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builders/build_docx.py

@@ -1,410 +1,410 @@
-#!/usr/bin/env python3
-"""
-builders/build_docx.py — Enhanced Word report builder.
-Sections: Cover, Executive Summary, Company Profile, Industry Profile,
-SWOT, Industry Trends, Financial Trends, Ratios, DuPont, Peer Comparison,
-Key Insights, Risks, Things to Watch, Limitations, Sources.
-"""
-import json, sys, datetime
-from pathlib import Path
-from typing import Dict, List, Optional
-
-try:
-    from docx import Document
-    from docx.shared import Pt, Cm, RGBColor, Inches
-    from docx.enum.text import WD_ALIGN_PARAGRAPH
-    from docx.enum.table import WD_ALIGN_VERTICAL, WD_TABLE_ALIGNMENT
-    from docx.oxml.ns import qn
-    from docx.oxml import OxmlElement
-except ImportError:
-    print("ERROR: pip install python-docx", file=sys.stderr); sys.exit(1)
-
-# ── Colors ────────────────────────────────────────────────────────────────────
-NAVY = RGBColor(0x1F, 0x4E, 0x78)
-GRAY = RGBColor(0x59, 0x59, 0x59)
-GREEN_DARK = RGBColor(0x37, 0x5A, 0x3D)
-
-# ── Formatting helpers ────────────────────────────────────────────────────────
-
-def _shd(cell, hex_color):
-    tcp = cell._tc.get_or_add_tcPr()
-    s = OxmlElement("w:shd")
-    s.set(qn("w:val"), "clear"); s.set(qn("w:color"), "auto"); s.set(qn("w:fill"), hex_color)
-    tcp.append(s)
-
-def _borders(cell, color="BFBFBF", sz="4"):
-    tcp = cell._tc.get_or_add_tcPr()
-    tb = OxmlElement("w:tcBorders")
-    for edge in ("top","left","bottom","right"):
-        b = OxmlElement(f"w:{edge}")
-        b.set(qn("w:val"),"single"); b.set(qn("w:sz"),sz); b.set(qn("w:color"),color)
-        tb.append(b)
-    tcp.append(tb)
-
-def _hdr_repeat(row):
-    trp = row._tr.get_or_add_trPr()
-    th = OxmlElement("w:tblHeader"); th.set(qn("w:val"),"true"); trp.append(th)
-
-def _add_heading(doc, text, level=1):
-    sizes = {0:28, 1:18, 2:14, 3:12}
-    p = doc.add_paragraph()
-    r = p.add_run(text)
-    r.font.size = Pt(sizes.get(level, 12)); r.font.bold = True
-    r.font.color.rgb = NAVY; r.font.name = "Calibri"
-    p.paragraph_format.space_before = Pt(12); p.paragraph_format.space_after = Pt(6)
-    return p
-
-def _add_text(doc, text, size=11):
-    if not text: return
-    for block in [b.strip() for b in str(text).split("\n\n") if b.strip()]:
-        p = doc.add_paragraph()
-        r = p.add_run(block.replace("\n"," "))
-        r.font.size = Pt(size); r.font.name = "Calibri"
-        p.paragraph_format.space_after = Pt(8)
-
-def _fmt_v(v, kind="money", currency="INR", units="Crores"):
-    if v is None: return "\u2014"
-    try:
-        sym = "\u20b9" if currency == "INR" else ("$" if currency == "USD" else "")
-        if kind == "money":
-            # Data is already scaled (Crores for INR, Millions for USD)
-            av = abs(v)
-            if currency == "INR":
-                if av >= 100000:  return f"{sym}{v/100000:,.2f} L.Cr"
-                return f"{sym}{v:,.2f} Cr"
-            else:
-                if av >= 1000:    return f"{sym}{v/1000:,.2f}B"
-                return f"{sym}{v:,.2f}M"
-        if kind == "money_dec": return f"{v:,.2f}"
-        if kind == "pct":   return f"{v*100:.1f}%"
-        if kind in ("ratio", "days"): return f"{v:.2f}"
-        return str(v)
-    except: return str(v)
-
-def _page_break(doc):
-    p = doc.add_paragraph(); p.add_run().add_break()
-
-# ── Table builder ─────────────────────────────────────────────────────────────
-
-def _table(doc, headers, rows, formats=None, currency="INR", units="Crores"):
-    t = doc.add_table(rows=1+len(rows), cols=len(headers))
-    t.alignment = WD_TABLE_ALIGNMENT.LEFT
-    hc = t.rows[0].cells
-    for i, h in enumerate(headers):
-        hc[i].text = ""
-        p = hc[i].paragraphs[0]
-        r = p.add_run(str(h)); r.font.bold = True; r.font.color.rgb = RGBColor(0xFF,0xFF,0xFF)
-        r.font.size = Pt(10); r.font.name = "Calibri"
-        p.alignment = WD_ALIGN_PARAGRAPH.CENTER if i > 0 else WD_ALIGN_PARAGRAPH.LEFT
-        _shd(hc[i], "1F4E78"); _borders(hc[i])
-    _hdr_repeat(t.rows[0])
-    for ri, row in enumerate(rows):
-        bg = "F2F2F2" if ri % 2 else "FFFFFF"
-        cells = t.rows[ri+1].cells
-        for ci, val in enumerate(row):
-            cells[ci].text = ""
-            p = cells[ci].paragraphs[0]
-            kind = formats[ci] if formats and ci < len(formats) else "money"
-            if ci == 0:
-                text = str(val) if val is not None else "—"
-            else:
-                text = _fmt_v(val, kind, currency, units)
-            r = p.add_run(text); r.font.size = Pt(10); r.font.name = "Calibri"
-            p.alignment = WD_ALIGN_PARAGRAPH.LEFT if ci == 0 else WD_ALIGN_PARAGRAPH.RIGHT
-            _shd(cells[ci], bg); _borders(cells[ci])
-    doc.add_paragraph().paragraph_format.space_after = Pt(6)
-    return t
-
-def _gather(items, src, years):
-    return [[label] + [(src.get(key) or [None]*len(years))[:len(years)][i]
-                       for i in range(len(years))] for label, key in items]
-
-# ── Section builders ──────────────────────────────────────────────────────────
-
-def _cover(doc, data):
-    co = data.get("company", {})
-    units_label = data.get("units_label", data.get("units", "Crores"))
-    currency = data.get("currency", co.get("currency", "INR"))
-    for _ in range(3): doc.add_paragraph()
-    p = doc.add_paragraph()
-    r = p.add_run("Financial Statement Analysis")
-    r.font.size = Pt(32); r.font.bold = True; r.font.color.rgb = NAVY; r.font.name = "Calibri"
-    p = doc.add_paragraph()
-    r = p.add_run(co.get("name",""))
-    r.font.size = Pt(24); r.font.color.rgb = GRAY; r.font.name = "Calibri"
-    p = doc.add_paragraph()
-    r = p.add_run(f"{co.get('ticker','')}  \u00b7  {co.get('exchange','')}  \u00b7  {co.get('industry','')}")
-    r.font.size = Pt(13); r.font.color.rgb = GRAY; r.font.italic = True; r.font.name = "Calibri"
-    for _ in range(6): doc.add_paragraph()
-    for text in [
-        f"Report date: {co.get('report_date', datetime.date.today().isoformat())}",
-        f"Analysis window: {', '.join(data.get('years',[]))}",
-        f"Reporting currency: {currency}  |  Units: {units_label}",
-        f"Market cap: {co.get('market_cap', 'N/A')}  |  Share price: {co.get('share_price', 'N/A')}",
-        f"AI models used: {', '.join(data.get('models_used',[]) or ['Data-only (no AI narrative)'])}",
-        f"Data source: {data.get('source', 'Yahoo Finance (yfinance)')}",
-    ]:
-        p = doc.add_paragraph(); r = p.add_run(text)
-        r.font.size = Pt(11); r.font.color.rgb = GRAY; r.font.name = "Calibri"
-    _page_break(doc)
-
-
-def _swot_section(doc, data):
-    _add_heading(doc, "SWOT Analysis", 1)
-    swot = data.get("swot", {})
-    configs = [
-        ("Strengths",     "strengths",     "C6EFCE", GREEN_DARK),
-        ("Weaknesses",    "weaknesses",    "FFC7CE", RGBColor(0xC0,0x00,0x00)),
-        ("Opportunities", "opportunities", "DDEBF7", NAVY),
-        ("Threats",       "threats",       "FFEB9C", RGBColor(0x7F,0x60,0x00)),
-    ]
-    
-    parsed_any = any(len(swot.get(k, [])) > 0 for _, k, _, _ in configs)
-    
-    if not parsed_any and swot.get("raw"):
-        for line in swot["raw"].split("\n"):
-            if line.strip():
-                p = doc.add_paragraph()
-                r = p.add_run(line.strip())
-                r.font.size = Pt(11); r.font.name = "Calibri"
-        doc.add_paragraph()
-        return
-        
-    t = doc.add_table(rows=2, cols=2)
-    t.alignment = WD_TABLE_ALIGNMENT.LEFT
-    idx = 0
-    for row_i in range(2):
-        for col_i in range(2):
-            if idx >= len(configs): break
-            label, key, bg, color = configs[idx]; idx += 1
-            cell = t.cell(row_i, col_i)
-            cell.text = ""
-            p = cell.paragraphs[0]
-            r = p.add_run(label); r.font.bold = True; r.font.size = Pt(12)
-            r.font.color.rgb = color; r.font.name = "Calibri"
-            _shd(cell, bg)
-            for item in swot.get(key, []):
-                p2 = cell.add_paragraph(f"• {item}")
-                for run in p2.runs: run.font.size = Pt(10); run.font.name = "Calibri"
-    doc.add_paragraph()
-
-
-def _trends_section(doc, data):
-    _add_heading(doc, "Multi-Year Financial Trends", 1)
-    years = data.get("years", [])
-    currency = data.get("currency", "INR")
-    units_label = data.get("units_label", data.get("units", "Crores"))
-    trend = data.get("trend_analysis", {})
-
-    for section_title, prose_key, items, src_key, fmts in [
-        ("Revenue & Profitability", "revenue_and_profitability",
-         [("Revenue","revenue"),("Gross Profit","gross_profit"),("Operating Income","operating_income"),
-          ("Net Income","net_income")],
-         "income_statement", ["text"]+["money"]*len(years)),
-        ("Balance Sheet Strength", "balance_sheet_strength",
-         [("Total Assets","total_assets"),("Total Equity","total_equity"),
-          ("Long-term Debt","long_term_debt"),("Current Ratio","current_ratio")],
-         "balance_sheet", ["text"]+["money"]*len(years)),
-        ("Cash Flow Generation", "cash_flow_generation",
-         [("Cash from Operations","cash_from_operations"),("Capex","capex"),("Free Cash Flow","free_cash_flow")],
-         "cash_flow", ["text"]+["money"]*len(years)),
-    ]:
-        _add_heading(doc, section_title, 2)
-        _add_text(doc, trend.get(prose_key,""))
-        src_data = data.get(src_key, {})
-        if src_key == "balance_sheet":
-            merged = dict(data.get("balance_sheet",{})); merged.update(data.get("ratios",{}))
-            src_data = merged
-        rows = _gather(items, src_data, years)
-        _table(doc, [f"({units_label})"]+years, rows, fmts, currency, units_label)
-
-
-def _ratio_section(doc, data):
-    _add_heading(doc, "Financial Ratio Analysis", 1)
-    _add_text(doc, data.get("ratio_analysis_commentary",""))
-    years = data.get("years", [])
-    ratios = data.get("ratios", {})
-    currency = data.get("currency", "INR")
-    units_label = data.get("units_label", data.get("units", "Crores"))
-    for sec_name, items in [
-        ("Profitability", [("Gross Margin","gross_margin","pct"),("Operating Margin","operating_margin","pct"),
-                           ("Net Margin","net_margin","pct"),("ROE","roe","pct"),("ROIC","roic","pct")]),
-        ("Liquidity & Solvency", [("Current Ratio","current_ratio","ratio"),("Quick Ratio","quick_ratio","ratio"),
-                                   ("Debt-to-Equity","debt_to_equity","ratio"),("Net Debt/EBITDA","net_debt_to_ebitda","ratio")]),
-        ("Efficiency", [("Asset Turnover","asset_turnover","ratio"),("DSO (days)","days_sales_outstanding","days"),
-                        ("DPO (days)","days_payables","days"),("CCC (days)","cash_conversion_cycle","days")]),
-        ("Cash Flow Quality", [("OCF/Net Income","ocf_to_net_income","ratio"),("FCF Margin","fcf_margin","pct"),
-                                ("FCF Conversion","fcf_conversion","ratio")]),
-    ]:
-        _add_heading(doc, sec_name, 3)
-        rows = []
-        for label, key, kind in items:
-            vals = (ratios.get(key,[]) + [None]*len(years))[:len(years)]
-            rows.append([label] + list(vals))
-        kinds = ["text"] + [items[0][2]]*len(years)
-        _table(doc, ["Metric"]+years, rows, kinds, currency, units_label)
-
-
-def _dupont_section(doc, data):
-    _add_heading(doc, "DuPont Analysis", 1)
-    p = doc.add_paragraph()
-    r = p.add_run("ROE  =  Net Margin  \u00d7  Asset Turnover  \u00d7  Equity Multiplier")
-    r.font.size = Pt(11); r.font.bold = True; r.font.name = "Calibri"
-    years = data.get("years", [])
-    dp = data.get("dupont", {})
-    currency = data.get("currency", "INR")
-    rows = []
-    for label, key, kind in [
-        ("Net Margin","net_margin","pct"),("\u00d7 Asset Turnover","asset_turnover","ratio"),
-        ("\u00d7 Equity Multiplier","equity_multiplier","ratio"),("= ROE (DuPont)","roe_computed","pct"),
-        ("ROE (Reported)","roe_reported","pct"),
-    ]:
-        vals = (dp.get(key,[]) + [None]*len(years))[:len(years)]
-        rows.append([label]+list(vals))
-    _table(doc, ["Component"]+years, rows, ["text"]+["pct"]*len(years), currency)
-
-
-def _peer_section(doc, data):
-    _add_heading(doc, "Peer Benchmarking", 1)
-    _add_text(doc, data.get("peer_comparison_commentary",""))
-    peers = data.get("peers", [])
-    currency = data.get("currency", "INR")
-    units_label = data.get("units_label", "Crores")
-    co = data.get("company",{}); ratios = data.get("ratios",{})
-    inc = data.get("income_statement",{}); val = data.get("valuation_snapshot",{})
-    def last(arr):
-        for v in reversed(arr or []):
-            if v is not None: return v
-        return None
-    headers = ["Company","Revenue","Op Margin","Net Margin","ROIC","D/E","FCF Margin","P/E"]
-    fmts = ["text","money","pct","pct","pct","ratio","pct","ratio"]
-    rows = [[
-        co.get("name",co.get("ticker",""))+" (Target)",
-        last(inc.get("revenue",[])), last(ratios.get("operating_margin",[])),
-        last(ratios.get("net_margin",[])), last(ratios.get("roic",[])),
-        last(ratios.get("debt_to_equity",[])), last(ratios.get("fcf_margin",[])),
-        val.get("pe_ttm"),
-    ]]
-    if not peers:
-        doc.add_paragraph("Peer comparison data not available for this run.")
-    for p in peers:
-        rows.append([p.get("name",p.get("ticker","")), p.get("revenue"), p.get("operating_margin"),
-                     p.get("net_margin"), p.get("roic"), p.get("debt_to_equity"),
-                     p.get("fcf_margin"), p.get("pe_ttm")])
-    if rows:
-        _table(doc, headers, rows, fmts, currency, units_label)
-
-
-def _bullets(doc, items, heading=None, level=1):
-    if heading: _add_heading(doc, heading, level)
-    for item in items:
-        if isinstance(item, dict):
-            p = doc.add_paragraph(style="List Bullet")
-            r1 = p.add_run(f"{item.get('category','')}: "); r1.font.bold=True; r1.font.size=Pt(11); r1.font.name="Calibri"
-            r2 = p.add_run(item.get("description","")); r2.font.size=Pt(11); r2.font.name="Calibri"
-        else:
-            p = doc.add_paragraph(str(item), style="List Bullet")
-            for r in p.runs: r.font.size=Pt(11); r.font.name="Calibri"
-        p.paragraph_format.space_after = Pt(2)
-
-
-def _sources_section(doc, data):
-    _add_heading(doc, "Sources", 1)
-    for i, src in enumerate(data.get("sources",[]), 1):
-        p = doc.add_paragraph()
-        p.add_run(f"{i}. ").font.size = Pt(10)
-        url_r = p.add_run(src.get("url",""))
-        url_r.font.size=Pt(10); url_r.font.color.rgb=RGBColor(0x05,0x63,0xC1); url_r.font.name="Calibri"
-        suffix = ""
-        if src.get("description"): suffix += f" — {src['description']}"
-        if src.get("retrieved"):   suffix += f" (retrieved {src['retrieved']})"
-        if suffix:
-            sr = p.add_run(suffix); sr.font.size=Pt(10); sr.font.color.rgb=GRAY; sr.font.name="Calibri"
-
-
-def _disclaimer(doc):
-    p = doc.add_paragraph()
-    r = p.add_run(
-        "Disclaimer: This report is generated from public financial data and AI analysis for informational "
-        "purposes only. It is not investment advice. Verify all figures against primary sources before "
-        "making any financial decisions."
-    )
-    r.font.size=Pt(9); r.font.italic=True; r.font.color.rgb=GRAY; r.font.name="Calibri"
-    p.paragraph_format.space_before=Pt(12)
-
-
-# ── Orchestrator ──────────────────────────────────────────────────────────────
-
-def build_document(data: dict, output_path: str) -> None:
-    doc = Document()
-    for section in doc.sections:
-        section.left_margin = section.right_margin = Cm(2.0)
-        section.top_margin  = section.bottom_margin = Cm(2.0)
-
-    _cover(doc, data)
-
-    # Executive Summary
-    _add_heading(doc, "Executive Summary", 1)
-    _add_text(doc, data.get("executive_summary",""))
-
-    # Company & Industry Profile
-    _add_heading(doc, "Company Profile", 1)
-    _add_text(doc, data.get("company_overview",""))
-
-    _add_heading(doc, "Industry Profile", 1)
-    _add_text(doc, data.get("industry_profile",""))
-
-    _add_heading(doc, "Industry Trends", 1)
-    _add_text(doc, data.get("industry_trends",""))
-
-    _swot_section(doc, data)
-    _trends_section(doc, data)
-    
-    # Visualizations Section
-    try:
-        from builders.chart_builder import generate_all_charts
-        charts = generate_all_charts(data)
-        if any(charts.values()):
-            _add_heading(doc, "Financial Visualizations", 1)
-            if charts.get("revenue_net_income"):
-                _add_heading(doc, "Revenue & Net Income Trend", 2)
-                doc.add_picture(charts["revenue_net_income"], width=Inches(6.0))
-            if charts.get("margins"):
-                _add_heading(doc, "Profitability Margins", 2)
-                doc.add_picture(charts["margins"], width=Inches(6.0))
-            if charts.get("cash_flow"):
-                _add_heading(doc, "Cash Flow & CAPEX", 2)
-                doc.add_picture(charts["cash_flow"], width=Inches(6.0))
-    except Exception as e:
-        print(f"Warning: Could not generate DOCX charts: {e}", file=sys.stderr)
-
-    _ratio_section(doc, data)
-    _dupont_section(doc, data)
-    _peer_section(doc, data)
-
-    if data.get("key_insights"):
-        _bullets(doc, data["key_insights"], "Key Insights", 1)
-    if data.get("risks"):
-        _bullets(doc, data["risks"], "Risks", 1)
-    if data.get("things_to_watch"):
-        _bullets(doc, data["things_to_watch"], "Things to Watch", 1)
-    if data.get("limitations_and_caveats"):
-        _bullets(doc, data["limitations_and_caveats"], "Limitations & Caveats", 1)
-
-    _sources_section(doc, data)
-    _disclaimer(doc)
-
-    Path(output_path).parent.mkdir(parents=True, exist_ok=True)
-    doc.save(output_path)
-
-
-if __name__ == "__main__":
-    import argparse
-    p = argparse.ArgumentParser()
-    p.add_argument("--input","-i",required=True)
-    p.add_argument("--output","-o",required=True)
-    args = p.parse_args()
-    with open(args.input, encoding="utf-8") as f:
-        data = json.load(f)
-    build_document(data, args.output)
-    print(f"Wrote: {args.output}")
+#!/usr/bin/env python3
+"""
+builders/build_docx.py — Enhanced Word report builder.
+Sections: Cover, Executive Summary, Company Profile, Industry Profile,
+SWOT, Industry Trends, Financial Trends, Ratios, DuPont, Peer Comparison,
+Key Insights, Risks, Things to Watch, Limitations, Sources.
