rules file (JSON)", type=["json"], disabled=True, key="validator_rules")
+st.multiselect("Quick checks", [
+    "No null values",
+    "No duplicate rows",
+    "All emails valid",
+    "All dates parseable",
+    "Numeric columns in range",
+], disabled=True)
+
+st.subheader("Report Format")
+
+st.selectbox("Output format", ["Excel (flagged rows)", "PDF summary", "Both"], disabled=True)
+
+st.divider()
+st.button("Validate & Generate Report", type="primary", use_container_width=True, disabled=True)
+
+# ---------------------------------------------------------------------------
+# Footer
+# ---------------------------------------------------------------------------
+
+st.divider()
+st.caption(
+    "Runs locally. Your data never leaves this computer. "
+    "| DataTools v3.0"
+)
--- a/src/gui/pages/9_Pipeline_Runner.py
+++ b/src/gui/pages/9_Pipeline_Runner.py
@@ -0,0 +1,95 @@
+"""DataTools Pipeline Runner — stub page."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import streamlit as st
+
+_project_root = Path(__file__).resolve().parent.parent.parent.parent
+if str(_project_root) not in sys.path:
+    sys.path.insert(0, str(_project_root))
+
+# ---------------------------------------------------------------------------
+# Header
+# ---------------------------------------------------------------------------
+
+st.title("⚙️ Pipeline Runner")
+st.caption("Chain tools in sequence and pass output between steps automatically.")
+
+st.info("This tool is under development.")
+
+# ---------------------------------------------------------------------------
+# What this tool will do
+# ---------------------------------------------------------------------------
+
+st.markdown("""
+**Features:**
+- Select tools to run in sequence
+- Recommended order: Text Cleaner → Format Standardizer → Missing Values → Deduplicator → Validator
+- Each step's output feeds into the next step's input
+- Per-step configuration overrides
+- Progress tracking across all steps
+- Final combined report
+""")
+
+st.divider()
+
+# ---------------------------------------------------------------------------
+# File upload (functional)
+# ---------------------------------------------------------------------------
+
+uploaded = st.file_uploader(
+    "Upload CSV or Excel file",
+    type=["csv", "tsv", "xlsx", "xls"],
+    help="Upload a file to preview. Processing is not yet available.",
+    key="pipeline_file_upload",
+)
+
+if uploaded is not None:
+    import pandas as pd
+    try:
+        if uploaded.name.endswith((".xlsx", ".xls")):
+            df = pd.read_excel(uploaded)
+        else:
+            df = pd.read_csv(uploaded)
+        st.subheader(f"Preview: {uploaded.name}")
+        st.caption(f"{len(df)} rows, {len(df.columns)} columns")
+        st.dataframe(df.head(10), use_container_width=True)
+    except Exception as e:
+        st.error(f"Failed to read file: {e}")
+
+# ---------------------------------------------------------------------------
+# Pipeline steps (checklist)
+# ---------------------------------------------------------------------------
+
+st.subheader("Pipeline Steps")
+st.caption("Select tools to include in the pipeline (recommended order):")
+
+st.checkbox("1. Text Cleaner", value=True, disabled=True)
+st.checkbox("2. Format Standardizer", value=True, disabled=True)
+st.checkbox("3. Missing Value Handler", value=True, disabled=True)
+st.checkbox("4. Column Mapper", value=False, disabled=True)
+st.checkbox("5. Outlier Detector", value=False, disabled=True)
+st.checkbox("6. Deduplicator", value=True, disabled=True)
+st.checkbox("7. Multi-File Merger", value=False, disabled=True)
+st.checkbox("8. Validator & Reporter", value=True, disabled=True)
+
+st.subheader("Pipeline Configuration")
+
+st.selectbox("On error", ["Stop pipeline", "Skip step and continue", "Prompt for decision"], disabled=True)
+st.checkbox("Generate combined report at end", value=True, disabled=True)
+
+st.divider()
+st.button("Run Pipeline", type="primary", use_container_width=True, disabled=True)
+
+# ---------------------------------------------------------------------------
+# Footer
+# ---------------------------------------------------------------------------
+
+st.divider()
+st.caption(
+    "Runs locally. Your data never leaves this computer. "
+    "| DataTools v3.0"
+)