+"""
+import json, sys, datetime
+from pathlib import Path
+from typing import Dict, List, Optional
+
+try:
+    from docx import Document
+    from docx.shared import Pt, Cm, RGBColor, Inches
+    from docx.enum.text import WD_ALIGN_PARAGRAPH
+    from docx.enum.table import WD_ALIGN_VERTICAL, WD_TABLE_ALIGNMENT
+    from docx.oxml.ns import qn
+    from docx.oxml import OxmlElement
+except ImportError:
+    print("ERROR: pip install python-docx", file=sys.stderr); sys.exit(1)
+
+# ── Colors ────────────────────────────────────────────────────────────────────
+NAVY = RGBColor(0x1F, 0x4E, 0x78)
+GRAY = RGBColor(0x59, 0x59, 0x59)
+GREEN_DARK = RGBColor(0x37, 0x5A, 0x3D)
+
+# ── Formatting helpers ────────────────────────────────────────────────────────
+
+def _shd(cell, hex_color):
+    tcp = cell._tc.get_or_add_tcPr()
+    s = OxmlElement("w:shd")
+    s.set(qn("w:val"), "clear"); s.set(qn("w:color"), "auto"); s.set(qn("w:fill"), hex_color)
+    tcp.append(s)
+
+def _borders(cell, color="BFBFBF", sz="4"):
+    tcp = cell._tc.get_or_add_tcPr()
+    tb = OxmlElement("w:tcBorders")
+    for edge in ("top","left","bottom","right"):
+        b = OxmlElement(f"w:{edge}")
+        b.set(qn("w:val"),"single"); b.set(qn("w:sz"),sz); b.set(qn("w:color"),color)
+        tb.append(b)
+    tcp.append(tb)
+
+def _hdr_repeat(row):
+    trp = row._tr.get_or_add_trPr()
+    th = OxmlElement("w:tblHeader"); th.set(qn("w:val"),"true"); trp.append(th)
+
+def _add_heading(doc, text, level=1):
+    sizes = {0:28, 1:18, 2:14, 3:12}
+    p = doc.add_paragraph()
+    r = p.add_run(text)
+    r.font.size = Pt(sizes.get(level, 12)); r.font.bold = True
+    r.font.color.rgb = NAVY; r.font.name = "Calibri"
+    p.paragraph_format.space_before = Pt(12); p.paragraph_format.space_after = Pt(6)
+    return p
+
+def _add_text(doc, text, size=11):
+    if not text: return
+    for block in [b.strip() for b in str(text).split("\n\n") if b.strip()]:
+        p = doc.add_paragraph()
+        r = p.add_run(block.replace("\n"," "))
+        r.font.size = Pt(size); r.font.name = "Calibri"
+        p.paragraph_format.space_after = Pt(8)
+
+def _fmt_v(v, kind="money", currency="INR", units="Crores"):
+    if v is None: return "\u2014"
+    try:
+        sym = "\u20b9" if currency == "INR" else ("$" if currency == "USD" else "")
+        if kind == "money":
+            # Data is already scaled (Crores for INR, Millions for USD)
+            av = abs(v)
+            if currency == "INR":
+                if av >= 100000:  return f"{sym}{v/100000:,.2f} L.Cr"
+                return f"{sym}{v:,.2f} Cr"
+            else:
+                if av >= 1000:    return f"{sym}{v/1000:,.2f}B"
+                return f"{sym}{v:,.2f}M"
+        if kind == "money_dec": return f"{v:,.2f}"
+        if kind == "pct":   return f"{v*100:.1f}%"
+        if kind in ("ratio", "days"): return f"{v:.2f}"
+        return str(v)
+    except: return str(v)
+
+def _page_break(doc):
+    p = doc.add_paragraph(); p.add_run().add_break()
+
+# ── Table builder ─────────────────────────────────────────────────────────────
+
+def _table(doc, headers, rows, formats=None, currency="INR", units="Crores"):
+    t = doc.add_table(rows=1+len(rows), cols=len(headers))
+    t.alignment = WD_TABLE_ALIGNMENT.LEFT
+    hc = t.rows[0].cells
+    for i, h in enumerate(headers):
+        hc[i].text = ""
+        p = hc[i].paragraphs[0]
+        r = p.add_run(str(h)); r.font.bold = True; r.font.color.rgb = RGBColor(0xFF,0xFF,0xFF)
+        r.font.size = Pt(10); r.font.name = "Calibri"
+        p.alignment = WD_ALIGN_PARAGRAPH.CENTER if i > 0 else WD_ALIGN_PARAGRAPH.LEFT
+        _shd(hc[i], "1F4E78"); _borders(hc[i])
+    _hdr_repeat(t.rows[0])
+    for ri, row in enumerate(rows):
+        bg = "F2F2F2" if ri % 2 else "FFFFFF"
+        cells = t.rows[ri+1].cells
+        for ci, val in enumerate(row):
+            cells[ci].text = ""
+            p = cells[ci].paragraphs[0]
+            kind = formats[ci] if formats and ci < len(formats) else "money"
+            if ci == 0:
+                text = str(val) if val is not None else "—"
+            else:
+                text = _fmt_v(val, kind, currency, units)
+            r = p.add_run(text); r.font.size = Pt(10); r.font.name = "Calibri"
+            p.alignment = WD_ALIGN_PARAGRAPH.LEFT if ci == 0 else WD_ALIGN_PARAGRAPH.RIGHT
+            _shd(cells[ci], bg); _borders(cells[ci])
+    doc.add_paragraph().paragraph_format.space_after = Pt(6)
+    return t
+
+def _gather(items, src, years):
+    return [[label] + [(src.get(key) or [None]*len(years))[:len(years)][i]
+                       for i in range(len(years))] for label, key in items]
+
+# ── Section builders ──────────────────────────────────────────────────────────
+
+def _cover(doc, data):
+    co = data.get("company", {})
+    units_label = data.get("units_label", data.get("units", "Crores"))
+    currency = data.get("currency", co.get("currency", "INR"))
+    for _ in range(3): doc.add_paragraph()
+    p = doc.add_paragraph()
+    r = p.add_run("Financial Statement Analysis")
+    r.font.size = Pt(32); r.font.bold = True; r.font.color.rgb = NAVY; r.font.name = "Calibri"
+    p = doc.add_paragraph()
+    r = p.add_run(co.get("name",""))
+    r.font.size = Pt(24); r.font.color.rgb = GRAY; r.font.name = "Calibri"
+    p = doc.add_paragraph()
+    r = p.add_run(f"{co.get('ticker','')}  \u00b7  {co.get('exchange','')}  \u00b7  {co.get('industry','')}")
+    r.font.size = Pt(13); r.font.color.rgb = GRAY; r.font.italic = True; r.font.name = "Calibri"
+    for _ in range(6): doc.add_paragraph()
+    for text in [
+        f"Report date: {co.get('report_date', datetime.date.today().isoformat())}",
+        f"Analysis window: {', '.join(data.get('years',[]))}",
+        f"Reporting currency: {currency}  |  Units: {units_label}",
+        f"Market cap: {co.get('market_cap', 'N/A')}  |  Share price: {co.get('share_price', 'N/A')}",
+        f"AI models used: {', '.join(data.get('models_used',[]) or ['Data-only (no AI narrative)'])}",
+        f"Data source: {data.get('source', 'Yahoo Finance (yahooquery)')}",
+    ]:
+        p = doc.add_paragraph(); r = p.add_run(text)
+        r.font.size = Pt(11); r.font.color.rgb = GRAY; r.font.name = "Calibri"
+    _page_break(doc)
+
+
+def _swot_section(doc, data):
+    _add_heading(doc, "SWOT Analysis", 1)
+    swot = data.get("swot", {})
+    configs = [
+        ("Strengths",     "strengths",     "C6EFCE", GREEN_DARK),
+        ("Weaknesses",    "weaknesses",    "FFC7CE", RGBColor(0xC0,0x00,0x00)),
+        ("Opportunities", "opportunities", "DDEBF7", NAVY),
+        ("Threats",       "threats",       "FFEB9C", RGBColor(0x7F,0x60,0x00)),
+    ]
+    
+    parsed_any = any(len(swot.get(k, [])) > 0 for _, k, _, _ in configs)
+    
+    if not parsed_any and swot.get("raw"):
+        for line in swot["raw"].split("\n"):
+            if line.strip():
+                p = doc.add_paragraph()
+                r = p.add_run(line.strip())
+                r.font.size = Pt(11); r.font.name = "Calibri"
+        doc.add_paragraph()
+        return
+        
+    t = doc.add_table(rows=2, cols=2)
+    t.alignment = WD_TABLE_ALIGNMENT.LEFT
+    idx = 0
+    for row_i in range(2):
+        for col_i in range(2):
+            if idx >= len(configs): break
+            label, key, bg, color = configs[idx]; idx += 1
+            cell = t.cell(row_i, col_i)
+            cell.text = ""
+            p = cell.paragraphs[0]
+            r = p.add_run(label); r.font.bold = True; r.font.size = Pt(12)
+            r.font.color.rgb = color; r.font.name = "Calibri"
+            _shd(cell, bg)
+            for item in swot.get(key, []):
+                p2 = cell.add_paragraph(f"• {item}")
+                for run in p2.runs: run.font.size = Pt(10); run.font.name = "Calibri"
+    doc.add_paragraph()
+
+
+def _trends_section(doc, data):
+    _add_heading(doc, "Multi-Year Financial Trends", 1)
+    years = data.get("years", [])
+    currency = data.get("currency", "INR")
+    units_label = data.get("units_label", data.get("units", "Crores"))
+    trend = data.get("trend_analysis", {})
+
+    for section_title, prose_key, items, src_key, fmts in [
+        ("Revenue & Profitability", "revenue_and_profitability",
+         [("Revenue","revenue"),("Gross Profit","gross_profit"),("Operating Income","operating_income"),
+          ("Net Income","net_income")],
+         "income_statement", ["text"]+["money"]*len(years)),
+        ("Balance Sheet Strength", "balance_sheet_strength",
+         [("Total Assets","total_assets"),("Total Equity","total_equity"),
+          ("Long-term Debt","long_term_debt"),("Current Ratio","current_ratio")],
+         "balance_sheet", ["text"]+["money"]*len(years)),
+        ("Cash Flow Generation", "cash_flow_generation",
+         [("Cash from Operations","cash_from_operations"),("Capex","capex"),("Free Cash Flow","free_cash_flow")],
+         "cash_flow", ["text"]+["money"]*len(years)),
+    ]:
+        _add_heading(doc, section_title, 2)
+        _add_text(doc, trend.get(prose_key,""))
+        src_data = data.get(src_key, {})
+        if src_key == "balance_sheet":
+            merged = dict(data.get("balance_sheet",{})); merged.update(data.get("ratios",{}))
+            src_data = merged
+        rows = _gather(items, src_data, years)
+        _table(doc, [f"({units_label})"]+years, rows, fmts, currency, units_label)
+
+
+def _ratio_section(doc, data):
+    _add_heading(doc, "Financial Ratio Analysis", 1)
+    _add_text(doc, data.get("ratio_analysis_commentary",""))
+    years = data.get("years", [])
+    ratios = data.get("ratios", {})
+    currency = data.get("currency", "INR")
+    units_label = data.get("units_label", data.get("units", "Crores"))
+    for sec_name, items in [
+        ("Profitability", [("Gross Margin","gross_margin","pct"),("Operating Margin","operating_margin","pct"),
+                           ("Net Margin","net_margin","pct"),("ROE","roe","pct"),("ROIC","roic","pct")]),
+        ("Liquidity & Solvency", [("Current Ratio","current_ratio","ratio"),("Quick Ratio","quick_ratio","ratio"),
+                                   ("Debt-to-Equity","debt_to_equity","ratio"),("Net Debt/EBITDA","net_debt_to_ebitda","ratio")]),
+        ("Efficiency", [("Asset Turnover","asset_turnover","ratio"),("DSO (days)","days_sales_outstanding","days"),
+                        ("DPO (days)","days_payables","days"),("CCC (days)","cash_conversion_cycle","days")]),
+        ("Cash Flow Quality", [("OCF/Net Income","ocf_to_net_income","ratio"),("FCF Margin","fcf_margin","pct"),
+                                ("FCF Conversion","fcf_conversion","ratio")]),
+    ]:
+        _add_heading(doc, sec_name, 3)
+        rows = []
+        for label, key, kind in items:
+            vals = (ratios.get(key,[]) + [None]*len(years))[:len(years)]
+            rows.append([label] + list(vals))
+        kinds = ["text"] + [items[0][2]]*len(years)
+        _table(doc, ["Metric"]+years, rows, kinds, currency, units_label)
+
+
+def _dupont_section(doc, data):
+    _add_heading(doc, "DuPont Analysis", 1)
+    p = doc.add_paragraph()
+    r = p.add_run("ROE  =  Net Margin  \u00d7  Asset Turnover  \u00d7  Equity Multiplier")
+    r.font.size = Pt(11); r.font.bold = True; r.font.name = "Calibri"
+    years = data.get("years", [])
+    dp = data.get("dupont", {})
+    currency = data.get("currency", "INR")
+    rows = []
+    for label, key, kind in [
+        ("Net Margin","net_margin","pct"),("\u00d7 Asset Turnover","asset_turnover","ratio"),
+        ("\u00d7 Equity Multiplier","equity_multiplier","ratio"),("= ROE (DuPont)","roe_computed","pct"),
+        ("ROE (Reported)","roe_reported","pct"),
+    ]:
+        vals = (dp.get(key,[]) + [None]*len(years))[:len(years)]
+        rows.append([label]+list(vals))
+    _table(doc, ["Component"]+years, rows, ["text"]+["pct"]*len(years), currency)
+
+
+def _peer_section(doc, data):
+    _add_heading(doc, "Peer Benchmarking", 1)
+    _add_text(doc, data.get("peer_comparison_commentary",""))
+    peers = data.get("peers", [])
+    currency = data.get("currency", "INR")
+    units_label = data.get("units_label", "Crores")
+    co = data.get("company",{}); ratios = data.get("ratios",{})
+    inc = data.get("income_statement",{}); val = data.get("valuation_snapshot",{})
+    def last(arr):
+        for v in reversed(arr or []):
+            if v is not None: return v
+        return None
+    headers = ["Company","Revenue","Op Margin","Net Margin","ROIC","D/E","FCF Margin","P/E"]
+    fmts = ["text","money","pct","pct","pct","ratio","pct","ratio"]
+    rows = [[
+        co.get("name",co.get("ticker",""))+" (Target)",
+        last(inc.get("revenue",[])), last(ratios.get("operating_margin",[])),
+        last(ratios.get("net_margin",[])), last(ratios.get("roic",[])),
+        last(ratios.get("debt_to_equity",[])), last(ratios.get("fcf_margin",[])),
+        val.get("pe_ttm"),
+    ]]
+    if not peers:
+        doc.add_paragraph("Peer comparison data not available for this run.")
+    for p in peers:
+        rows.append([p.get("name",p.get("ticker","")), p.get("revenue"), p.get("operating_margin"),
+                     p.get("net_margin"), p.get("roic"), p.get("debt_to_equity"),
+                     p.get("fcf_margin"), p.get("pe_ttm")])
+    if rows:
+        _table(doc, headers, rows, fmts, currency, units_label)
+
+
+def _bullets(doc, items, heading=None, level=1):
+    if heading: _add_heading(doc, heading, level)
+    for item in items:
+        if isinstance(item, dict):
+            p = doc.add_paragraph(style="List Bullet")
+            r1 = p.add_run(f"{item.get('category','')}: "); r1.font.bold=True; r1.font.size=Pt(11); r1.font.name="Calibri"
+            r2 = p.add_run(item.get("description","")); r2.font.size=Pt(11); r2.font.name="Calibri"
+        else:
+            p = doc.add_paragraph(str(item), style="List Bullet")
+            for r in p.runs: r.font.size=Pt(11); r.font.name="Calibri"
+        p.paragraph_format.space_after = Pt(2)
+
+
+def _sources_section(doc, data):
+    _add_heading(doc, "Sources", 1)
+    for i, src in enumerate(data.get("sources",[]), 1):
+        p = doc.add_paragraph()
+        p.add_run(f"{i}. ").font.size = Pt(10)
+        url_r = p.add_run(src.get("url",""))
+        url_r.font.size=Pt(10); url_r.font.color.rgb=RGBColor(0x05,0x63,0xC1); url_r.font.name="Calibri"
+        suffix = ""
+        if src.get("description"): suffix += f" — {src['description']}"
+        if src.get("retrieved"):   suffix += f" (retrieved {src['retrieved']})"
+        if suffix:
+            sr = p.add_run(suffix); sr.font.size=Pt(10); sr.font.color.rgb=GRAY; sr.font.name="Calibri"
+
+
+def _disclaimer(doc):
+    p = doc.add_paragraph()
+    r = p.add_run(
+        "Disclaimer: This report is generated from public financial data and AI analysis for informational "
+        "purposes only. It is not investment advice. Verify all figures against primary sources before "
+        "making any financial decisions."
+    )
+    r.font.size=Pt(9); r.font.italic=True; r.font.color.rgb=GRAY; r.font.name="Calibri"
+    p.paragraph_format.space_before=Pt(12)
+
+
+# ── Orchestrator ──────────────────────────────────────────────────────────────
+
+def build_document(data: dict, output_path: str) -> None:
+    doc = Document()
+    for section in doc.sections:
+        section.left_margin = section.right_margin = Cm(2.0)
+        section.top_margin  = section.bottom_margin = Cm(2.0)
+
+    _cover(doc, data)
+
+    # Executive Summary
+    _add_heading(doc, "Executive Summary", 1)
+    _add_text(doc, data.get("executive_summary",""))
+
+    # Company & Industry Profile
+    _add_heading(doc, "Company Profile", 1)
+    _add_text(doc, data.get("company_overview",""))
+
+    _add_heading(doc, "Industry Profile", 1)
+    _add_text(doc, data.get("industry_profile",""))
+
+    _add_heading(doc, "Industry Trends", 1)
+    _add_text(doc, data.get("industry_trends",""))
+
+    _swot_section(doc, data)
+    _trends_section(doc, data)
+    
+    # Visualizations Section
+    try:
+        from builders.chart_builder import generate_all_charts
+        charts = generate_all_charts(data)
+        if any(charts.values()):
+            _add_heading(doc, "Financial Visualizations", 1)
+            if charts.get("revenue_net_income"):
+                _add_heading(doc, "Revenue & Net Income Trend", 2)
+                doc.add_picture(charts["revenue_net_income"], width=Inches(6.0))
+            if charts.get("margins"):
+                _add_heading(doc, "Profitability Margins", 2)
+                doc.add_picture(charts["margins"], width=Inches(6.0))
+            if charts.get("cash_flow"):
+                _add_heading(doc, "Cash Flow & CAPEX", 2)
+                doc.add_picture(charts["cash_flow"], width=Inches(6.0))
+    except Exception as e:
+        print(f"Warning: Could not generate DOCX charts: {e}", file=sys.stderr)
+
+    _ratio_section(doc, data)
+    _dupont_section(doc, data)
+    _peer_section(doc, data)
+
+    if data.get("key_insights"):
+        _bullets(doc, data["key_insights"], "Key Insights", 1)
+    if data.get("risks"):
+        _bullets(doc, data["risks"], "Risks", 1)
+    if data.get("things_to_watch"):
+        _bullets(doc, data["things_to_watch"], "Things to Watch", 1)
+    if data.get("limitations_and_caveats"):
+        _bullets(doc, data["limitations_and_caveats"], "Limitations & Caveats", 1)
+
+    _sources_section(doc, data)
+    _disclaimer(doc)
+
+    Path(output_path).parent.mkdir(parents=True, exist_ok=True)
+    doc.save(output_path)
+
+
+if __name__ == "__main__":
+    import argparse
+    p = argparse.ArgumentParser()
+    p.add_argument("--input","-i",required=True)
+    p.add_argument("--output","-o",required=True)
+    args = p.parse_args()
+    with open(args.input, encoding="utf-8") as f:
+        data = json.load(f)
+    build_document(data, args.output)
+    print(f"Wrote: {args.output}")

builders/build_excel.py

@@ -1,587 +1,587 @@
-#!/usr/bin/env python3
-"""
-builders/build_excel.py — Enhanced Excel workbook builder.
-Reads the skill's original builder and adds DuPont, SWOT, and Indian unit support.
-"""
-import json, sys, datetime
-from pathlib import Path
-
-try:
-    from openpyxl import Workbook
-    from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
-    from openpyxl.utils import get_column_letter
-    from openpyxl.chart import BarChart, LineChart, Reference
-except ImportError:
-    print("ERROR: pip install openpyxl", file=sys.stderr); sys.exit(1)
-
-# ── Style constants ───────────────────────────────────────────────────────────
-HDR_FILL  = PatternFill("solid", fgColor="1F4E78")
-HDR_FONT  = Font(name="Calibri", size=11, bold=True, color="FFFFFF")
-SEC_FILL  = PatternFill("solid", fgColor="D9E1F2")
-SEC_FONT  = Font(name="Calibri", size=11, bold=True, color="1F4E78")
-LBL_FONT  = Font(name="Calibri", size=11)
-TTL_FONT  = Font(name="Calibri", size=16, bold=True, color="1F4E78")
-DAT_FONT  = Font(name="Calibri", size=11)
-GRN_FILL  = PatternFill("solid", fgColor="C6EFCE")
-RED_FILL  = PatternFill("solid", fgColor="FFC7CE")
-YLW_FILL  = PatternFill("solid", fgColor="FFEB9C")
-
-THIN = Border(
-    left=Side(style="thin", color="BFBFBF"), right=Side(style="thin", color="BFBFBF"),
-    top=Side(style="thin",  color="BFBFBF"), bottom=Side(style="thin", color="BFBFBF"),
-)
-CENTER = Alignment(horizontal="center", vertical="center", wrap_text=True)
-LEFT   = Alignment(horizontal="left",   vertical="center", wrap_text=True)
-RIGHT  = Alignment(horizontal="right",  vertical="center")
-
-FMT_MONEY = '#,##0;(#,##0);"—"'
-FMT_DEC   = '#,##0.00;(#,##0.00);"—"'
-FMT_PCT   = '0.0%;(0.0%);"—"'
-FMT_RATIO = '0.00;(0.00);"—"'
-FMT_DAYS  = '0.0;"—"'
-
-# ── Helpers ───────────────────────────────────────────────────────────────────
-
-def _safe(arr, i, default=None):
-    try: return arr[i] if arr and arr[i] is not None else default
-    except: return default
-
-def _hdr(ws, row, cols, start=1):
-    for i, h in enumerate(cols):
-        c = ws.cell(row=row, column=start+i, value=h)
-        c.font, c.fill, c.alignment, c.border = HDR_FONT, HDR_FILL, CENTER, THIN
-
-def _sec(ws, row, label, ncols, start=1):
-    ws.cell(row=row, column=start, value=label).font = SEC_FONT
-    ws.cell(row=row, column=start).fill = SEC_FILL
-    ws.cell(row=row, column=start).alignment = LEFT
-    for col in range(start+1, start+ncols):
-        ws.cell(row=row, column=col).fill = SEC_FILL
-
-def _data_row(ws, row, label, values, fmt=FMT_MONEY, start=1):
-    c = ws.cell(row=row, column=start, value=label)
-    c.font, c.alignment, c.border = LBL_FONT, LEFT, THIN
-    for i, v in enumerate(values):
-        cell = ws.cell(row=row, column=start+1+i, value=v if v is not None else "—")
-        cell.font, cell.alignment, cell.border = DAT_FONT, RIGHT, THIN
-        if v is not None and isinstance(v, (int, float)):
-            cell.number_format = fmt
-
-def _title(ws, title, subtitle=None):
-    ws.cell(row=1, column=1, value=title).font = TTL_FONT
-    if subtitle:
-        ws.cell(row=2, column=1, value=subtitle).font = Font(name="Calibri", size=11, italic=True, color="595959")
-
-def _widths(ws, widths):
-    for i, w in enumerate(widths):
-        ws.column_dimensions[get_column_letter(i+1)].width = w
-
-def _freeze(ws, hdr_row):
-    ws.freeze_panes = ws.cell(row=hdr_row+1, column=2)
-
-def _last(arr):
-    if not arr: return None
-    for v in reversed(arr):
-        if v is not None: return v
-    return None
-
-# ── Statement section helper ──────────────────────────────────────────────────
-
-def _stmt_section(ws, row, title, items, data_dict, years, fmt=FMT_MONEY):
-    _sec(ws, row, title, 1+len(years))
-    row += 1
-    for label, key in items:
-        vals = data_dict.get(key, [None]*len(years))
-        vals = (vals + [None]*len(years))[:len(years)]
-        _data_row(ws, row, label, vals, fmt)
-        row += 1
-    return row
-
-# ── Sheet builders ────────────────────────────────────────────────────────────
-
-def _summary_sheet(wb, data):
-    ws = wb.create_sheet("Summary")
-    co = data.get("company", {})
-    units_label = data.get("units_label", data.get("units", "Crores"))
-    currency    = data.get("currency", co.get("currency", "INR"))
-    _title(ws, f"{co.get('name','')} ({co.get('ticker','')}) — Financial Analysis",
-           f"Report date: {co.get('report_date', datetime.date.today().isoformat())}")
-    r = 4
-    for label, val in [
-        ("Company",         co.get("name","")),
-        ("Ticker",          co.get("ticker","")),
-        ("Exchange",        co.get("exchange","")),
-        ("Industry",        co.get("industry","")),
-        ("Sector",          co.get("sector","")),
-        ("Currency",        currency),
-        ("Reporting Units", units_label),
-        ("Fiscal Year End", co.get("fiscal_year_end","")),
-        ("Analysis Period", f"{len(data.get('years',[]))} years ({', '.join(data.get('years',[]))})"),
-        ("Share Price",     co.get("share_price")),
-        ("Market Cap",      co.get("market_cap")),
-        ("Data Source",     data.get("source", "Yahoo Finance (yfinance)")),
-        ("Models Used",     ", ".join(data.get("models_used", []) or ["None"])),
-        ("Warnings",        " | ".join(data.get("warnings", []))),
-    ]:
-        ws.cell(row=r, column=1, value=label).font = LBL_FONT
-        ws.cell(row=r, column=2, value=val).font   = DAT_FONT
-        r += 1
-    _widths(ws, [28, 50])
-
-
-def _income_sheet(wb, data):
-    ws = wb.create_sheet("Income Statement")
-    co, years = data.get("company",{}), data.get("years",[])
-    units_label = data.get("units_label", data.get("units", "Crores"))
-    currency    = data.get("currency", co.get("currency", "INR"))
-    _title(ws, f"Income Statement — {co.get('ticker','')}",
-           f"All figures in {units_label}")
-    _hdr(ws, 4, [f"({units_label})"] + years)
-    inc = data.get("income_statement", {})
-    r = _stmt_section(ws, 5, "Revenue & Profitability", [
-        ("Revenue",              "revenue"),
-        ("Cost of Revenue",      "cost_of_revenue"),
-        ("Gross Profit",         "gross_profit"),
-        ("Operating Expenses",   "operating_expenses"),
-        ("Operating Income (EBIT)","operating_income"),
-        ("D&A",                  "depreciation_amort"),
-        ("EBITDA",               "ebitda"),
-        ("Interest Expense",     "interest_expense"),
-        ("Pretax Income",        "pretax_income"),
-        ("Tax Expense",          "tax_expense"),
-        ("Net Income",           "net_income"),
-    ], inc, years)
-    r += 1
-    _sec(ws, r, "Per-Share Data", 1+len(years)); r += 1
-    _data_row(ws, r, "Diluted EPS",    inc.get("diluted_eps",[None]*len(years))[:len(years)], FMT_DEC); r += 1
-    _data_row(ws, r, "Diluted Shares (M)", inc.get("diluted_shares",[None]*len(years))[:len(years)], FMT_MONEY)
-
-    # Add revenue bar chart
-    if len(years) >= 2:
-        chart = BarChart()
-        chart.title = f"Revenue Trend ({units_label})"
-        chart.style = 10
-        chart.y_axis.title = units_label
-        rev_row = 6
-        data_ref = Reference(ws, min_col=2, max_col=1+len(years), min_row=rev_row, max_row=rev_row)
-        cats_ref = Reference(ws, min_col=2, max_col=1+len(years), min_row=4, max_row=4)
-        chart.add_data(data_ref)
-        chart.set_categories(cats_ref)
-        chart.shape = 4
-        ws.add_chart(chart, f"A{r+3}")
-
-    _widths(ws, [34]+[16]*len(years)); _freeze(ws, 4)
-
-
-def _balance_sheet(wb, data):
-    ws = wb.create_sheet("Balance Sheet")
-    co, years = data.get("company",{}), data.get("years",[])
-    units_label = data.get("units_label", data.get("units", "Crores"))
-    _title(ws, f"Balance Sheet — {co.get('ticker','')}",
-           f"As of fiscal year-end. {units_label}")
-    _hdr(ws, 4, [f"({units_label})"] + years)
-    bs = data.get("balance_sheet", {})
-    r = 5
-    for sec_title, items in [
-        ("Current Assets", [
-            ("Cash & Equivalents","cash_and_equivalents"),("Short-term Investments","short_term_investments"),
-            ("Receivables","receivables"),("Inventory","inventory"),("Other Current Assets","other_current_assets"),
-            ("Total Current Assets","total_current_assets"),
-        ]),
-        ("Non-Current Assets", [
-            ("PP&E (Net)","ppe_net"),("Goodwill","goodwill"),("Intangibles","intangibles"),
-            ("Other LT Assets","other_long_term_assets"),("Total Assets","total_assets"),
-        ]),
-        ("Current Liabilities", [
-            ("Accounts Payable","accounts_payable"),("Short-term Debt","short_term_debt"),
-            ("Other Current Liabilities","other_current_liabilities"),
-            ("Total Current Liabilities","total_current_liabilities"),
-        ]),
-        ("Non-Current Liabilities & Equity", [
-            ("Long-term Debt","long_term_debt"),("Other LT Liabilities","other_long_term_liabilities"),
-            ("Total Liabilities","total_liabilities"),("Total Equity","total_equity"),
-            ("Total Liabilities + Equity","total_liab_and_equity"),
-        ]),
-    ]:
-        r = _stmt_section(ws, r, sec_title, items, bs, years)
-    _widths(ws, [36]+[16]*len(years)); _freeze(ws, 4)
-
-
-def _cashflow_sheet(wb, data):
-    ws = wb.create_sheet("Cash Flow")
-    co, years = data.get("company",{}), data.get("years",[])
-    units_label = data.get("units_label", data.get("units", "Crores"))
-    _title(ws, f"Cash Flow Statement — {co.get('ticker','')}",
-           f"All figures in {units_label}")
-    _hdr(ws, 4, [f"({units_label})"] + years)
-    cf = data.get("cash_flow", {})
-    r = 5
-    for sec_title, items in [
-        ("Operating Activities", [
-            ("Net Income","net_income"),("D&A","depreciation_amort"),("Stock-Based Comp","stock_based_comp"),
-            ("Working Capital Changes","working_capital_changes"),("Cash from Operations","cash_from_operations"),
-        ]),
-        ("Investing Activities", [
-            ("Capital Expenditures","capex"),("Acquisitions","acquisitions"),("Cash from Investing","cash_from_investing"),
-        ]),
-        ("Financing Activities", [
-            ("Debt Issuance/(Repayment)","debt_issuance_repayment"),("Buybacks","stock_buybacks"),
-            ("Dividends Paid","dividends_paid"),("Cash from Financing","cash_from_financing"),
-        ]),
-        ("Summary", [
-            ("Net Change in Cash","net_change_in_cash"),("Free Cash Flow","free_cash_flow"),
-        ]),
-    ]:
-        r = _stmt_section(ws, r, sec_title, items, cf, years)
-    _widths(ws, [36]+[16]*len(years)); _freeze(ws, 4)
-
-
-RATIO_SECTIONS = [
-    ("Profitability", [
-        ("gross_margin","Gross Margin",FMT_PCT),("operating_margin","Operating Margin",FMT_PCT),
-        ("ebitda_margin","EBITDA Margin",FMT_PCT),("net_margin","Net Margin",FMT_PCT),
-        ("roe","ROE",FMT_PCT),("roa","ROA",FMT_PCT),("roic","ROIC",FMT_PCT),
-    ]),
-    ("Liquidity", [
-        ("current_ratio","Current Ratio",FMT_RATIO),("quick_ratio","Quick Ratio",FMT_RATIO),
-        ("cash_ratio","Cash Ratio",FMT_RATIO),
-    ]),
-    ("Solvency / Leverage", [
-        ("debt_to_equity","Debt-to-Equity",FMT_RATIO),("debt_to_assets","Debt-to-Assets",FMT_RATIO),
-        ("net_debt_to_ebitda","Net Debt/EBITDA",FMT_RATIO),("interest_coverage","Interest Coverage",FMT_RATIO),
-    ]),
-    ("Efficiency", [
-        ("asset_turnover","Asset Turnover",FMT_RATIO),("inventory_turnover","Inventory Turnover",FMT_RATIO),
-        ("days_inventory","Days Inventory (DIO)",FMT_DAYS),("days_sales_outstanding","DSO",FMT_DAYS),
-        ("days_payables","DPO",FMT_DAYS),("cash_conversion_cycle","Cash Conversion Cycle",FMT_DAYS),
-    ]),
-    ("Cash Flow Quality", [
-        ("ocf_to_net_income","OCF/Net Income",FMT_RATIO),("fcf_margin","FCF Margin",FMT_PCT),
-        ("fcf_conversion","FCF Conversion",FMT_RATIO),("capex_intensity","Capex/Revenue",FMT_PCT),
-    ]),
-]
-
-
-def _ratios_sheet(wb, data):
-    ws = wb.create_sheet("Ratios")
-    years = data.get("years", [])
-    _title(ws, f"Financial Ratios — {data.get('company',{}).get('ticker','')}", "5-year trend")
-    _hdr(ws, 4, ["Ratio"] + years)
-    ratios = data.get("ratios", {})
-    r = 5
-    for sec_name, items in RATIO_SECTIONS:
-        _sec(ws, r, sec_name, 1+len(years)); r += 1
-        for key, label, fmt in items:
-            vals = ratios.get(key, [None]*len(years))
-            vals = (vals + [None]*len(years))[:len(years)]
-            _data_row(ws, r, label, vals, fmt); r += 1
-    _widths(ws, [40]+[14]*len(years)); _freeze(ws, 4)
-
-
-def _dupont_sheet(wb, data):
-    ws = wb.create_sheet("DuPont Analysis")
-    years = data.get("years", [])
-    _title(ws, "DuPont Decomposition", "ROE = Net Margin × Asset Turnover × Equity Multiplier")
-    _hdr(ws, 4, ["Component"] + years)
-    dp = data.get("dupont", {})
-    r = 5
-    for label, key, fmt in [
-        ("Net Margin",       "net_margin",        FMT_PCT),
-        ("× Asset Turnover", "asset_turnover",     FMT_RATIO),
-        ("× Equity Multiplier","equity_multiplier",FMT_RATIO),
-        ("= ROE (DuPont)",   "roe_computed",       FMT_PCT),
-        ("ROE (Reported)",   "roe_reported",       FMT_PCT),
-    ]:
-        vals = dp.get(key, [None]*len(years))
-        vals = (vals + [None]*len(years))[:len(years)]
-        _data_row(ws, r, label, vals, fmt)
-        r += 1
-    _widths(ws, [28]+[14]*len(years))
-
-
-def _growth_sheet(wb, data):
-    ws = wb.create_sheet("Growth Metrics")
-    years = data.get("years", [])
-    yoy_hdrs = [f"{years[i]} vs {years[i-1]}" for i in range(1, len(years))] if len(years) > 1 else []
-    _title(ws, "Growth Metrics", "YoY growth and CAGRs")
-    _hdr(ws, 4, ["Metric"] + yoy_hdrs + ["CAGR"])
-    g = data.get("growth_metrics", {})
-    r = 5
-    for label, yoy_key, cagr_key in [
-        ("Revenue",        "revenue_yoy",        "revenue_cagr_5y"),
-        ("Operating Income","operating_income_yoy","operating_income_cagr_5y"),
-        ("Net Income",     "net_income_yoy",      "net_income_cagr_5y"),
-        ("Diluted EPS",    "eps_yoy",             "eps_cagr_5y"),
-        ("Free Cash Flow", "fcf_yoy",             "fcf_cagr_5y"),
-    ]:
-        yoy = g.get(yoy_key, [])[1:]  # skip first None
-        cagr = g.get(cagr_key)
-        vals = (yoy + [None]*len(yoy_hdrs))[:len(yoy_hdrs)] + [cagr]
-        _data_row(ws, r, label, vals, FMT_PCT); r += 1
-    _widths(ws, [28]+[16]*(len(yoy_hdrs)+1))
-
-
-def _peers_sheet(wb, data):
-    ws = wb.create_sheet("Peer Comparison")
-    co = data.get("company", {})
-    _title(ws, f"Peer Comparison — {co.get('ticker','')}", "Latest fiscal year")
-    cols = ["Company","Ticker","Revenue","Gross Margin","Op Margin","Net Margin",
-            "ROE","ROIC","D/E","Current Ratio","FCF Margin","P/E","EV/EBITDA","Rationale"]
-    _hdr(ws, 4, cols)
-    r = 5
-    years = data.get("years", [])
-    rat = data.get("ratios", {}); inc = data.get("income_statement", {})
-    val = data.get("valuation_snapshot", {})
-    # Target row
-    target = [
-        co.get("name",""), co.get("ticker",""),
-        _last(inc.get("revenue",[])), _last(rat.get("gross_margin",[])),
-        _last(rat.get("operating_margin",[])), _last(rat.get("net_margin",[])),
-        _last(rat.get("roe",[])), _last(rat.get("roic",[])),
-        _last(rat.get("debt_to_equity",[])), _last(rat.get("current_ratio",[])),
-        _last(rat.get("fcf_margin",[])), val.get("pe_ttm"), val.get("ev_ebitda"),
-        "Target company",
-    ]
-    fmts = ["","","money","pct","pct","pct","pct","pct","ratio","ratio","pct","ratio","ratio",""]
-    _write_peer_row(ws, r, target, fmts, is_target=True); r += 1
-    for p in data.get("peers", []):
-        peer_row = [
-            p.get("name",""), p.get("ticker",""), p.get("revenue"),
-            p.get("gross_margin"), p.get("operating_margin"), p.get("net_margin"),
-            p.get("roe"), p.get("roic"), p.get("debt_to_equity"), p.get("current_ratio"),
-            p.get("fcf_margin"), p.get("pe_ttm"), p.get("ev_ebitda"), p.get("rationale",""),
-        ]
-        _write_peer_row(ws, r, peer_row, fmts); r += 1
-    _widths(ws, [24,10,16,12,12,12,10,10,10,12,12,10,12,50])
-
-
-def _write_peer_row(ws, row, values, fmts, is_target=False):
-    fmt_map = {"money":FMT_MONEY,"pct":FMT_PCT,"ratio":FMT_RATIO}
-    fill = PatternFill("solid", fgColor="FFF2CC") if is_target else None
-    for i, v in enumerate(values):
-        c = ws.cell(row=row, column=i+1, value=v if v is not None else "—")
-        c.border = THIN
-        c.font = Font(name="Calibri", size=11, bold=is_target)
-        if fill: c.fill = fill
-        fmt = fmts[i] if i < len(fmts) else ""
-        if fmt in fmt_map and isinstance(v, (int, float)):
-            c.number_format = fmt_map[fmt]
-            c.alignment = RIGHT
-        else:
-            c.alignment = LEFT
-
-
-def _swot_sheet(wb, data):
-    ws = wb.create_sheet("SWOT Analysis")
-    _title(ws, "SWOT Analysis", "Strengths, Weaknesses, Opportunities, Threats")
-    swot = data.get("swot", {})
-    r = 4
-    colors = {"Strengths":"C6EFCE","Weaknesses":"FFC7CE","Opportunities":"DDEBF7","Threats":"FFEB9C"}
-    for category, key in [("Strengths","strengths"),("Weaknesses","weaknesses"),
-                           ("Opportunities","opportunities"),("Threats","threats")]:
-        fill = PatternFill("solid", fgColor=colors[category])
-        hdr_cell = ws.cell(row=r, column=1, value=category)
-        hdr_cell.font = Font(name="Calibri", size=12, bold=True)
-        hdr_cell.fill = fill
-        r += 1
-        for item in swot.get(key, []):
-            c = ws.cell(row=r, column=1, value=f"• {item}")
-            c.font = Font(name="Calibri", size=11)
-            c.alignment = Alignment(horizontal="left", vertical="top", wrap_text=True)
-            ws.row_dimensions[r].height = 32
-            r += 1
-        r += 1
-    _widths(ws, [90])
-
-
-def _dcf_sheet(wb, data):
-    ws = wb.create_sheet("DCF Valuation")
-    _title(ws, "Discounted Cash Flow (DCF) Model", "Interactive Valuation Model")
-    
-    co = data.get("company", {})
-    cf = data.get("cash_flow", {})
-    inc = data.get("income_statement", {})
-    bal = data.get("balance_sheet", {})
-    
-    # Get latest data
-    fcf_history = [v for v in cf.get("free_cash_flow", []) if v is not None]
-    latest_fcf = fcf_history[-1] if fcf_history else 0
-    
-    shares_history = [v for v in inc.get("diluted_shares", []) if v is not None]
-    latest_shares = shares_history[-1] if shares_history else 1
-    
-    cash_history = [v for v in bal.get("cash_and_equivalents", []) if v is not None]
-    cash = cash_history[-1] if cash_history else 0
-    
-    debt_history = [v for v in bal.get("total_liabilities", []) if v is not None]
-    debt = debt_history[-1] if debt_history else 0
-    
-    # Inputs
-    val_snap = data.get("valuation_snapshot", {})
-    wacc_est = val_snap.get("wacc_estimate", 0.10)
-    target_price = val_snap.get("target_mean_price", "N/A")
-    
-    r = 4
-    _sec(ws, r, "Model Inputs (Editable - Yellow Cells)", 2); r += 1
-    
-    ws.cell(row=r, column=1, value="Starting FCF (Base)").font = LBL_FONT
-    ws.cell(row=r, column=2, value=latest_fcf).font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; ws.cell(row=r, column=2).fill = PatternFill("solid", fgColor="FFFFE0"); r += 1
-    
-    ws.cell(row=r, column=1, value="WACC (Discount Rate)").font = LBL_FONT
-    ws.cell(row=r, column=2, value=wacc_est).font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_PCT; ws.cell(row=r, column=2).fill = PatternFill("solid", fgColor="FFFFE0"); r += 1
-    
-    ws.cell(row=r, column=1, value="Terminal Growth Rate").font = LBL_FONT
-    ws.cell(row=r, column=2, value=0.025).font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_PCT; ws.cell(row=r, column=2).fill = PatternFill("solid", fgColor="FFFFE0"); r += 1
-    
-    ws.cell(row=r, column=1, value="Cash & Equivalents").font = LBL_FONT
-    ws.cell(row=r, column=2, value=cash).font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; ws.cell(row=r, column=2).fill = PatternFill("solid", fgColor="FFFFE0"); r += 1
-    
-    ws.cell(row=r, column=1, value="Total Debt").font = LBL_FONT
-    ws.cell(row=r, column=2, value=debt).font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; ws.cell(row=r, column=2).fill = PatternFill("solid", fgColor="FFFFE0"); r += 1
-    
-    ws.cell(row=r, column=1, value="Diluted Shares").font = LBL_FONT
-    ws.cell(row=r, column=2, value=latest_shares).font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; ws.cell(row=r, column=2).fill = PatternFill("solid", fgColor="FFFFE0"); r += 1
-    
-    r += 1
-    _sec(ws, r, "Projected Cash Flows (5 Years)", 6); r += 1
-    _hdr(ws, r, ["Year 1", "Year 2", "Year 3", "Year 4", "Year 5"], start=2); r += 1
-    
-    # Growth rates
-    ws.cell(row=r, column=1, value="Projected Growth Rate").font = LBL_FONT
-    for c in range(2, 7):
-        ws.cell(row=r, column=c, value=0.05).font = DAT_FONT; ws.cell(row=r, column=c).number_format = FMT_PCT; ws.cell(row=r, column=c).fill = PatternFill("solid", fgColor="FFFFE0")
-    r += 1
-    
-    # FCF Projection Formulas
-    ws.cell(row=r, column=1, value="Projected FCF").font = LBL_FONT
-    ws.cell(row=r, column=2, value="=B5*(1+B14)") # Year 1 = Base * (1+g1)
-    ws.cell(row=r, column=3, value="=B15*(1+C14)")
-    ws.cell(row=r, column=4, value="=C15*(1+D14)")
-    ws.cell(row=r, column=5, value="=D15*(1+E14)")
-    ws.cell(row=r, column=6, value="=E15*(1+F14)")
-    for c in range(2, 7):
-        ws.cell(row=r, column=c).font = DAT_FONT; ws.cell(row=r, column=c).number_format = FMT_MONEY
-    r += 1
-    
-    # Discount Factor Formulas
-    ws.cell(row=r, column=1, value="Discount Factor").font = LBL_FONT
-    ws.cell(row=r, column=2, value="=1/((1+$B$6)^1)")
-    ws.cell(row=r, column=3, value="=1/((1+$B$6)^2)")
-    ws.cell(row=r, column=4, value="=1/((1+$B$6)^3)")
-    ws.cell(row=r, column=5, value="=1/((1+$B$6)^4)")
-    ws.cell(row=r, column=6, value="=1/((1+$B$6)^5)")
-    for c in range(2, 7):
-        ws.cell(row=r, column=c).font = DAT_FONT; ws.cell(row=r, column=c).number_format = FMT_RATIO
-    r += 1
-    
-    # PV of FCF Formulas
-    ws.cell(row=r, column=1, value="PV of FCF").font = LBL_FONT
-    for c, col_l in enumerate(["B", "C", "D", "E", "F"], start=2):
-        ws.cell(row=r, column=c, value=f"={col_l}15*{col_l}16").font = DAT_FONT; ws.cell(row=r, column=c).number_format = FMT_MONEY
-    r += 2
-    
-    # Valuation Output
-    _sec(ws, r, "Valuation Output", 2); r += 1
-    
-    ws.cell(row=r, column=1, value="Sum of PV of FCF").font = LBL_FONT
-    ws.cell(row=r, column=2, value="=SUM(B17:F17)").font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; r += 1
-    
-    ws.cell(row=r, column=1, value="Terminal Value").font = LBL_FONT
-    ws.cell(row=r, column=2, value="=(F15*(1+B7))/(B6-B7)").font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; r += 1
-    
-    ws.cell(row=r, column=1, value="PV of Terminal Value").font = LBL_FONT
-    ws.cell(row=r, column=2, value="=B21*F16").font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; r += 1
-    
-    ws.cell(row=r, column=1, value="Implied Enterprise Value").font = LBL_FONT
-    ws.cell(row=r, column=2, value="=B20+B22").font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; r += 1
-    
-    ws.cell(row=r, column=1, value="Implied Equity Value").font = LBL_FONT
-    ws.cell(row=r, column=2, value="=B23+B8-B9").font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; r += 1
-    
-    ws.cell(row=r, column=1, value="Implied Share Price").font = Font(name="Calibri", size=12, bold=True)
-    ws.cell(row=r, column=2, value="=B24/B10").font = Font(name="Calibri", size=12, bold=True); ws.cell(row=r, column=2).number_format = FMT_MONEY; r += 1
-    
-    ws.cell(row=r, column=1, value="Analyst Mean Target Price").font = LBL_FONT
-    ws.cell(row=r, column=2, value=target_price).font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; ws.cell(row=r, column=2).fill = PatternFill("solid", fgColor="DDEBF7"); r += 1
-    
-    _widths(ws, [30, 20, 20, 20, 20, 20])
-
-
-def _narrative_sheet(wb, data):
-    ws = wb.create_sheet("Industry & AI Insights")
-    _title(ws, "AI Narrative Analysis", "Generated by AI Models")
-    r = 4
-    
-    exec_summary = data.get("executive_summary", "")
-    if exec_summary:
-        _sec(ws, r, "Executive Summary", 2); r += 1
-        c = ws.cell(row=r, column=1, value=exec_summary)
-        c.font = LBL_FONT; c.alignment = Alignment(wrap_text=True, vertical="top")
-        ws.merge_cells(start_row=r, start_column=1, end_row=r, end_column=4)
-        ws.row_dimensions[r].height = 100
-        r += 2
-        
-    industry_trends = data.get("industry_trends", "")
-    if industry_trends:
-        _sec(ws, r, "Industry Trends", 2); r += 1
-        c = ws.cell(row=r, column=1, value=industry_trends)
-        c.font = LBL_FONT; c.alignment = Alignment(wrap_text=True, vertical="top")
-        ws.merge_cells(start_row=r, start_column=1, end_row=r, end_column=4)
-        ws.row_dimensions[r].height = 120
-        r += 2
-        
-    _widths(ws, [100])
-
-
-def _sources_sheet(wb, data):
-    ws = wb.create_sheet("Sources")
-    _title(ws, "Sources", "All data sources and retrieval dates")
-    _hdr(ws, 4, ["#","URL","Description","Retrieved"])
-    r = 5
-    for i, src in enumerate(data.get("sources",[]), start=1):
-        ws.cell(row=r, column=1, value=i).alignment = CENTER
-        url = src.get("url","")
-        uc = ws.cell(row=r, column=2, value=url)
-        uc.font = Font(name="Calibri", size=10, color="0563C1", underline="single")
-        uc.alignment = LEFT
-        if url:
-            try: uc.hyperlink = url
-            except: pass
-        ws.cell(row=r, column=3, value=src.get("description","")).alignment = LEFT
-        ws.cell(row=r, column=4, value=src.get("retrieved","")).alignment = CENTER
-        for col in range(1,5): ws.cell(row=r,column=col).border = THIN
-        ws.row_dimensions[r].height = 28; r += 1
-    _widths(ws, [6,70,50,16])
-
-
-# ── Orchestrator ──────────────────────────────────────────────────────────────
-
-def build_workbook(data: dict, output_path: str) -> None:
-    wb = Workbook()
-    wb.remove(wb.active)
-    _summary_sheet(wb, data)
-    _income_sheet(wb, data)
-    _balance_sheet(wb, data)
-    _cashflow_sheet(wb, data)
-    _ratios_sheet(wb, data)
-    _dupont_sheet(wb, data)
-    _dcf_sheet(wb, data)
-    _growth_sheet(wb, data)
-    _peers_sheet(wb, data)
-    _swot_sheet(wb, data)
-    _narrative_sheet(wb, data)
-    _sources_sheet(wb, data)
-    wb.save(output_path)
-
-
-if __name__ == "__main__":
-    import argparse
-    p = argparse.ArgumentParser()
-    p.add_argument("--input", "-i", required=True)
-    p.add_argument("--output", "-o", required=True)
-    args = p.parse_args()
-    with open(args.input, encoding="utf-8") as f:
-        data = json.load(f)
-    Path(args.output).parent.mkdir(parents=True, exist_ok=True)
-    build_workbook(data, args.output)
-    print(f"Wrote: {args.output}")
+#!/usr/bin/env python3
+"""
+builders/build_excel.py — Enhanced Excel workbook builder.
+Reads the skill's original builder and adds DuPont, SWOT, and Indian unit support.
+"""
+import json, sys, datetime
+from pathlib import Path
+
+try:
+    from openpyxl import Workbook
+    from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
+    from openpyxl.utils import get_column_letter
+    from openpyxl.chart import BarChart, LineChart, Reference
+except ImportError:
+    print("ERROR: pip install openpyxl", file=sys.stderr); sys.exit(1)
+
+# ── Style constants ───────────────────────────────────────────────────────────
+HDR_FILL  = PatternFill("solid", fgColor="1F4E78")
+HDR_FONT  = Font(name="Calibri", size=11, bold=True, color="FFFFFF")
+SEC_FILL  = PatternFill("solid", fgColor="D9E1F2")
+SEC_FONT  = Font(name="Calibri", size=11, bold=True, color="1F4E78")
+LBL_FONT  = Font(name="Calibri", size=11)
+TTL_FONT  = Font(name="Calibri", size=16, bold=True, color="1F4E78")
+DAT_FONT  = Font(name="Calibri", size=11)
+GRN_FILL  = PatternFill("solid", fgColor="C6EFCE")
+RED_FILL  = PatternFill("solid", fgColor="FFC7CE")
+YLW_FILL  = PatternFill("solid", fgColor="FFEB9C")
+
+THIN = Border(
+    left=Side(style="thin", color="BFBFBF"), right=Side(style="thin", color="BFBFBF"),
+    top=Side(style="thin",  color="BFBFBF"), bottom=Side(style="thin", color="BFBFBF"),
+)
+CENTER = Alignment(horizontal="center", vertical="center", wrap_text=True)
+LEFT   = Alignment(horizontal="left",   vertical="center", wrap_text=True)
+RIGHT  = Alignment(horizontal="right",  vertical="center")
+
+FMT_MONEY = '#,##0;(#,##0);"—"'
+FMT_DEC   = '#,##0.00;(#,##0.00);"—"'
+FMT_PCT   = '0.0%;(0.0%);"—"'
+FMT_RATIO = '0.00;(0.00);"—"'
+FMT_DAYS  = '0.0;"—"'
+
+# ── Helpers ───────────────────────────────────────────────────────────────────
+
+def _safe(arr, i, default=None):
+    try: return arr[i] if arr and arr[i] is not None else default
+    except: return default
+
+def _hdr(ws, row, cols, start=1):
+    for i, h in enumerate(cols):
+        c = ws.cell(row=row, column=start+i, value=h)
+        c.font, c.fill, c.alignment, c.border = HDR_FONT, HDR_FILL, CENTER, THIN
+
+def _sec(ws, row, label, ncols, start=1):
+    ws.cell(row=row, column=start, value=label).font = SEC_FONT
+    ws.cell(row=row, column=start).fill = SEC_FILL
+    ws.cell(row=row, column=start).alignment = LEFT
+    for col in range(start+1, start+ncols):
+        ws.cell(row=row, column=col).fill = SEC_FILL
+
+def _data_row(ws, row, label, values, fmt=FMT_MONEY, start=1):
+    c = ws.cell(row=row, column=start, value=label)
+    c.font, c.alignment, c.border = LBL_FONT, LEFT, THIN
+    for i, v in enumerate(values):
+        cell = ws.cell(row=row, column=start+1+i, value=v if v is not None else "—")
+        cell.font, cell.alignment, cell.border = DAT_FONT, RIGHT, THIN
+        if v is not None and isinstance(v, (int, float)):
+            cell.number_format = fmt
+
+def _title(ws, title, subtitle=None):
+    ws.cell(row=1, column=1, value=title).font = TTL_FONT
+    if subtitle:
+        ws.cell(row=2, column=1, value=subtitle).font = Font(name="Calibri", size=11, italic=True, color="595959")
+
+def _widths(ws, widths):
+    for i, w in enumerate(widths):
+        ws.column_dimensions[get_column_letter(i+1)].width = w
+
+def _freeze(ws, hdr_row):
+    ws.freeze_panes = ws.cell(row=hdr_row+1, column=2)
+
+def _last(arr):
+    if not arr: return None
+    for v in reversed(arr):
+        if v is not None: return v
+    return None
+
+# ── Statement section helper ──────────────────────────────────────────────────
+
+def _stmt_section(ws, row, title, items, data_dict, years, fmt=FMT_MONEY):
+    _sec(ws, row, title, 1+len(years))
+    row += 1
+    for label, key in items:
+        vals = data_dict.get(key, [None]*len(years))
+        vals = (vals + [None]*len(years))[:len(years)]
+        _data_row(ws, row, label, vals, fmt)
+        row += 1
+    return row
+
+# ── Sheet builders ────────────────────────────────────────────────────────────
+
+def _summary_sheet(wb, data):
+    ws = wb.create_sheet("Summary")
+    co = data.get("company", {})
+    units_label = data.get("units_label", data.get("units", "Crores"))
+    currency    = data.get("currency", co.get("currency", "INR"))
+    _title(ws, f"{co.get('name','')} ({co.get('ticker','')}) — Financial Analysis",
+           f"Report date: {co.get('report_date', datetime.date.today().isoformat())}")
+    r = 4
+    for label, val in [
+        ("Company",         co.get("name","")),
+        ("Ticker",          co.get("ticker","")),
+        ("Exchange",        co.get("exchange","")),
+        ("Industry",        co.get("industry","")),
+        ("Sector",          co.get("sector","")),
+        ("Currency",        currency),
+        ("Reporting Units", units_label),
+        ("Fiscal Year End", co.get("fiscal_year_end","")),
+        ("Analysis Period", f"{len(data.get('years',[]))} years ({', '.join(data.get('years',[]))})"),
+        ("Share Price",     co.get("share_price")),
+        ("Market Cap",      co.get("market_cap")),
+        ("Data Source",     data.get("source", "Yahoo Finance (yahooquery)")),
+        ("Models Used",     ", ".join(data.get("models_used", []) or ["None"])),
+        ("Warnings",        " | ".join(data.get("warnings", []))),
+    ]:
+        ws.cell(row=r, column=1, value=label).font = LBL_FONT
+        ws.cell(row=r, column=2, value=val).font   = DAT_FONT
+        r += 1
+    _widths(ws, [28, 50])
+
+
+def _income_sheet(wb, data):
+    ws = wb.create_sheet("Income Statement")
+    co, years = data.get("company",{}), data.get("years",[])
+    units_label = data.get("units_label", data.get("units", "Crores"))
+    currency    = data.get("currency", co.get("currency", "INR"))
+    _title(ws, f"Income Statement — {co.get('ticker','')}",
+           f"All figures in {units_label}")
+    _hdr(ws, 4, [f"({units_label})"] + years)
+    inc = data.get("income_statement", {})
+    r = _stmt_section(ws, 5, "Revenue & Profitability", [
+        ("Revenue",              "revenue"),
+        ("Cost of Revenue",      "cost_of_revenue"),
+        ("Gross Profit",         "gross_profit"),
+        ("Operating Expenses",   "operating_expenses"),
+        ("Operating Income (EBIT)","operating_income"),
+        ("D&A",                  "depreciation_amort"),
+        ("EBITDA",               "ebitda"),
+        ("Interest Expense",     "interest_expense"),
+        ("Pretax Income",        "pretax_income"),
+        ("Tax Expense",          "tax_expense"),
+        ("Net Income",           "net_income"),
+    ], inc, years)
+    r += 1
+    _sec(ws, r, "Per-Share Data", 1+len(years)); r += 1
+    _data_row(ws, r, "Diluted EPS",    inc.get("diluted_eps",[None]*len(years))[:len(years)], FMT_DEC); r += 1
+    _data_row(ws, r, "Diluted Shares (M)", inc.get("diluted_shares",[None]*len(years))[:len(years)], FMT_MONEY)
+
+    # Add revenue bar chart
+    if len(years) >= 2:
+        chart = BarChart()
+        chart.title = f"Revenue Trend ({units_label})"
+        chart.style = 10
+        chart.y_axis.title = units_label
+        rev_row = 6
+        data_ref = Reference(ws, min_col=2, max_col=1+len(years), min_row=rev_row, max_row=rev_row)
+        cats_ref = Reference(ws, min_col=2, max_col=1+len(years), min_row=4, max_row=4)
+        chart.add_data(data_ref)
+        chart.set_categories(cats_ref)
+        chart.shape = 4
+        ws.add_chart(chart, f"A{r+3}")
+
+    _widths(ws, [34]+[16]*len(years)); _freeze(ws, 4)
+
+
+def _balance_sheet(wb, data):
+    ws = wb.create_sheet("Balance Sheet")
+    co, years = data.get("company",{}), data.get("years",[])
+    units_label = data.get("units_label", data.get("units", "Crores"))
+    _title(ws, f"Balance Sheet — {co.get('ticker','')}",
+           f"As of fiscal year-end. {units_label}")
+    _hdr(ws, 4, [f"({units_label})"] + years)
+    bs = data.get("balance_sheet", {})
+    r = 5
+    for sec_title, items in [
+        ("Current Assets", [
+            ("Cash & Equivalents","cash_and_equivalents"),("Short-term Investments","short_term_investments"),
+            ("Receivables","receivables"),("Inventory","inventory"),("Other Current Assets","other_current_assets"),
+            ("Total Current Assets","total_current_assets"),
+        ]),
+        ("Non-Current Assets", [
+            ("PP&E (Net)","ppe_net"),("Goodwill","goodwill"),("Intangibles","intangibles"),
+            ("Other LT Assets","other_long_term_assets"),("Total Assets","total_assets"),
+        ]),
+        ("Current Liabilities", [
+            ("Accounts Payable","accounts_payable"),("Short-term Debt","short_term_debt"),
+            ("Other Current Liabilities","other_current_liabilities"),
+            ("Total Current Liabilities","total_current_liabilities"),
+        ]),
+        ("Non-Current Liabilities & Equity", [
+            ("Long-term Debt","long_term_debt"),("Other LT Liabilities","other_long_term_liabilities"),
+            ("Total Liabilities","total_liabilities"),("Total Equity","total_equity"),
+            ("Total Liabilities + Equity","total_liab_and_equity"),
+        ]),
+    ]:
+        r = _stmt_section(ws, r, sec_title, items, bs, years)
+    _widths(ws, [36]+[16]*len(years)); _freeze(ws, 4)
+
+
+def _cashflow_sheet(wb, data):
+    ws = wb.create_sheet("Cash Flow")
+    co, years = data.get("company",{}), data.get("years",[])
+    units_label = data.get("units_label", data.get("units", "Crores"))
+    _title(ws, f"Cash Flow Statement — {co.get('ticker','')}",
+           f"All figures in {units_label}")
+    _hdr(ws, 4, [f"({units_label})"] + years)
+    cf = data.get("cash_flow", {})
+    r = 5
+    for sec_title, items in [
+        ("Operating Activities", [
+            ("Net Income","net_income"),("D&A","depreciation_amort"),("Stock-Based Comp","stock_based_comp"),
+            ("Working Capital Changes","working_capital_changes"),("Cash from Operations","cash_from_operations"),
+        ]),
+        ("Investing Activities", [
+            ("Capital Expenditures","capex"),("Acquisitions","acquisitions"),("Cash from Investing","cash_from_investing"),
+        ]),
+        ("Financing Activities", [
+            ("Debt Issuance/(Repayment)","debt_issuance_repayment"),("Buybacks","stock_buybacks"),
+            ("Dividends Paid","dividends_paid"),("Cash from Financing","cash_from_financing"),
+        ]),
+        ("Summary", [
+            ("Net Change in Cash","net_change_in_cash"),("Free Cash Flow","free_cash_flow"),
+        ]),
+    ]:
+        r = _stmt_section(ws, r, sec_title, items, cf, years)
+    _widths(ws, [36]+[16]*len(years)); _freeze(ws, 4)
+
+
+RATIO_SECTIONS = [
+    ("Profitability", [
+        ("gross_margin","Gross Margin",FMT_PCT),("operating_margin","Operating Margin",FMT_PCT),
+        ("ebitda_margin","EBITDA Margin",FMT_PCT),("net_margin","Net Margin",FMT_PCT),
+        ("roe","ROE",FMT_PCT),("roa","ROA",FMT_PCT),("roic","ROIC",FMT_PCT),
+    ]),
+    ("Liquidity", [
+        ("current_ratio","Current Ratio",FMT_RATIO),("quick_ratio","Quick Ratio",FMT_RATIO),
+        ("cash_ratio","Cash Ratio",FMT_RATIO),
+    ]),
+    ("Solvency / Leverage", [
+        ("debt_to_equity","Debt-to-Equity",FMT_RATIO),("debt_to_assets","Debt-to-Assets",FMT_RATIO),
+        ("net_debt_to_ebitda","Net Debt/EBITDA",FMT_RATIO),("interest_coverage","Interest Coverage",FMT_RATIO),
+    ]),
+    ("Efficiency", [
+        ("asset_turnover","Asset Turnover",FMT_RATIO),("inventory_turnover","Inventory Turnover",FMT_RATIO),
+        ("days_inventory","Days Inventory (DIO)",FMT_DAYS),("days_sales_outstanding","DSO",FMT_DAYS),
+        ("days_payables","DPO",FMT_DAYS),("cash_conversion_cycle","Cash Conversion Cycle",FMT_DAYS),
+    ]),
+    ("Cash Flow Quality", [
+        ("ocf_to_net_income","OCF/Net Income",FMT_RATIO),("fcf_margin","FCF Margin",FMT_PCT),
+        ("fcf_conversion","FCF Conversion",FMT_RATIO),("capex_intensity","Capex/Revenue",FMT_PCT),
+    ]),
+]
+
+
+def _ratios_sheet(wb, data):
+    ws = wb.create_sheet("Ratios")
+    years = data.get("years", [])
+    _title(ws, f"Financial Ratios — {data.get('company',{}).get('ticker','')}", "5-year trend")
+    _hdr(ws, 4, ["Ratio"] + years)
+    ratios = data.get("ratios", {})
+    r = 5
+    for sec_name, items in RATIO_SECTIONS:
+        _sec(ws, r, sec_name, 1+len(years)); r += 1
+        for key, label, fmt in items:
+            vals = ratios.get(key, [None]*len(years))
+            vals = (vals + [None]*len(years))[:len(years)]
+            _data_row(ws, r, label, vals, fmt); r += 1
+    _widths(ws, [40]+[14]*len(years)); _freeze(ws, 4)
+
+
+def _dupont_sheet(wb, data):
+    ws = wb.create_sheet("DuPont Analysis")
+    years = data.get("years", [])
+    _title(ws, "DuPont Decomposition", "ROE = Net Margin × Asset Turnover × Equity Multiplier")
+    _hdr(ws, 4, ["Component"] + years)
+    dp = data.get("dupont", {})
+    r = 5
+    for label, key, fmt in [
+        ("Net Margin",       "net_margin",        FMT_PCT),
+        ("× Asset Turnover", "asset_turnover",     FMT_RATIO),
+        ("× Equity Multiplier","equity_multiplier",FMT_RATIO),
+        ("= ROE (DuPont)",   "roe_computed",       FMT_PCT),
+        ("ROE (Reported)",   "roe_reported",       FMT_PCT),
+    ]:
+        vals = dp.get(key, [None]*len(years))
+        vals = (vals + [None]*len(years))[:len(years)]
+        _data_row(ws, r, label, vals, fmt)
+        r += 1
+    _widths(ws, [28]+[14]*len(years))
+
+
+def _growth_sheet(wb, data):
+    ws = wb.create_sheet("Growth Metrics")
+    years = data.get("years", [])
+    yoy_hdrs = [f"{years[i]} vs {years[i-1]}" for i in range(1, len(years))] if len(years) > 1 else []
+    _title(ws, "Growth Metrics", "YoY growth and CAGRs")
+    _hdr(ws, 4, ["Metric"] + yoy_hdrs + ["CAGR"])
+    g = data.get("growth_metrics", {})
+    r = 5
+    for label, yoy_key, cagr_key in [
+        ("Revenue",        "revenue_yoy",        "revenue_cagr_5y"),
+        ("Operating Income","operating_income_yoy","operating_income_cagr_5y"),
+        ("Net Income",     "net_income_yoy",      "net_income_cagr_5y"),
+        ("Diluted EPS",    "eps_yoy",             "eps_cagr_5y"),
+        ("Free Cash Flow", "fcf_yoy",             "fcf_cagr_5y"),
+    ]:
+        yoy = g.get(yoy_key, [])[1:]  # skip first None
+        cagr = g.get(cagr_key)
+        vals = (yoy + [None]*len(yoy_hdrs))[:len(yoy_hdrs)] + [cagr]
+        _data_row(ws, r, label, vals, FMT_PCT); r += 1
+    _widths(ws, [28]+[16]*(len(yoy_hdrs)+1))
+
+
+def _peers_sheet(wb, data):
+    ws = wb.create_sheet("Peer Comparison")
+    co = data.get("company", {})
+    _title(ws, f"Peer Comparison — {co.get('ticker','')}", "Latest fiscal year")
+    cols = ["Company","Ticker","Revenue","Gross Margin","Op Margin","Net Margin",
+            "ROE","ROIC","D/E","Current Ratio","FCF Margin","P/E","EV/EBITDA","Rationale"]
+    _hdr(ws, 4, cols)
+    r = 5
+    years = data.get("years", [])
+    rat = data.get("ratios", {}); inc = data.get("income_statement", {})
+    val = data.get("valuation_snapshot", {})
+    # Target row
+    target = [
+        co.get("name",""), co.get("ticker",""),
+        _last(inc.get("revenue",[])), _last(rat.get("gross_margin",[])),
+        _last(rat.get("operating_margin",[])), _last(rat.get("net_margin",[])),
+        _last(rat.get("roe",[])), _last(rat.get("roic",[])),
+        _last(rat.get("debt_to_equity",[])), _last(rat.get("current_ratio",[])),
+        _last(rat.get("fcf_margin",[])), val.get("pe_ttm"), val.get("ev_ebitda"),
+        "Target company",
+    ]
+    fmts = ["","","money","pct","pct","pct","pct","pct","ratio","ratio","pct","ratio","ratio",""]
+    _write_peer_row(ws, r, target, fmts, is_target=True); r += 1
+    for p in data.get("peers", []):
+        peer_row = [
+            p.get("name",""), p.get("ticker",""), p.get("revenue"),
+            p.get("gross_margin"), p.get("operating_margin"), p.get("net_margin"),
+            p.get("roe"), p.get("roic"), p.get("debt_to_equity"), p.get("current_ratio"),
+            p.get("fcf_margin"), p.get("pe_ttm"), p.get("ev_ebitda"), p.get("rationale",""),
+        ]
+        _write_peer_row(ws, r, peer_row, fmts); r += 1
+    _widths(ws, [24,10,16,12,12,12,10,10,10,12,12,10,12,50])
+
+
+def _write_peer_row(ws, row, values, fmts, is_target=False):
+    fmt_map = {"money":FMT_MONEY,"pct":FMT_PCT,"ratio":FMT_RATIO}
+    fill = PatternFill("solid", fgColor="FFF2CC") if is_target else None
+    for i, v in enumerate(values):
+        c = ws.cell(row=row, column=i+1, value=v if v is not None else "—")
+        c.border = THIN
+        c.font = Font(name="Calibri", size=11, bold=is_target)
+        if fill: c.fill = fill
+        fmt = fmts[i] if i < len(fmts) else ""
+        if fmt in fmt_map and isinstance(v, (int, float)):
+            c.number_format = fmt_map[fmt]
+            c.alignment = RIGHT
+        else:
+            c.alignment = LEFT
+
+
+def _swot_sheet(wb, data):
+    ws = wb.create_sheet("SWOT Analysis")
+    _title(ws, "SWOT Analysis", "Strengths, Weaknesses, Opportunities, Threats")
+    swot = data.get("swot", {})
+    r = 4
+    colors = {"Strengths":"C6EFCE","Weaknesses":"FFC7CE","Opportunities":"DDEBF7","Threats":"FFEB9C"}
+    for category, key in [("Strengths","strengths"),("Weaknesses","weaknesses"),
+                           ("Opportunities","opportunities"),("Threats","threats")]:
+        fill = PatternFill("solid", fgColor=colors[category])
+        hdr_cell = ws.cell(row=r, column=1, value=category)
+        hdr_cell.font = Font(name="Calibri", size=12, bold=True)
+        hdr_cell.fill = fill
+        r += 1
+        for item in swot.get(key, []):
+            c = ws.cell(row=r, column=1, value=f"• {item}")
+            c.font = Font(name="Calibri", size=11)
+            c.alignment = Alignment(horizontal="left", vertical="top", wrap_text=True)
+            ws.row_dimensions[r].height = 32
+            r += 1
+        r += 1
+    _widths(ws, [90])
+
+
+def _dcf_sheet(wb, data):
+    ws = wb.create_sheet("DCF Valuation")
+    _title(ws, "Discounted Cash Flow (DCF) Model", "Interactive Valuation Model")
+    
+    co = data.get("company", {})
+    cf = data.get("cash_flow", {})
+    inc = data.get("income_statement", {})
+    bal = data.get("balance_sheet", {})
+    
+    # Get latest data
+    fcf_history = [v for v in cf.get("free_cash_flow", []) if v is not None]
+    latest_fcf = fcf_history[-1] if fcf_history else 0
+    
+    shares_history = [v for v in inc.get("diluted_shares", []) if v is not None]
+    latest_shares = shares_history[-1] if shares_history else 1
+    
+    cash_history = [v for v in bal.get("cash_and_equivalents", []) if v is not None]
+    cash = cash_history[-1] if cash_history else 0
+    
+    debt_history = [v for v in bal.get("total_liabilities", []) if v is not None]
+    debt = debt_history[-1] if debt_history else 0
+    
+    # Inputs
+    val_snap = data.get("valuation_snapshot", {})
+    wacc_est = val_snap.get("wacc_estimate", 0.10)
+    target_price = val_snap.get("target_mean_price", "N/A")
+    
+    r = 4
+    _sec(ws, r, "Model Inputs (Editable - Yellow Cells)", 2); r += 1
+    
+    ws.cell(row=r, column=1, value="Starting FCF (Base)").font = LBL_FONT
+    ws.cell(row=r, column=2, value=latest_fcf).font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; ws.cell(row=r, column=2).fill = PatternFill("solid", fgColor="FFFFE0"); r += 1
+    
+    ws.cell(row=r, column=1, value="WACC (Discount Rate)").font = LBL_FONT
+    ws.cell(row=r, column=2, value=wacc_est).font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_PCT; ws.cell(row=r, column=2).fill = PatternFill("solid", fgColor="FFFFE0"); r += 1
+    
+    ws.cell(row=r, column=1, value="Terminal Growth Rate").font = LBL_FONT
+    ws.cell(row=r, column=2, value=0.025).font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_PCT; ws.cell(row=r, column=2).fill = PatternFill("solid", fgColor="FFFFE0"); r += 1
+    
+    ws.cell(row=r, column=1, value="Cash & Equivalents").font = LBL_FONT
+    ws.cell(row=r, column=2, value=cash).font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; ws.cell(row=r, column=2).fill = PatternFill("solid", fgColor="FFFFE0"); r += 1
+    
+    ws.cell(row=r, column=1, value="Total Debt").font = LBL_FONT
+    ws.cell(row=r, column=2, value=debt).font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; ws.cell(row=r, column=2).fill = PatternFill("solid", fgColor="FFFFE0"); r += 1
+    
+    ws.cell(row=r, column=1, value="Diluted Shares").font = LBL_FONT
+    ws.cell(row=r, column=2, value=latest_shares).font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; ws.cell(row=r, column=2).fill = PatternFill("solid", fgColor="FFFFE0"); r += 1
+    
+    r += 1
+    _sec(ws, r, "Projected Cash Flows (5 Years)", 6); r += 1
+    _hdr(ws, r, ["Year 1", "Year 2", "Year 3", "Year 4", "Year 5"], start=2); r += 1
+    
+    # Growth rates
+    ws.cell(row=r, column=1, value="Projected Growth Rate").font = LBL_FONT
+    for c in range(2, 7):
+        ws.cell(row=r, column=c, value=0.05).font = DAT_FONT; ws.cell(row=r, column=c).number_format = FMT_PCT; ws.cell(row=r, column=c).fill = PatternFill("solid", fgColor="FFFFE0")
+    r += 1
+    
+    # FCF Projection Formulas
+    ws.cell(row=r, column=1, value="Projected FCF").font = LBL_FONT
+    ws.cell(row=r, column=2, value="=B5*(1+B14)") # Year 1 = Base * (1+g1)
+    ws.cell(row=r, column=3, value="=B15*(1+C14)")
+    ws.cell(row=r, column=4, value="=C15*(1+D14)")
+    ws.cell(row=r, column=5, value="=D15*(1+E14)")
+    ws.cell(row=r, column=6, value="=E15*(1+F14)")
+    for c in range(2, 7):
+        ws.cell(row=r, column=c).font = DAT_FONT; ws.cell(row=r, column=c).number_format = FMT_MONEY
+    r += 1
+    
+    # Discount Factor Formulas
+    ws.cell(row=r, column=1, value="Discount Factor").font = LBL_FONT
+    ws.cell(row=r, column=2, value="=1/((1+$B$6)^1)")
+    ws.cell(row=r, column=3, value="=1/((1+$B$6)^2)")
+    ws.cell(row=r, column=4, value="=1/((1+$B$6)^3)")
+    ws.cell(row=r, column=5, value="=1/((1+$B$6)^4)")
+    ws.cell(row=r, column=6, value="=1/((1+$B$6)^5)")
+    for c in range(2, 7):
+        ws.cell(row=r, column=c).font = DAT_FONT; ws.cell(row=r, column=c).number_format = FMT_RATIO
+    r += 1
+    
+    # PV of FCF Formulas
+    ws.cell(row=r, column=1, value="PV of FCF").font = LBL_FONT
+    for c, col_l in enumerate(["B", "C", "D", "E", "F"], start=2):
+        ws.cell(row=r, column=c, value=f"={col_l}15*{col_l}16").font = DAT_FONT; ws.cell(row=r, column=c).number_format = FMT_MONEY
+    r += 2
+    
+    # Valuation Output
+    _sec(ws, r, "Valuation Output", 2); r += 1
+    
+    ws.cell(row=r, column=1, value="Sum of PV of FCF").font = LBL_FONT
+    ws.cell(row=r, column=2, value="=SUM(B17:F17)").font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; r += 1
+    
+    ws.cell(row=r, column=1, value="Terminal Value").font = LBL_FONT
+    ws.cell(row=r, column=2, value="=(F15*(1+B7))/(B6-B7)").font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; r += 1
+    
+    ws.cell(row=r, column=1, value="PV of Terminal Value").font = LBL_FONT
+    ws.cell(row=r, column=2, value="=B21*F16").font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; r += 1
+    
+    ws.cell(row=r, column=1, value="Implied Enterprise Value").font = LBL_FONT
+    ws.cell(row=r, column=2, value="=B20+B22").font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; r += 1
+    
+    ws.cell(row=r, column=1, value="Implied Equity Value").font = LBL_FONT
+    ws.cell(row=r, column=2, value="=B23+B8-B9").font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; r += 1
+    
+    ws.cell(row=r, column=1, value="Implied Share Price").font = Font(name="Calibri", size=12, bold=True)
+    ws.cell(row=r, column=2, value="=B24/B10").font = Font(name="Calibri", size=12, bold=True); ws.cell(row=r, column=2).number_format = FMT_MONEY; r += 1
+    
+    ws.cell(row=r, column=1, value="Analyst Mean Target Price").font = LBL_FONT
+    ws.cell(row=r, column=2, value=target_price).font = DAT_FONT; ws.cell(row=r, column=2).number_format = FMT_MONEY; ws.cell(row=r, column=2).fill = PatternFill("solid", fgColor="DDEBF7"); r += 1
+    
+    _widths(ws, [30, 20, 20, 20, 20, 20])
+
+
+def _narrative_sheet(wb, data):
+    ws = wb.create_sheet("Industry & AI Insights")
+    _title(ws, "AI Narrative Analysis", "Generated by AI Models")
+    r = 4
+    
+    exec_summary = data.get("executive_summary", "")
+    if exec_summary:
+        _sec(ws, r, "Executive Summary", 2); r += 1
+        c = ws.cell(row=r, column=1, value=exec_summary)
+        c.font = LBL_FONT; c.alignment = Alignment(wrap_text=True, vertical="top")
+        ws.merge_cells(start_row=r, start_column=1, end_row=r, end_column=4)
+        ws.row_dimensions[r].height = 100
+        r += 2
+        
+    industry_trends = data.get("industry_trends", "")
+    if industry_trends:
+        _sec(ws, r, "Industry Trends", 2); r += 1
+        c = ws.cell(row=r, column=1, value=industry_trends)
+        c.font = LBL_FONT; c.alignment = Alignment(wrap_text=True, vertical="top")
+        ws.merge_cells(start_row=r, start_column=1, end_row=r, end_column=4)
+        ws.row_dimensions[r].height = 120
+        r += 2
+        
+    _widths(ws, [100])
+
+
+def _sources_sheet(wb, data):
+    ws = wb.create_sheet("Sources")
+    _title(ws, "Sources", "All data sources and retrieval dates")
+    _hdr(ws, 4, ["#","URL","Description","Retrieved"])
+    r = 5
+    for i, src in enumerate(data.get("sources",[]), start=1):
+        ws.cell(row=r, column=1, value=i).alignment = CENTER
+        url = src.get("url","")
+        uc = ws.cell(row=r, column=2, value=url)
+        uc.font = Font(name="Calibri", size=10, color="0563C1", underline="single")
+        uc.alignment = LEFT
+        if url:
+            try: uc.hyperlink = url
+            except: pass
+        ws.cell(row=r, column=3, value=src.get("description","")).alignment = LEFT
+        ws.cell(row=r, column=4, value=src.get("retrieved","")).alignment = CENTER
+        for col in range(1,5): ws.cell(row=r,column=col).border = THIN
+        ws.row_dimensions[r].height = 28; r += 1
+    _widths(ws, [6,70,50,16])
+
+
+# ── Orchestrator ──────────────────────────────────────────────────────────────
+
+def build_workbook(data: dict, output_path: str) -> None:
+    wb = Workbook()
+    wb.remove(wb.active)
+    _summary_sheet(wb, data)
+    _income_sheet(wb, data)
+    _balance_sheet(wb, data)
+    _cashflow_sheet(wb, data)
+    _ratios_sheet(wb, data)
+    _dupont_sheet(wb, data)
+    _dcf_sheet(wb, data)
+    _growth_sheet(wb, data)
+    _peers_sheet(wb, data)
+    _swot_sheet(wb, data)
+    _narrative_sheet(wb, data)
+    _sources_sheet(wb, data)
+    wb.save(output_path)
+
+
+if __name__ == "__main__":
+    import argparse
+    p = argparse.ArgumentParser()
+    p.add_argument("--input", "-i", required=True)
+    p.add_argument("--output", "-o", required=True)
+    args = p.parse_args()
+    with open(args.input, encoding="utf-8") as f:
+        data = json.load(f)
+    Path(args.output).parent.mkdir(parents=True, exist_ok=True)
+    build_workbook(data, args.output)
+    print(f"Wrote: {args.output}")

pipeline/resolver.py

@@ -1,346 +1,354 @@
-"""
-pipeline/resolver.py — Resolve a company name or ticker to structured metadata.
-
-Source priority:
-  1. yfinance (free, no key, excellent Indian stock support)
-  2. FMP API (if key available)
-  3. Mark as unlisted with clear message
-"""
-from __future__ import annotations
-
-import sys
-from pathlib import Path
-from typing import Optional
-
-sys.path.insert(0, str(Path(__file__).parent.parent))
-from config import FMP_API_KEY, FMP_BASE, REQUEST_TIMEOUT
-
-import requests
-
-
-# ── CompanyMeta ───────────────────────────────────────────────────────────────
-
-class CompanyMeta:
-    def __init__(self, name, ticker, exchange, currency, sector, industry,
-                 country, description, website, mkt_cap, share_price, is_listed,
-                 source_note="", fiscal_year_end="March"):
-        self.name = name
-        self.ticker = ticker
-        self.exchange = exchange
-        self.currency = currency
-        self.sector = sector
-        self.industry = industry
-        self.country = country
-        self.description = description
-        self.website = website
-        self.mkt_cap = mkt_cap
-        self.share_price = share_price
-        self.is_listed = is_listed
-        self.source_note = source_note
-        self.fiscal_year_end = fiscal_year_end
-
-    def to_dict(self):
-        return self.__dict__.copy()
-
-
-# ── Indian company name → NSE ticker map ─────────────────────────────────────
-
-INDIAN_TICKERS = {
-    # IT
-    "tcs": "TCS.NS", "tata consultancy": "TCS.NS", "tata consultancy services": "TCS.NS",
-    "infosys": "INFY.NS", "infy": "INFY.NS",
-    "wipro": "WIPRO.NS",
-    "hcl": "HCLTECH.NS", "hcl tech": "HCLTECH.NS", "hcl technologies": "HCLTECH.NS",
-    "tech mahindra": "TECHM.NS", "techm": "TECHM.NS",
-    "ltimindtree": "LTIM.NS", "lti mindtree": "LTIM.NS",
-    "mphasis": "MPHASIS.NS",
-    "persistent": "PERSISTENT.NS",
-    "coforge": "COFORGE.NS",
-    # Banking
-    "hdfc bank": "HDFCBANK.NS", "hdfcbank": "HDFCBANK.NS",
-    "icici bank": "ICICIBANK.NS", "icicibank": "ICICIBANK.NS",
-    "axis bank": "AXISBANK.NS", "axisbank": "AXISBANK.NS",
-    "sbi": "SBIN.NS", "state bank": "SBIN.NS", "state bank of india": "SBIN.NS",
-    "kotak": "KOTAKBANK.NS", "kotak bank": "KOTAKBANK.NS", "kotak mahindra bank": "KOTAKBANK.NS",
-    "yes bank": "YESBANK.NS",
-    "indusind bank": "INDUSINDBK.NS",
-    "bank of baroda": "BANKBARODA.NS",
-    "pnb": "PNB.NS", "punjab national bank": "PNB.NS",
-    "canara bank": "CANARABANK.NS",
-    # Conglomerates / Energy
-    "reliance": "RELIANCE.NS", "reliance industries": "RELIANCE.NS", "ril": "RELIANCE.NS",
-    "tata": "TATAMOTORS.NS",
-    "adani enterprises": "ADANIENT.NS", "adani ports": "ADANIPORTS.NS",
-    "adani green": "ADANIGREEN.NS", "adani total gas": "ATGL.NS",
-    "ongc": "ONGC.NS",
-    "oil and natural gas": "ONGC.NS",
-    "ioc": "IOC.NS", "indian oil": "IOC.NS",
-    "bpcl": "BPCL.NS", "bharat petroleum": "BPCL.NS",
-    "coal india": "COALINDIA.NS",
-    # FMCG
-    "hindustan unilever": "HINDUNILVR.NS", "hul": "HINDUNILVR.NS",
-    "itc": "ITC.NS",
-    "nestle india": "NESTLEIND.NS", "nestle": "NESTLEIND.NS",
-    "britannia": "BRITANNIA.NS",
-    "dabur": "DABUR.NS",
-    "marico": "MARICO.NS",
-    "godrej consumer": "GODREJCP.NS", "godrej": "GODREJCP.NS",
-    "emami": "EMAMILTD.NS",
-    # Pharma
-    "sun pharma": "SUNPHARMA.NS", "sun pharmaceutical": "SUNPHARMA.NS",
-    "cipla": "CIPLA.NS",
-    "dr reddy": "DRREDDY.NS", "dr reddys": "DRREDDY.NS",
-    "divi's labs": "DIVISLAB.NS", "divis": "DIVISLAB.NS",
-    "aurobindo": "AUROPHARMA.NS",
-    "lupin": "LUPIN.NS",
-    "torrent pharma": "TORNTPHARM.NS",
-    # Auto
-    "maruti": "MARUTI.NS", "maruti suzuki": "MARUTI.NS",
-    "tata motors": "TATAMOTORS.NS",
-    "m&m": "M&M.NS", "mahindra": "M&M.NS", "mahindra and mahindra": "M&M.NS",
-    "hero motocorp": "HEROMOTOCO.NS", "hero": "HEROMOTOCO.NS",
-    "bajaj auto": "BAJAJ-AUTO.NS",
-    "eicher": "EICHERMOT.NS", "royal enfield": "EICHERMOT.NS",
-    "tvs motor": "TVSMOTOR.NS",
-    # Finance (non-bank)
-    "bajaj finance": "BAJFINANCE.NS",
-    "bajaj finserv": "BAJAJFINSV.NS",
-    "hdfc life": "HDFCLIFE.NS",
-    "sbi life": "SBILIFE.NS",
-    "lic": "LICI.NS",
-    "icici prudential": "ICICIPRULI.NS",
-    "muthoot": "MUTHOOTFIN.NS",
-    # Infra / Utilities
-    "larsen": "LT.NS", "l&t": "LT.NS", "larsen and toubro": "LT.NS",
-    "ntpc": "NTPC.NS",
-    "power grid": "POWERGRID.NS",
-    "tata power": "TATAPOWER.NS",
-    "adani power": "ADANIPOWER.NS",
-    # Cement / Materials
-    "ultratech": "ULTRACEMCO.NS", "ultratech cement": "ULTRACEMCO.NS",
-    "shree cement": "SHREECEM.NS",
-    "ambuja": "AMBUJACEM.NS",
-    "acc": "ACC.NS",
-    "grasim": "GRASIM.NS",
-    "tata steel": "TATASTEEL.NS",
-    "jsw steel": "JSWSTEEL.NS",
-    "hindalco": "HINDALCO.NS",
-    "vedanta": "VEDL.NS",
-    "jspl": "JINDALSTEL.NS",
-    # Paints / Consumer
-    "asian paints": "ASIANPAINT.NS",
-    "berger paints": "BERGEPAINT.NS",
-    "pidilite": "PIDILITIND.NS",
-    "havells": "HAVELLS.NS",
-    "voltas": "VOLTAS.NS",
-    "titan": "TITAN.NS",
-    "tanishq": "TITAN.NS",
-    # Retail / Consumer
-    "dmart": "DMART.NS", "avenue supermarts": "DMART.NS",
-    "trent": "TRENT.NS",
-    "zomato": "ZOMATO.NS",
-    "nykaa": "NYKAA.NS",
-    "policybazaar": "POLICYBZR.NS",
-    # Telecom
-    "airtel": "BHARTIARTL.NS", "bharti airtel": "BHARTIARTL.NS",
-    "jio": "RELIANCE.NS",  # Jio is part of Reliance
-    # IT Infrastructure
-    "hcl infosystems": "HCLISEC.NS",
-    "dixon": "DIXON.NS",
-}
-
-
-def _guess_ticker(query: str) -> Optional[str]:
-    """Try to find an Indian NSE ticker for a given name/query."""
-    q = query.lower().strip()
-    # Exact match
-    if q in INDIAN_TICKERS:
-        return INDIAN_TICKERS[q]
-    # Partial match
-    for key, ticker in INDIAN_TICKERS.items():
-        if key in q or q in key:
-            return ticker
-    return None
-
-
-# ── yfinance resolution ───────────────────────────────────────────────────────
-
-def _yf_resolve(ticker: str, log_fn=None) -> Optional[CompanyMeta]:
-    """Try to resolve a ticker via yfinance. Returns CompanyMeta or None."""
-    def log(msg):
-        if log_fn: log_fn(msg)
-    try:
-        import yfinance as yf
-        t = yf.Ticker(ticker)
-        info = t.info
-        name = info.get("longName") or info.get("shortName", "")
-        if not name:
-            return None
-        exchange_code = info.get("exchange", "")
-        currency = info.get("currency", "INR")
-        exchange_map = {
-            "NSI": "NSE (India)", "BSE": "BSE (India)",
-            "NMS": "NASDAQ (USA)", "NYQ": "NYSE (USA)",
-            "LSE": "LSE (UK)",
-        }
-        exchange_display = exchange_map.get(exchange_code, exchange_code or "Unknown")
-        country = info.get("country", "India")
-        # Fiscal year end
-        fy_end = "March" if currency == "INR" else "December"
-
-        log(f"  ✓ yfinance: {name} | {ticker} | {exchange_display}")
-        return CompanyMeta(
-            name=name,
-            ticker=ticker,
-            exchange=exchange_display,
-            currency=currency,
-            sector=info.get("sector", ""),
-            industry=info.get("industry", ""),
-            country=country,
-            description=info.get("longBusinessSummary", ""),
-            website=info.get("website", ""),
-            mkt_cap=info.get("marketCap"),
-            share_price=info.get("currentPrice") or info.get("regularMarketPrice"),
-            is_listed=True,
-            source_note="Data sourced from Yahoo Finance (yfinance).",
-            fiscal_year_end=fy_end,
-        )
-    except Exception as e:
-        log(f"  ✗ yfinance failed for {ticker}: {e}")
-        return None
-
-
-# ── FMP resolution ────────────────────────────────────────────────────────────
-
-def _fmp_get(path: str, params: dict | None = None):
-    if not FMP_API_KEY:
-        return None
-    url = f"{FMP_BASE}{path}"
-    p = {"apikey": FMP_API_KEY}
-    if params: p.update(params)
-    try:
-        r = requests.get(url, params=p, timeout=REQUEST_TIMEOUT)
-        r.raise_for_status()
-        return r.json()
-    except Exception:
-        return None
-
-
-def _fmp_resolve(query: str, log_fn=None) -> Optional[CompanyMeta]:
-    """Try FMP search for global companies."""
-    def log(msg):
-        if log_fn: log_fn(msg)
-    if not FMP_API_KEY:
-        return None
-    log("  → Searching FMP …")
-    # Direct profile
-    profile = _fmp_get(f"/v3/profile/{query.upper()}")
-    if not (profile and isinstance(profile, list) and profile[0].get("symbol")):
-        # Search
-        results = _fmp_get("/v3/search", {"query": query, "limit": 5})
-        if results:
-            best = results[0]
-            profile = _fmp_get(f"/v3/profile/{best['symbol']}")
-    if not (profile and isinstance(profile, list)):
-        return None
-    p = profile[0]
-    if not p.get("companyName"):
-        return None
-    exchange = p.get("exchange", "") or p.get("exchangeShortName", "")
-    exchange_map = {"NSE":"NSE (India)","BSE":"BSE (India)","NASDAQ":"NASDAQ (USA)","NYSE":"NYSE (USA)"}
-    log(f"  ✓ FMP: {p.get('companyName')} ({p.get('symbol')})")
-    return CompanyMeta(
-        name=p.get("companyName",""),
-        ticker=p.get("symbol",""),
-        exchange=exchange_map.get(exchange, exchange),
-        currency=p.get("currency","USD"),
-        sector=p.get("sector",""),
-        industry=p.get("industry",""),
-        country=p.get("country",""),
-        description=p.get("description",""),
-        website=p.get("website",""),
-        mkt_cap=p.get("mktCap"),
-        share_price=p.get("price"),
-        is_listed=True,
-        source_note="Data sourced from FMP.",
-        fiscal_year_end="December",
-    )
-
-
-# ── yfinance search by company name ──────────────────────────────────────────
-
-def _yf_search(query: str, log_fn=None) -> Optional[CompanyMeta]:
-    """Use yfinance's search to find a ticker from a company name."""
-    def log(msg):
-        if log_fn: log_fn(msg)
-    try:
-        import yfinance as yf
-        log(f"  → yfinance search for '{query}' …")
-        results = yf.Search(query, max_results=5)
-        quotes = results.quotes
-        if not quotes:
-            return None
-        # Take the first relevant result
-        for q in quotes:
-            symbol = q.get("symbol", "")
-            qtype = q.get("quoteType", "")
-            if qtype in ("EQUITY", "MUTUALFUND") and symbol:
-                log(f"  → Found: {q.get('longname', symbol)} ({symbol})")
-                return _yf_resolve(symbol, log_fn)
-        return None
-    except Exception as e:
-        log(f"  ✗ yfinance search failed: {e}")
-        return None
-
-
-# ── Public resolve() ──────────────────────────────────────────────────────────
-
-def resolve(query: str, log_fn=None) -> CompanyMeta:
-    """
-    Resolve a company name or ticker to CompanyMeta.
-    Source priority: Indian map → yfinance direct → yfinance search → FMP → unlisted
-    """
-    def log(msg):
-        if log_fn: log_fn(msg)
-
-    query = query.strip()
-    log(f"Resolving: '{query}' …")
-
-    # 1. Check Indian name map → resolve via yfinance
-    indian_ticker = _guess_ticker(query)
-    if indian_ticker:
-        log(f"  → Mapped to Indian ticker: {indian_ticker}")
-        meta = _yf_resolve(indian_ticker, log_fn)
-        if meta:
-            return meta
-
-    # 2. Try direct yfinance (query as-is, and with .NS suffix)
-    for candidate in [query, query.upper(), f"{query.upper()}.NS", f"{query.upper()}.BO"]:
-        meta = _yf_resolve(candidate, log_fn)
-        if meta:
-            return meta
-
-    # 3. yfinance search
-    meta = _yf_search(query, log_fn)
-    if meta:
-        return meta
-
-    # 4. FMP search (for global companies)
-    meta = _fmp_resolve(query, log_fn)
-    if meta:
-        return meta
-
-    # 5. Unlisted fallback
-    log(f"  ⚠ '{query}' not found in any public market database.")
-    log("  → Treating as unlisted / private company.")
-    return CompanyMeta(
-        name=query, ticker="UNLISTED", exchange="Unlisted / Private",
-        currency="INR", sector="Unknown", industry="Unknown",
-        country="India", description="", website="",
-        mkt_cap=None, share_price=None, is_listed=False,
-        source_note=(
-            f"'{query}' could not be found. It may be unlisted, private, or use a different name. "
-            "Try using the exact BSE/NSE ticker (e.g. RELIANCE, TCS, HDFCBANK)."
-        ),
-        fiscal_year_end="March",
-    )
+"""
+pipeline/resolver.py — Resolve a company name or ticker to structured metadata.
+
+Source priority:
+  1. yahooquery (free, no key, excellent Indian stock support)
+  2. FMP API (if key available)
+  3. Mark as unlisted with clear message
+"""
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+from typing import Optional
+
+sys.path.insert(0, str(Path(__file__).parent.parent))
+from config import FMP_API_KEY, FMP_BASE, REQUEST_TIMEOUT
+
+import requests
+
+
+# ── CompanyMeta ───────────────────────────────────────────────────────────────
+
+class CompanyMeta:
+    def __init__(self, name, ticker, exchange, currency, sector, industry,
+                 country, description, website, mkt_cap, share_price, is_listed,
+                 source_note="", fiscal_year_end="March"):
+        self.name = name
+        self.ticker = ticker
+        self.exchange = exchange
+        self.currency = currency
+        self.sector = sector
+        self.industry = industry
+        self.country = country
+        self.description = description
+        self.website = website
+        self.mkt_cap = mkt_cap
+        self.share_price = share_price
+        self.is_listed = is_listed
+        self.source_note = source_note
+        self.fiscal_year_end = fiscal_year_end
+
+    def to_dict(self):
+        return self.__dict__.copy()
+
+
+# ── Indian company name → NSE ticker map ─────────────────────────────────────
+
+INDIAN_TICKERS = {
+    # IT
+    "tcs": "TCS.NS", "tata consultancy": "TCS.NS", "tata consultancy services": "TCS.NS",
+    "infosys": "INFY.NS", "infy": "INFY.NS",
+    "wipro": "WIPRO.NS",
+    "hcl": "HCLTECH.NS", "hcl tech": "HCLTECH.NS", "hcl technologies": "HCLTECH.NS",
+    "tech mahindra": "TECHM.NS", "techm": "TECHM.NS",
+    "ltimindtree": "LTIM.NS", "lti mindtree": "LTIM.NS",
+    "mphasis": "MPHASIS.NS",
+    "persistent": "PERSISTENT.NS",
+    "coforge": "COFORGE.NS",
+    # Banking
+    "hdfc bank": "HDFCBANK.NS", "hdfcbank": "HDFCBANK.NS",
+    "icici bank": "ICICIBANK.NS", "icicibank": "ICICIBANK.NS",
+    "axis bank": "AXISBANK.NS", "axisbank": "AXISBANK.NS",
+    "sbi": "SBIN.NS", "state bank": "SBIN.NS", "state bank of india": "SBIN.NS",
+    "kotak": "KOTAKBANK.NS", "kotak bank": "KOTAKBANK.NS", "kotak mahindra bank": "KOTAKBANK.NS",
+    "yes bank": "YESBANK.NS",
+    "indusind bank": "INDUSINDBK.NS",
+    "bank of baroda": "BANKBARODA.NS",
+    "pnb": "PNB.NS", "punjab national bank": "PNB.NS",
+    "canara bank": "CANARABANK.NS",
+    # Conglomerates / Energy
+    "reliance": "RELIANCE.NS", "reliance industries": "RELIANCE.NS", "ril": "RELIANCE.NS",
+    "tata": "TATAMOTORS.NS",
+    "adani enterprises": "ADANIENT.NS", "adani ports": "ADANIPORTS.NS",
+    "adani green": "ADANIGREEN.NS", "adani total gas": "ATGL.NS",
+    "ongc": "ONGC.NS",
+    "oil and natural gas": "ONGC.NS",
+    "ioc": "IOC.NS", "indian oil": "IOC.NS",
+    "bpcl": "BPCL.NS", "bharat petroleum": "BPCL.NS",
+    "coal india": "COALINDIA.NS",
+    # FMCG
+    "hindustan unilever": "HINDUNILVR.NS", "hul": "HINDUNILVR.NS",
+    "itc": "ITC.NS",
+    "nestle india": "NESTLEIND.NS", "nestle": "NESTLEIND.NS",
+    "britannia": "BRITANNIA.NS",
+    "dabur": "DABUR.NS",
+    "marico": "MARICO.NS",
+    "godrej consumer": "GODREJCP.NS", "godrej": "GODREJCP.NS",
+    "emami": "EMAMILTD.NS",
+    # Pharma
+    "sun pharma": "SUNPHARMA.NS", "sun pharmaceutical": "SUNPHARMA.NS",
+    "cipla": "CIPLA.NS",
+    "dr reddy": "DRREDDY.NS", "dr reddys": "DRREDDY.NS",
+    "divi's labs": "DIVISLAB.NS", "divis": "DIVISLAB.NS",
+    "aurobindo": "AUROPHARMA.NS",
+    "lupin": "LUPIN.NS",
+    "torrent pharma": "TORNTPHARM.NS",
+    # Auto
+    "maruti": "MARUTI.NS", "maruti suzuki": "MARUTI.NS",
+    "tata motors": "TATAMOTORS.NS",
+    "m&m": "M&M.NS", "mahindra": "M&M.NS", "mahindra and mahindra": "M&M.NS",
+    "hero motocorp": "HEROMOTOCO.NS", "hero": "HEROMOTOCO.NS",
+    "bajaj auto": "BAJAJ-AUTO.NS",
+    "eicher": "EICHERMOT.NS", "royal enfield": "EICHERMOT.NS",
+    "tvs motor": "TVSMOTOR.NS",
+    # Finance (non-bank)
+    "bajaj finance": "BAJFINANCE.NS",
+    "bajaj finserv": "BAJAJFINSV.NS",
+    "hdfc life": "HDFCLIFE.NS",
+    "sbi life": "SBILIFE.NS",
+    "lic": "LICI.NS",
+    "icici prudential": "ICICIPRULI.NS",
+    "muthoot": "MUTHOOTFIN.NS",
+    # Infra / Utilities
+    "larsen": "LT.NS", "l&t": "LT.NS", "larsen and toubro": "LT.NS",
+    "ntpc": "NTPC.NS",
+    "power grid": "POWERGRID.NS",
+    "tata power": "TATAPOWER.NS",
+    "adani power": "ADANIPOWER.NS",
+    # Cement / Materials
+    "ultratech": "ULTRACEMCO.NS", "ultratech cement": "ULTRACEMCO.NS",
+    "shree cement": "SHREECEM.NS",
+    "ambuja": "AMBUJACEM.NS",
+    "acc": "ACC.NS",
+    "grasim": "GRASIM.NS",
+    "tata steel": "TATASTEEL.NS",
+    "jsw steel": "JSWSTEEL.NS",
+    "hindalco": "HINDALCO.NS",
+    "vedanta": "VEDL.NS",
+    "jspl": "JINDALSTEL.NS",
+    # Paints / Consumer
+    "asian paints": "ASIANPAINT.NS",
+    "berger paints": "BERGEPAINT.NS",
+    "pidilite": "PIDILITIND.NS",
+    "havells": "HAVELLS.NS",
+    "voltas": "VOLTAS.NS",
+    "titan": "TITAN.NS",
+    "tanishq": "TITAN.NS",
+    # Retail / Consumer
+    "dmart": "DMART.NS", "avenue supermarts": "DMART.NS",
+    "trent": "TRENT.NS",
+    "zomato": "ZOMATO.NS",
+    "nykaa": "NYKAA.NS",
+    "policybazaar": "POLICYBZR.NS",
+    # Telecom
+    "airtel": "BHARTIARTL.NS", "bharti airtel": "BHARTIARTL.NS",
+    "jio": "RELIANCE.NS",  # Jio is part of Reliance
+    # IT Infrastructure
+    "hcl infosystems": "HCLISEC.NS",
+    "dixon": "DIXON.NS",
+}
+
+
+def _guess_ticker(query: str) -> Optional[str]:
+    """Try to find an Indian NSE ticker for a given name/query."""
+    q = query.lower().strip()
+    # Exact match
+    if q in INDIAN_TICKERS:
+        return INDIAN_TICKERS[q]
+    # Partial match
+    for key, ticker in INDIAN_TICKERS.items():
+        if key in q or q in key:
+            return ticker
+    return None
+
+
+# ── yahooquery resolution ───────────────────────────────────────────────────────
+
+def _yq_resolve(ticker: str, log_fn=None) -> Optional[CompanyMeta]:
+    """Try to resolve a ticker via yahooquery. Returns CompanyMeta or None."""
+    def log(msg):
+        if log_fn: log_fn(msg)
+    try:
+        import yahooquery as yq
+        t = yq.Ticker(ticker)
+        
+        # Yahooquery returns dicts with ticker as the key
+        price_data = t.price.get(ticker, {})
+        profile_data = t.summary_profile.get(ticker, {})
+        
+        if isinstance(price_data, str) or isinstance(profile_data, str):
+            # Yahooquery sometimes returns strings like "No fundamentals data found"
+            log(f"  ✗ yahooquery returned no data for {ticker}")
+            return None
+            
+        name = price_data.get("longName") or price_data.get("shortName", "")
+        if not name:
+            return None
+            
+        exchange_code = price_data.get("exchange", "")
+        currency = price_data.get("currency", "INR")
+        exchange_map = {
+            "NSI": "NSE (India)", "BSE": "BSE (India)",
+            "NMS": "NASDAQ (USA)", "NYQ": "NYSE (USA)",
+            "LSE": "LSE (UK)",
+        }
+        exchange_display = exchange_map.get(exchange_code, exchange_code or "Unknown")
+        country = profile_data.get("country", "India")
+        # Fiscal year end
+        fy_end = "March" if currency == "INR" else "December"
+
+        log(f"  ✓ yahooquery: {name} | {ticker} | {exchange_display}")
+        return CompanyMeta(
+            name=name,
+            ticker=ticker,
+            exchange=exchange_display,
+            currency=currency,
+            sector=profile_data.get("sector", ""),
+            industry=profile_data.get("industry", ""),
+            country=country,
+            description=profile_data.get("longBusinessSummary", ""),
+            website=profile_data.get("website", ""),
+            mkt_cap=price_data.get("marketCap"),
+            share_price=price_data.get("regularMarketPrice"),
+            is_listed=True,
+            source_note="Data sourced from Yahoo Finance (yahooquery).",
+            fiscal_year_end=fy_end,
+        )
+    except Exception as e:
+        log(f"  ✗ yahooquery failed for {ticker}: {e}")
+        return None
+
+
+# ── FMP resolution ────────────────────────────────────────────────────────────
+
+def _fmp_get(path: str, params: dict | None = None):
+    if not FMP_API_KEY:
+        return None
+    url = f"{FMP_BASE}{path}"
+    p = {"apikey": FMP_API_KEY}
+    if params: p.update(params)
+    try:
+        r = requests.get(url, params=p, timeout=REQUEST_TIMEOUT)
+        r.raise_for_status()
+        return r.json()
+    except Exception:
+        return None
+
+
+def _fmp_resolve(query: str, log_fn=None) -> Optional[CompanyMeta]:
+    """Try FMP search for global companies."""
+    def log(msg):
+        if log_fn: log_fn(msg)
+    if not FMP_API_KEY:
+        return None
+    log("  → Searching FMP …")
+    # Direct profile
+    profile = _fmp_get(f"/v3/profile/{query.upper()}")
+    if not (profile and isinstance(profile, list) and profile[0].get("symbol")):
+        # Search
+        results = _fmp_get("/v3/search", {"query": query, "limit": 5})
+        if results:
+            best = results[0]
+            profile = _fmp_get(f"/v3/profile/{best['symbol']}")
+    if not (profile and isinstance(profile, list)):
+        return None
+    p = profile[0]
+    if not p.get("companyName"):
+        return None
+    exchange = p.get("exchange", "") or p.get("exchangeShortName", "")
+    exchange_map = {"NSE":"NSE (India)","BSE":"BSE (India)","NASDAQ":"NASDAQ (USA)","NYSE":"NYSE (USA)"}
+    log(f"  ✓ FMP: {p.get('companyName')} ({p.get('symbol')})")
+    return CompanyMeta(
+        name=p.get("companyName",""),
+        ticker=p.get("symbol",""),
+        exchange=exchange_map.get(exchange, exchange),
+        currency=p.get("currency","USD"),
+        sector=p.get("sector",""),
+        industry=p.get("industry",""),
+        country=p.get("country",""),
+        description=p.get("description",""),
+        website=p.get("website",""),
+        mkt_cap=p.get("mktCap"),
+        share_price=p.get("price"),
+        is_listed=True,
+        source_note="Data sourced from FMP.",
+        fiscal_year_end="December",
+    )
+
+
+# ── yahooquery search by company name ──────────────────────────────────────────
+
+def _yq_search(query: str, log_fn=None) -> Optional[CompanyMeta]:
+    """Use yahooquery's search to find a ticker from a company name."""
+    def log(msg):
+        if log_fn: log_fn(msg)
+    try:
+        import yahooquery as yq
+        log(f"  → yahooquery search for '{query}' …")
+        results = yq.search(query)
+        quotes = results.get("quotes", [])
+        if not quotes:
+            return None
+        # Take the first relevant result
+        for q in quotes:
+            symbol = q.get("symbol", "")
+            qtype = q.get("quoteType", "")
+            if qtype in ("EQUITY", "MUTUALFUND") and symbol:
+                log(f"  → Found: {q.get('longname', symbol)} ({symbol})")
+                return _yq_resolve(symbol, log_fn)
+        return None
+    except Exception as e:
+        log(f"  ✗ yahooquery search failed: {e}")
+        return None
+
+
+# ── Public resolve() ──────────────────────────────────────────────────────────
+
+def resolve(query: str, log_fn=None) -> CompanyMeta:
+    """
+    Resolve a company name or ticker to CompanyMeta.
+    Source priority: Indian map → yahooquery direct → yahooquery search → FMP → unlisted
+    """
+    def log(msg):
+        if log_fn: log_fn(msg)
+
+    query = query.strip()
+    log(f"Resolving: '{query}' …")
+
+    # 1. Check Indian name map → resolve via yahooquery
+    indian_ticker = _guess_ticker(query)
+    if indian_ticker:
+        log(f"  ✓ Matches known Indian map → {indian_ticker}")
+        meta = _yq_resolve(indian_ticker, log_fn)
+        if meta: return meta
+
+    # 2. Try direct yahooquery (query as-is, and with .NS suffix)
+    for candidate in [query.upper(), f"{query.upper()}.NS", f"{query.upper()}.BO"]:
+        log(f"  → Trying direct ticker: {candidate}")
+        meta = _yq_resolve(candidate, log_fn)
+        if meta: return meta
+
+    # 3. yahooquery search
+    meta = _yq_search(query, log_fn)
+    if meta: return meta
+
+    # 4. FMP Search (for global companies)
+    meta = _fmp_resolve(query, log_fn)
+    if meta:
+        return meta
+
+    # 5. Unlisted fallback
+    log(f"  ⚠ '{query}' not found in any public market database.")
+    log("  → Treating as unlisted / private company.")
+    return CompanyMeta(
+        name=query, ticker="UNLISTED", exchange="Unlisted / Private",
+        currency="INR", sector="Unknown", industry="Unknown",
+        country="India", description="", website="",
+        mkt_cap=None, share_price=None, is_listed=False,
+        source_note=(
+            f"'{query}' could not be found. It may be unlisted, private, or use a different name. "
+            "Try using the exact BSE/NSE ticker (e.g. RELIANCE, TCS, HDFCBANK)."
+        ),
+        fiscal_year_end="March",
+    )

web_app/app.py

@@ -1,588 +1,588 @@
-"""
-web_app/app.py — Cloud-hosted Streamlit Portal for Financial Analysis.
-
-Provides a rich dark UI dashboard to scrape, compute, analyze and download reports
-for Excel, Word, and PowerPoint formats using user-supplied LLM credentials.
-"""
-import os
-import sys
-import time
-import datetime
-from pathlib import Path
-import streamlit as st
-
-# Setup sys.path so local packages can be resolved from parent directory
-PARENT_DIR = Path(__file__).parent.parent
-sys.path.insert(0, str(PARENT_DIR))
-
-# Import builders and pipeline
-from pipeline.resolver import resolve
-from pipeline.fetcher import fetch_all
-from pipeline.peer_selector import select_peers
-from pipeline.calculator import calculate
-from pipeline.analyst import generate_narrative
-from builders.build_excel import build_workbook
-from builders.build_docx import build_document
-from builders.build_pptx import build_presentation
-from web_app.database import save_audit_log, get_audit_logs
-
-# Streamlit Page Config
-st.set_page_config(
-    page_title="Financial Statement Analyzer Cloud Portal",
-    page_icon="📈",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# Dark Theme Glassmorphism Custom CSS Injection
-st.markdown("""
-<style>
-    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700&display=swap');
-    
-    html, body, [class*="css"] {
-        font-family: 'Inter', sans-serif;
-    }
-    
-    .main {
-        background-color: #0A0F1D;
-        color: #F3F4F6;
-    }
-    
-    /* Sidebar styling */
-    [data-testid="stSidebar"] {
-        background-color: #0F162A;
-        border-right: 1px solid #1E293B;
-    }
-    
-    /* Title and Subtitle */
-    .dashboard-title {
-        font-size: 2.5rem;
-        font-weight: 700;
-        background: linear-gradient(135deg, #60A5FA, #34D399);
-        -webkit-background-clip: text;
-        -webkit-text-fill-color: transparent;
-        margin-bottom: 0.2rem;
-    }
-    
-    .dashboard-subtitle {
-        font-size: 1.1rem;
-        font-weight: 300;
-        color: #9CA3AF;
-        margin-bottom: 2rem;
-    }
-    
-    /* Glowing card containers */
-    .glass-card {
-        background: rgba(30, 41, 59, 0.45);
-        border: 1px solid rgba(255, 255, 255, 0.08);
-        border-radius: 16px;
-        padding: 1.5rem;
-        backdrop-filter: blur(12px);
-        margin-bottom: 1.5rem;
-        box-shadow: 0 8px 32px 0 rgba(0, 0, 0, 0.37);
-    }
-    
-    .status-log {
-        background-color: #0D1117 !important;
-        border: 1px solid #1F242C !important;
-        border-radius: 8px !important;
-        color: #C9D1D9 !important;
-        font-family: 'Consolas', monospace !important;
-        padding: 12px;
-        max-height: 380px;
-        overflow-y: auto;
-    }
-    
-    /* Primary buttons styling */
-    div.stButton > button:first-child {
-        background: linear-gradient(135deg, #3B82F6, #10B981);
-        border: none;
-        color: white;
-        font-weight: 600;
-        padding: 0.6rem 2rem;
-        border-radius: 8px;
-        transition: transform 0.2s, box-shadow 0.2s;
-        box-shadow: 0 0 15px rgba(59, 130, 246, 0.4);
-    }
-    
-    div.stButton > button:first-child:hover {
-        transform: translateY(-2px);
-        box-shadow: 0 0 25px rgba(16, 185, 129, 0.6);
-        color: white;
-    }
-    
-    /* Checkbox layouts styling */
-    .stCheckbox > label {
-        color: #D1D5DB !important;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# ── Sidebar — Connect Your AI ──────────────────────────────────────────
-st.sidebar.markdown("""
-<h2 style='color:#60A5FA;margin-top:0;'>🤖 Connect Your AI</h2>
-<p style='font-size:0.8rem;color:#9CA3AF;'>
-  Bring your own key. Keys live only in your browser session — never stored on the server.
-</p>
-""", unsafe_allow_html=True)
-
-# ── FREE OPTIONS ───────────────────────────────────────────────────────
-st.sidebar.markdown("""
-<div style='background:rgba(16,185,129,0.08);border:1px solid rgba(16,185,129,0.25);
-            border-radius:8px;padding:0.6rem 0.85rem;margin-bottom:0.5rem;'>
-  <span style='font-size:0.75rem;font-weight:700;color:#34D399;letter-spacing:0.06em;'>🆓 FREE — NO CREDIT CARD</span>
-</div>
-""", unsafe_allow_html=True)
-
-st.sidebar.markdown("""
-**NVIDIA NIM** *(Llama 3.1 405B / Nemotron)*  
-<a href='https://build.nvidia.com/explore/discover' target='_blank' style='font-size:0.75rem;color:#34D399;'>Get free key → build.nvidia.com</a>
-""", unsafe_allow_html=True)
-nvidia_key = st.sidebar.text_input(
-    "NVIDIA API Key", type="password",
-    placeholder="nvapi-…",
-    value=os.environ.get("NVIDIA_API_KEY", ""),
-    help="Free credits available. Sign up at build.nvidia.com.",
-    key="inp_nvidia"
-)
-
-st.sidebar.markdown("""
-**Groq** *(Llama 3.3 70B — fast & free)*  
-<a href='https://console.groq.com/keys' target='_blank' style='font-size:0.75rem;color:#34D399;'>Get free key → console.groq.com</a>
-""", unsafe_allow_html=True)
-groq_key = st.sidebar.text_input(
-    "Groq API Key", type="password",
-    placeholder="gsk_…",
-    value=os.environ.get("GROQ_API_KEY", ""),
-    help="100% free. Sign up at console.groq.com → API Keys. No credit card.",
-    key="inp_groq"
-)
-
-st.sidebar.markdown("""
-**Google Gemini** *(Gemini 2.0 Flash — free tier)*  
-<a href='https://aistudio.google.com/app/apikey' target='_blank' style='font-size:0.75rem;color:#34D399;'>Get free key → aistudio.google.com</a>
-""", unsafe_allow_html=True)
-gemini_key = st.sidebar.text_input(
-    "Gemini API Key", type="password",
-    placeholder="AIza…",
-    value=os.environ.get("GEMINI_API_KEY", ""),
-    help="Free tier: 15 requests/min, 1M tokens/day. No billing needed.",
-    key="inp_gemini"
-)
-
-st.sidebar.markdown("""
-**Hugging Face** *(Serverless API)*  
-<a href='https://huggingface.co/settings/tokens' target='_blank' style='font-size:0.75rem;color:#34D399;'>Get free key → huggingface.co</a>
-""", unsafe_allow_html=True)
-
-_env_hf = os.environ.get("HF_API_KEY", "")
-if _env_hf:
-    hf_key = _env_hf
-    st.sidebar.success("✅ HF Key securely loaded from server secrets.")
-else:
-    hf_key = st.sidebar.text_input(
-        "Hugging Face API Key", type="password",
-        placeholder="hf_…",
-        help="Used for intelligent peer selection and DeepSeek analysis.",
-        key="inp_hf"
-    )
-
-# ── PAID OPTIONS ───────────────────────────────────────────────────────
-st.sidebar.markdown("""
-<div style='background:rgba(99,102,241,0.08);border:1px solid rgba(99,102,241,0.25);
-            border-radius:8px;padding:0.6rem 0.85rem;margin:0.5rem 0;'>
-  <span style='font-size:0.75rem;font-weight:700;color:#818CF8;letter-spacing:0.06em;'>💳 OPTIONAL — PAID APIS</span>
-</div>
-""", unsafe_allow_html=True)
-
-with st.sidebar.expander("Claude (Anthropic) — paid", expanded=False):
-    st.markdown("<a href='https://console.anthropic.com/' target='_blank' style='font-size:0.75rem;color:#818CF8;'>Get key → console.anthropic.com</a>", unsafe_allow_html=True)
-    claude_key = st.text_input(
-        "Claude API Key", type="password",
-        placeholder="sk-ant-…",
-        value=os.environ.get("CLAUDE_API_KEY", ""),
-        key="inp_claude"
-    )
-
-with st.sidebar.expander("ChatGPT (OpenAI) — paid", expanded=False):
-    st.markdown("<a href='https://platform.openai.com/api-keys' target='_blank' style='font-size:0.75rem;color:#818CF8;'>Get key → platform.openai.com</a>", unsafe_allow_html=True)
-    openai_key = st.text_input(
-        "OpenAI API Key", type="password",
-        placeholder="sk-proj-…",
-        value=os.environ.get("OPENAI_API_KEY", ""),
-        key="inp_openai"
-    )
-
-st.sidebar.markdown("---")
-st.sidebar.markdown("<h3 style='color:#34D399;'>👤 User Profile</h3>", unsafe_allow_html=True)
-username = st.sidebar.text_input("Username / Email", value="", placeholder="name@company.com", help="Mandatory for audit trail")
-
-# ── Main Dashboard Layout ──────────────────────────────────────────────
-st.markdown("<h1 class='dashboard-title'>📈 Financial Statement Analyzer</h1>", unsafe_allow_html=True)
-st.markdown("<p class='dashboard-subtitle'>Multi-model AI insights scraper and report compilation portal</p>", unsafe_allow_html=True)
-
-# Left Column: Inputs
-# Right Column: Exporters & Progress
-col1, col2 = st.columns([1, 1.2])
-
-with col1:
-    st.markdown("<div class='glass-card'>", unsafe_allow_html=True)
-    st.subheader("Analysis Parameters")
-    
-    # ── Ticker Search & Suggestion Logic ──
-    search_col1, search_col2 = st.columns([3, 1])
-    with search_col1:
-        search_query = st.text_input("Search Company Name or Ticker", placeholder="e.g. TCS, Reliance, AAPL, MSFT")
-    with search_col2:
-        st.markdown("<div style='margin-top:28px;'></div>", unsafe_allow_html=True)
-        search_clicked = st.button("🔍 Find", use_container_width=True)
-
-    ticker_input = search_query # default if no suggestions
-
-    if search_clicked and search_query:
-        with st.spinner("Searching..."):
-            try:
-                import yfinance as yf
-                res = yf.Search(search_query, max_results=8)
-                quotes = [q for q in res.quotes if q.get("quoteType") in ("EQUITY", "MUTUALFUND", "ETF")]
-                st.session_state["ticker_suggestions"] = quotes
-            except Exception:
-                st.session_state["ticker_suggestions"] = []
-
-    if "ticker_suggestions" in st.session_state and st.session_state["ticker_suggestions"]:
-        options = {}
-        for q in st.session_state["ticker_suggestions"]:
-            name = q.get('longname') or q.get('shortname') or q.get('symbol')
-            label = f"{name} ({q['symbol']}) - {q.get('exchange', 'Unknown')}"
-            options[label] = q['symbol']
-            
-        options[f"Use exact input: '{search_query}'"] = search_query
-        selected_label = st.selectbox("Confirm Company:", list(options.keys()))
-        ticker_input = options[selected_label]
-    
-    col_years, col_refresh = st.columns([1, 1])
-    with col_years:
-        analysis_years = st.selectbox("Analysis Horizon", options=[5, 7, 10], index=0)
-    with col_refresh:
-        force_refresh = st.checkbox("Bypass Cache (Force Scrape)", value=False)
-    
-    st.markdown("<b>Download Formats:</b>", unsafe_allow_html=True)
-    chk_excel = st.checkbox("Excel Analysis Workbook (.xlsx)", value=True)
-    chk_word  = st.checkbox("Word Summary Report (.docx)",     value=True)
-    chk_pptx  = st.checkbox("PowerPoint Pitch Deck (.pptx)",   value=True)
-    
-    # ── AI Engine selection — auto-disable if key not supplied
-    st.markdown("<b>AI Engines:</b>", unsafe_allow_html=True)
-
-    _hint = "<span style='font-size:0.72rem;color:#9CA3AF;'>(add key in sidebar →)</span>"
-
-    engines = []
-    
-    if st.checkbox(
-        "🆓 NVIDIA NIM — Llama 3.1 405B",
-        value=bool(nvidia_key),
-        disabled=not nvidia_key,
-        help="Free credits. Get key at build.nvidia.com"
-    ):
-        engines.append("nvidia")
-
-    if st.checkbox(
-        "🆓 Groq — Llama 3.3 70B",
-        value=bool(groq_key),
-        disabled=not groq_key,
-        help="Free. Get key at console.groq.com"
-    ):
-        engines.append("groq")
-        
-    if st.checkbox(
-        "🆓 Hugging Face — DeepSeek-R1",
-        value=bool(hf_key),
-        disabled=not hf_key,
-        help="Free via Hugging Face Inference API"
-    ):
-        engines.append("huggingface")
-
-    if st.checkbox(
-        "🆓 Gemini 2.0 Flash (Google)",
-        value=bool(gemini_key),
-        disabled=not gemini_key,
-        help="Free tier. Get key at aistudio.google.com"
-    ):
-        engines.append("gemini")
-
-    if st.checkbox(
-        "💳 Claude Haiku (Anthropic)",
-        value=False,
-        disabled=not claude_key,
-        help="Paid. Add key in sidebar to enable."
-    ):
-        engines.append("claude")
-
-    if st.checkbox(
-        "💳 ChatGPT — GPT-4o mini (OpenAI)",
-        value=False,
-        disabled=not openai_key,
-        help="Paid. Add key in sidebar to enable."
-    ):
-        engines.append("openai")
-
-    if not groq_key and not gemini_key:
-        st.info("💡 Add a **free Groq key** in the sidebar to enable AI narratives.", icon="🔑")
-
-    st.markdown("<br>", unsafe_allow_html=True)
-    start_btn = st.button("Begin Financial Scrape & Analysis")
-    st.markdown("</div>", unsafe_allow_html=True)
-
-with col2:
-    st.markdown("<div class='glass-card'>", unsafe_allow_html=True)
-    st.subheader("Execution Status & Logs")
-    
-    log_container = st.empty()
-    download_container = st.empty()
-    
-    log_container.code("Ready to analyze. Input a company and click the button above.", language="text")
-    st.markdown("</div>", unsafe_allow_html=True)
-
-# ── Analysis Runner ───────────────────────────────────────────────────
-if start_btn:
-    if not username or "@" not in username:
-        st.error("Please enter a valid email address in the User Profile section before analyzing.")
-    elif not ticker_input:
-        st.error("Please enter a stock ticker or company name.")
-    elif not (chk_excel or chk_word or chk_pptx):
-        st.error("Please select at least one download format checkbox.")
-    else:
-        # Inject credentials into all modules that cache them at import time
-        import config
-        import pipeline.analyst
-        import pipeline.key_validator
-        
-        config.GEMINI_API_KEY = gemini_key
-        config.CLAUDE_API_KEY = claude_key
-        config.OPENAI_API_KEY = openai_key
-        config.GROQ_API_KEY   = groq_key
-        config.NVIDIA_API_KEY = nvidia_key
-        config.HF_API_KEY     = hf_key
-        
-        os.environ["GEMINI_API_KEY"] = gemini_key
-        os.environ["CLAUDE_API_KEY"] = claude_key
-        os.environ["OPENAI_API_KEY"] = openai_key
-        os.environ["NVIDIA_API_KEY"] = nvidia_key
-        os.environ["HF_API_KEY"]     = hf_key
-        
-        pipeline.analyst.GEMINI_API_KEY = gemini_key
-        pipeline.analyst.CLAUDE_API_KEY = claude_key
-        pipeline.analyst.OPENAI_API_KEY = openai_key
-        pipeline.analyst.GROQ_API_KEY   = groq_key
-        pipeline.analyst.NVIDIA_API_KEY = nvidia_key
-        
-        pipeline.key_validator.GEMINI_API_KEY = gemini_key
-        pipeline.key_validator.CLAUDE_API_KEY = claude_key
-        pipeline.key_validator.OPENAI_API_KEY = openai_key
-        pipeline.key_validator.GROQ_API_KEY   = groq_key
-        pipeline.key_validator.NVIDIA_API_KEY = nvidia_key
-
-        # Push into environment for any sub-process
-        for k, v in [("GEMINI_API_KEY", gemini_key), ("CLAUDE_API_KEY", claude_key),
-                     ("OPENAI_API_KEY", openai_key), ("GROQ_API_KEY", groq_key), 
-                     ("NVIDIA_API_KEY", nvidia_key), ("HF_API_KEY", hf_key)]:
-            if v: os.environ[k] = v
-        
-        # Validate that selected engines have keys
-        failed_validation = False
-        engine_key_map = {
-            "nvidia": (nvidia_key, "NVIDIA"),
-            "groq":   (groq_key,   "Groq"),
-            "gemini": (gemini_key, "Gemini"),
-            "claude": (claude_key, "Claude"),
-            "openai": (openai_key, "ChatGPT"),
-            "huggingface": (hf_key, "Hugging Face"),
-        }
-        for engine in engines:
-            key, label = engine_key_map[engine]
-            if not key:
-                st.error(f"Please supply a {label} API Key or uncheck the {label} model.")
-                failed_validation = True
-            
-        if not failed_validation:
-            logs = []
-            
-            def log_fn(msg):
-                logs.append(msg)
-                log_container.code("\n".join(logs), language="text")
-                
-            try:
-                log_fn("="*60)
-                log_fn(f"Starting analysis for: '{ticker_input}'")
-                log_fn(f"Time: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
-                log_fn(f"Formats: " + ", ".join([f for f, ok in [("Excel", chk_excel), ("Word", chk_word), ("PowerPoint", chk_pptx)] if ok]))
-                log_fn(f"AI Models: {engines or ['None (Data Only)']}")
-                log_fn("="*60)
-                
-                # 1. Resolve Company Info
-                log_fn("\n[1/7] Resolving company metadata …")
-                meta = resolve(ticker_input, log_fn=log_fn)
-                log_fn(f"  ✓ {meta.name} | {meta.ticker} | {meta.exchange}")
-                
-                # 2. Fetch Statements
-                log_fn("\n[2/7] Scrape financial statements …")
-                fin_data = fetch_all(meta, force_refresh=force_refresh, log_fn=log_fn)
-                
-                # 3. Peers Comparison Selection
-                log_fn("\n[3/7] Benchmarking peer selection …")
-                fin_data["peers"] = select_peers(meta, log_fn=log_fn)
-                
-                # 4. Math ratios calculations
-                log_fn("\n[4/7] Computing financial ratios & DuPont decomposition …")
-                fin_data = calculate(fin_data, meta.to_dict())
-                log_fn("  ✓ Completed ratio math computations")
-                
-                # 5. AI Synthesis Narrative
-                log_fn("\n[5/7] Requesting AI Model narrative generation …")
-                narrative = generate_narrative(fin_data, meta.to_dict(), engines, log_fn=log_fn)
-                fin_data.update(narrative)
-                
-                # 6. Generate outputs
-                log_fn("\n[6/7] Building output report files …")
-                fin_data["company"] = {
-                    "name": meta.name, "ticker": meta.ticker, "exchange": meta.exchange,
-                    "currency": fin_data.get("currency", meta.currency),
-                    "units": fin_data.get("units", "millions"),
-                    "fiscal_year_end": "",
-                    "industry": meta.industry, "sector": meta.sector,
-                    "business_description": meta.description,
-                    "report_date": datetime.date.today().isoformat(),
-                    "analysis_period_years": len(fin_data.get("years", [])),
-                    "share_price": meta.share_price, "market_cap": meta.mkt_cap,
-                }
-                fin_data["models_used"] = engines
-                fin_data.setdefault("sources", [])
-                
-                ticker_safe = meta.ticker.replace(".", "_").replace("/", "_")
-                timestamp_suffix = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-                
-                # Exporters path settings
-                excel_bytes = None
-                word_bytes = None
-                pptx_bytes = None
-                
-                out_dir = PARENT_DIR / "outputs"
-                os.makedirs(str(out_dir), exist_ok=True)
-                
-                # Build files in memory / disk
-                if chk_excel:
-                    xlsx_path = out_dir / f"{ticker_safe}_financial_analysis_{timestamp_suffix}.xlsx"
-                    build_workbook(fin_data, str(xlsx_path))
-                    with open(xlsx_path, "rb") as f:
-                        excel_bytes = f.read()
-                    log_fn(f"  ✓ Created Excel analysis: {xlsx_path.name}")
-                    
-                if chk_word:
-                    docx_path = out_dir / f"{ticker_safe}_financial_analysis_{timestamp_suffix}.docx"
-                    build_document(fin_data, str(docx_path))
-                    with open(docx_path, "rb") as f:
-                        word_bytes = f.read()
-                    log_fn(f"  ✓ Created Word summary: {docx_path.name}")
-                    
-                if chk_pptx:
-                    pptx_path = out_dir / f"{ticker_safe}_financial_analysis_{timestamp_suffix}.pptx"
-                    build_presentation(fin_data, str(pptx_path))
-                    with open(pptx_path, "rb") as f:
-                        pptx_bytes = f.read()
-                    log_fn(f"  ✓ Created PowerPoint Presentation: {pptx_path.name}")
-                
-                log_fn(f"\n[7/7] ✅ Financial analysis process complete!")
-                log_fn("="*60)
-                
-                # ── Generate download buttons in UI
-                with download_container.container():
-                    st.success("Download ready!")
-                    dl_col1, dl_col2, dl_col3, dl_col4 = st.columns(4)
-                    
-                    download_files = []
-                    
-                    if excel_bytes:
-                        dl_col1.download_button(
-                            label="📥 Download Excel",
-                            data=excel_bytes,
-                            file_name=f"{ticker_safe}_analysis.xlsx",
-                            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
-                            use_container_width=True
-                        )
-                        download_files.append((f"{ticker_safe}_analysis.xlsx", excel_bytes))
-                        
-                    if word_bytes:
-                        dl_col2.download_button(
-                            label="📄 Download Word",
-                            data=word_bytes,
-                            file_name=f"{ticker_safe}_report.docx",
-                            mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
-                            use_container_width=True
-                        )
-                        download_files.append((f"{ticker_safe}_report.docx", word_bytes))
-                        
-                    if pptx_bytes:
-                        dl_col3.download_button(
-                            label="📊 Download PPTX",
-                            data=pptx_bytes,
-                            file_name=f"{ticker_safe}_deck.pptx",
-                            mime="application/vnd.openxmlformats-officedocument.presentationml.presentation",
-                            use_container_width=True
-                        )
-                        download_files.append((f"{ticker_safe}_deck.pptx", pptx_bytes))
-
-                    if len(download_files) > 1:
-                        import zipfile
-                        import io
-                        zip_buffer = io.BytesIO()
-                        with zipfile.ZipFile(zip_buffer, "a", zipfile.ZIP_DEFLATED, False) as zip_file:
-                            for file_name, data in download_files:
-                                zip_file.writestr(file_name, data)
-                        dl_col4.download_button(
-                            label="📦 Download All (ZIP)",
-                            data=zip_buffer.getvalue(),
-                            file_name=f"{ticker_safe}_all_reports.zip",
-                            mime="application/zip",
-                            use_container_width=True
-                        )
-                # Write search activity log to audit table
-                selected_formats = [f for f, ok in [("xlsx", chk_excel), ("docx", chk_word), ("pptx", chk_pptx)] if ok]
-                save_audit_log(
-                    user_email=username,
-                    company_ticker=meta.ticker,
-                    company_name=meta.name,
-                    selected_formats=selected_formats,
-                    ai_models=engines,
-                    status="completed"
-                )
-                
-            except Exception as e:
-                log_fn(f"\n❌ Pipeline failed: {e}")
-                import traceback
-                log_fn(traceback.format_exc())
-                st.error(f"Analysis failed: {e}")
-                
-                # Save audit log failure
-                try:
-                    selected_formats = [f for f, ok in [("xlsx", chk_excel), ("docx", chk_word), ("pptx", chk_pptx)] if ok]
-                    save_audit_log(
-                        user_email=username,
-                        company_ticker=ticker_input,
-                        company_name="N/A",
-                        selected_formats=selected_formats,
-                        ai_models=engines,
-                        status="failed"
-                    )
-                except:
-                    pass
-
-# ── Audit History Panel Table ───────────────────────────────────────────
-st.markdown("---")
-st.markdown("<h3 style='color:#60A5FA;margin-top:0;'>📜 Query Audit Trail & History</h3>", unsafe_allow_html=True)
-st.markdown("<p style='font-size:0.85rem;color:#9CA3AF;'>Past analysis runs requested by team members.</p>", unsafe_allow_html=True)
-
-history_logs = get_audit_logs()
-if history_logs:
-    st.table(history_logs)
-else:
-    st.info("No queries have been run yet.")
+"""
+web_app/app.py — Cloud-hosted Streamlit Portal for Financial Analysis.
+
+Provides a rich dark UI dashboard to scrape, compute, analyze and download reports
+for Excel, Word, and PowerPoint formats using user-supplied LLM credentials.
+"""
+import os
+import sys
+import time
+import datetime
+from pathlib import Path
+import streamlit as st
+
+# Setup sys.path so local packages can be resolved from parent directory
+PARENT_DIR = Path(__file__).parent.parent
+sys.path.insert(0, str(PARENT_DIR))
+
+# Import builders and pipeline
+from pipeline.resolver import resolve
+from pipeline.fetcher import fetch_all
+from pipeline.peer_selector import select_peers
+from pipeline.calculator import calculate
+from pipeline.analyst import generate_narrative
+from builders.build_excel import build_workbook
+from builders.build_docx import build_document
+from builders.build_pptx import build_presentation
+from web_app.database import save_audit_log, get_audit_logs
+
+# Streamlit Page Config
+st.set_page_config(
+    page_title="Financial Statement Analyzer Cloud Portal",
+    page_icon="📈",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Dark Theme Glassmorphism Custom CSS Injection
+st.markdown("""
+<style>
+    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600;700&display=swap');
+    
+    html, body, [class*="css"] {
+        font-family: 'Inter', sans-serif;
+    }
+    
+    .main {
+        background-color: #0A0F1D;
+        color: #F3F4F6;
+    }
+    
+    /* Sidebar styling */
+    [data-testid="stSidebar"] {
+        background-color: #0F162A;
+        border-right: 1px solid #1E293B;
+    }
+    
+    /* Title and Subtitle */
+    .dashboard-title {
+        font-size: 2.5rem;
+        font-weight: 700;
+        background: linear-gradient(135deg, #60A5FA, #34D399);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        margin-bottom: 0.2rem;
+    }
+    
+    .dashboard-subtitle {
+        font-size: 1.1rem;
+        font-weight: 300;
+        color: #9CA3AF;
+        margin-bottom: 2rem;
+    }
+    
+    /* Glowing card containers */
+    .glass-card {
+        background: rgba(30, 41, 59, 0.45);
+        border: 1px solid rgba(255, 255, 255, 0.08);
+        border-radius: 16px;
+        padding: 1.5rem;
+        backdrop-filter: blur(12px);
+        margin-bottom: 1.5rem;
+        box-shadow: 0 8px 32px 0 rgba(0, 0, 0, 0.37);
+    }
+    
+    .status-log {
+        background-color: #0D1117 !important;
+        border: 1px solid #1F242C !important;
+        border-radius: 8px !important;
+        color: #C9D1D9 !important;
+        font-family: 'Consolas', monospace !important;
+        padding: 12px;
+        max-height: 380px;
+        overflow-y: auto;
+    }
+    
+    /* Primary buttons styling */
+    div.stButton > button:first-child {
+        background: linear-gradient(135deg, #3B82F6, #10B981);
+        border: none;
+        color: white;
+        font-weight: 600;
+        padding: 0.6rem 2rem;
+        border-radius: 8px;
+        transition: transform 0.2s, box-shadow 0.2s;
+        box-shadow: 0 0 15px rgba(59, 130, 246, 0.4);
+    }
+    
+    div.stButton > button:first-child:hover {
+        transform: translateY(-2px);
+        box-shadow: 0 0 25px rgba(16, 185, 129, 0.6);
+        color: white;
+    }
+    
+    /* Checkbox layouts styling */
+    .stCheckbox > label {
+        color: #D1D5DB !important;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# ── Sidebar — Connect Your AI ──────────────────────────────────────────
+st.sidebar.markdown("""
+<h2 style='color:#60A5FA;margin-top:0;'>🤖 Connect Your AI</h2>
+<p style='font-size:0.8rem;color:#9CA3AF;'>
+  Bring your own key. Keys live only in your browser session — never stored on the server.
+</p>
+""", unsafe_allow_html=True)
+
+# ── FREE OPTIONS ───────────────────────────────────────────────────────
+st.sidebar.markdown("""
+<div style='background:rgba(16,185,129,0.08);border:1px solid rgba(16,185,129,0.25);
+            border-radius:8px;padding:0.6rem 0.85rem;margin-bottom:0.5rem;'>
+  <span style='font-size:0.75rem;font-weight:700;color:#34D399;letter-spacing:0.06em;'>🆓 FREE — NO CREDIT CARD</span>
+</div>
+""", unsafe_allow_html=True)
+
+st.sidebar.markdown("""
+**NVIDIA NIM** *(Llama 3.1 405B / Nemotron)*  
+<a href='https://build.nvidia.com/explore/discover' target='_blank' style='font-size:0.75rem;color:#34D399;'>Get free key → build.nvidia.com</a>
+""", unsafe_allow_html=True)
+nvidia_key = st.sidebar.text_input(
+    "NVIDIA API Key", type="password",
+    placeholder="nvapi-…",
+    value=os.environ.get("NVIDIA_API_KEY", ""),
+    help="Free credits available. Sign up at build.nvidia.com.",
+    key="inp_nvidia"
+)
+
+st.sidebar.markdown("""
+**Groq** *(Llama 3.3 70B — fast & free)*  
+<a href='https://console.groq.com/keys' target='_blank' style='font-size:0.75rem;color:#34D399;'>Get free key → console.groq.com</a>
+""", unsafe_allow_html=True)
+groq_key = st.sidebar.text_input(
+    "Groq API Key", type="password",
+    placeholder="gsk_…",
+    value=os.environ.get("GROQ_API_KEY", ""),
+    help="100% free. Sign up at console.groq.com → API Keys. No credit card.",
+    key="inp_groq"
+)
+
+st.sidebar.markdown("""
+**Google Gemini** *(Gemini 2.0 Flash — free tier)*  
+<a href='https://aistudio.google.com/app/apikey' target='_blank' style='font-size:0.75rem;color:#34D399;'>Get free key → aistudio.google.com</a>
+""", unsafe_allow_html=True)
+gemini_key = st.sidebar.text_input(
+    "Gemini API Key", type="password",
+    placeholder="AIza…",
+    value=os.environ.get("GEMINI_API_KEY", ""),
+    help="Free tier: 15 requests/min, 1M tokens/day. No billing needed.",
+    key="inp_gemini"
+)
+
+st.sidebar.markdown("""
+**Hugging Face** *(Serverless API)*  
+<a href='https://huggingface.co/settings/tokens' target='_blank' style='font-size:0.75rem;color:#34D399;'>Get free key → huggingface.co</a>
+""", unsafe_allow_html=True)
+
+_env_hf = os.environ.get("HF_API_KEY", "")
+if _env_hf:
+    hf_key = _env_hf
+    st.sidebar.success("✅ HF Key securely loaded from server secrets.")
+else:
+    hf_key = st.sidebar.text_input(
+        "Hugging Face API Key", type="password",
+        placeholder="hf_…",
+        help="Used for intelligent peer selection and DeepSeek analysis.",
+        key="inp_hf"
+    )
+
+# ── PAID OPTIONS ───────────────────────────────────────────────────────
+st.sidebar.markdown("""
+<div style='background:rgba(99,102,241,0.08);border:1px solid rgba(99,102,241,0.25);
+            border-radius:8px;padding:0.6rem 0.85rem;margin:0.5rem 0;'>
+  <span style='font-size:0.75rem;font-weight:700;color:#818CF8;letter-spacing:0.06em;'>💳 OPTIONAL — PAID APIS</span>
+</div>
+""", unsafe_allow_html=True)
+
+with st.sidebar.expander("Claude (Anthropic) — paid", expanded=False):
+    st.markdown("<a href='https://console.anthropic.com/' target='_blank' style='font-size:0.75rem;color:#818CF8;'>Get key → console.anthropic.com</a>", unsafe_allow_html=True)
+    claude_key = st.text_input(
+        "Claude API Key", type="password",
+        placeholder="sk-ant-…",
+        value=os.environ.get("CLAUDE_API_KEY", ""),
+        key="inp_claude"
+    )
+
+with st.sidebar.expander("ChatGPT (OpenAI) — paid", expanded=False):
+    st.markdown("<a href='https://platform.openai.com/api-keys' target='_blank' style='font-size:0.75rem;color:#818CF8;'>Get key → platform.openai.com</a>", unsafe_allow_html=True)
+    openai_key = st.text_input(
+        "OpenAI API Key", type="password",
+        placeholder="sk-proj-…",
+        value=os.environ.get("OPENAI_API_KEY", ""),
+        key="inp_openai"
+    )
+
+st.sidebar.markdown("---")
+st.sidebar.markdown("<h3 style='color:#34D399;'>👤 User Profile</h3>", unsafe_allow_html=True)
+username = st.sidebar.text_input("Username / Email", value="", placeholder="name@company.com", help="Mandatory for audit trail")
+
+# ── Main Dashboard Layout ──────────────────────────────────────────────
+st.markdown("<h1 class='dashboard-title'>📈 Financial Statement Analyzer</h1>", unsafe_allow_html=True)
+st.markdown("<p class='dashboard-subtitle'>Multi-model AI insights scraper and report compilation portal</p>", unsafe_allow_html=True)
+
+# Left Column: Inputs
+# Right Column: Exporters & Progress
+col1, col2 = st.columns([1, 1.2])
+
+with col1:
+    st.markdown("<div class='glass-card'>", unsafe_allow_html=True)
+    st.subheader("Analysis Parameters")
+    
+    # ── Ticker Search & Suggestion Logic ──
+    search_col1, search_col2 = st.columns([3, 1])
+    with search_col1:
+        search_query = st.text_input("Search Company Name or Ticker", placeholder="e.g. TCS, Reliance, AAPL, MSFT")
+    with search_col2:
+        st.markdown("<div style='margin-top:28px;'></div>", unsafe_allow_html=True)
+        search_clicked = st.button("🔍 Find", use_container_width=True)
+
+    ticker_input = search_query # default if no suggestions
+
+    if search_clicked and search_query:
+        with st.spinner("Searching..."):
+            try:
+                import yahooquery as yf
+                res = yf.Search(search_query, max_results=8)
+                quotes = [q for q in res.quotes if q.get("quoteType") in ("EQUITY", "MUTUALFUND", "ETF")]
+                st.session_state["ticker_suggestions"] = quotes
+            except Exception:
+                st.session_state["ticker_suggestions"] = []
+
+    if "ticker_suggestions" in st.session_state and st.session_state["ticker_suggestions"]:
+        options = {}
+        for q in st.session_state["ticker_suggestions"]:
+            name = q.get('longname') or q.get('shortname') or q.get('symbol')
+            label = f"{name} ({q['symbol']}) - {q.get('exchange', 'Unknown')}"
+            options[label] = q['symbol']
+            
+        options[f"Use exact input: '{search_query}'"] = search_query
+        selected_label = st.selectbox("Confirm Company:", list(options.keys()))
+        ticker_input = options[selected_label]
+    
+    col_years, col_refresh = st.columns([1, 1])
+    with col_years:
+        analysis_years = st.selectbox("Analysis Horizon", options=[5, 7, 10], index=0)
+    with col_refresh:
+        force_refresh = st.checkbox("Bypass Cache (Force Scrape)", value=False)
+    
+    st.markdown("<b>Download Formats:</b>", unsafe_allow_html=True)
+    chk_excel = st.checkbox("Excel Analysis Workbook (.xlsx)", value=True)
+    chk_word  = st.checkbox("Word Summary Report (.docx)",     value=True)
+    chk_pptx  = st.checkbox("PowerPoint Pitch Deck (.pptx)",   value=True)
+    
+    # ── AI Engine selection — auto-disable if key not supplied
+    st.markdown("<b>AI Engines:</b>", unsafe_allow_html=True)
+
+    _hint = "<span style='font-size:0.72rem;color:#9CA3AF;'>(add key in sidebar →)</span>"
+
+    engines = []
+    
+    if st.checkbox(
+        "🆓 NVIDIA NIM — Llama 3.1 405B",
+        value=bool(nvidia_key),
+        disabled=not nvidia_key,
+        help="Free credits. Get key at build.nvidia.com"
+    ):
+        engines.append("nvidia")
+
+    if st.checkbox(
+        "🆓 Groq — Llama 3.3 70B",
+        value=bool(groq_key),
+        disabled=not groq_key,
+        help="Free. Get key at console.groq.com"
+    ):
+        engines.append("groq")
+        
+    if st.checkbox(
+        "🆓 Hugging Face — DeepSeek-R1",
+        value=bool(hf_key),
+        disabled=not hf_key,
+        help="Free via Hugging Face Inference API"
+    ):
+        engines.append("huggingface")
+
+    if st.checkbox(
+        "🆓 Gemini 2.0 Flash (Google)",
+        value=bool(gemini_key),
+        disabled=not gemini_key,
+        help="Free tier. Get key at aistudio.google.com"
+    ):
+        engines.append("gemini")
+
+    if st.checkbox(
+        "💳 Claude Haiku (Anthropic)",
+        value=False,
+        disabled=not claude_key,
+        help="Paid. Add key in sidebar to enable."
+    ):
+        engines.append("claude")
+
+    if st.checkbox(
+        "💳 ChatGPT — GPT-4o mini (OpenAI)",
+        value=False,
+        disabled=not openai_key,
+        help="Paid. Add key in sidebar to enable."
+    ):
+        engines.append("openai")
+
+    if not groq_key and not gemini_key:
+        st.info("💡 Add a **free Groq key** in the sidebar to enable AI narratives.", icon="🔑")
+
+    st.markdown("<br>", unsafe_allow_html=True)
+    start_btn = st.button("Begin Financial Scrape & Analysis")
+    st.markdown("</div>", unsafe_allow_html=True)
+
+with col2:
+    st.markdown("<div class='glass-card'>", unsafe_allow_html=True)
+    st.subheader("Execution Status & Logs")
+    
+    log_container = st.empty()
+    download_container = st.empty()
+    
+    log_container.code("Ready to analyze. Input a company and click the button above.", language="text")
+    st.markdown("</div>", unsafe_allow_html=True)
+
+# ── Analysis Runner ───────────────────────────────────────────────────
+if start_btn:
+    if not username or "@" not in username:
+        st.error("Please enter a valid email address in the User Profile section before analyzing.")
+    elif not ticker_input:
+        st.error("Please enter a stock ticker or company name.")
+    elif not (chk_excel or chk_word or chk_pptx):
+        st.error("Please select at least one download format checkbox.")
+    else:
+        # Inject credentials into all modules that cache them at import time
+        import config
+        import pipeline.analyst
+        import pipeline.key_validator
+        
+        config.GEMINI_API_KEY = gemini_key
+        config.CLAUDE_API_KEY = claude_key
+        config.OPENAI_API_KEY = openai_key
+        config.GROQ_API_KEY   = groq_key
+        config.NVIDIA_API_KEY = nvidia_key
+        config.HF_API_KEY     = hf_key
+        
+        os.environ["GEMINI_API_KEY"] = gemini_key
+        os.environ["CLAUDE_API_KEY"] = claude_key
+        os.environ["OPENAI_API_KEY"] = openai_key
+        os.environ["NVIDIA_API_KEY"] = nvidia_key
+        os.environ["HF_API_KEY"]     = hf_key
+        
+        pipeline.analyst.GEMINI_API_KEY = gemini_key
+        pipeline.analyst.CLAUDE_API_KEY = claude_key
+        pipeline.analyst.OPENAI_API_KEY = openai_key
+        pipeline.analyst.GROQ_API_KEY   = groq_key
+        pipeline.analyst.NVIDIA_API_KEY = nvidia_key
+        
+        pipeline.key_validator.GEMINI_API_KEY = gemini_key
+        pipeline.key_validator.CLAUDE_API_KEY = claude_key
+        pipeline.key_validator.OPENAI_API_KEY = openai_key
+        pipeline.key_validator.GROQ_API_KEY   = groq_key
+        pipeline.key_validator.NVIDIA_API_KEY = nvidia_key
+
+        # Push into environment for any sub-process
+        for k, v in [("GEMINI_API_KEY", gemini_key), ("CLAUDE_API_KEY", claude_key),
+                     ("OPENAI_API_KEY", openai_key), ("GROQ_API_KEY", groq_key), 
+                     ("NVIDIA_API_KEY", nvidia_key), ("HF_API_KEY", hf_key)]:
+            if v: os.environ[k] = v
+        
+        # Validate that selected engines have keys
+        failed_validation = False
+        engine_key_map = {
+            "nvidia": (nvidia_key, "NVIDIA"),
+            "groq":   (groq_key,   "Groq"),
+            "gemini": (gemini_key, "Gemini"),
+            "claude": (claude_key, "Claude"),
+            "openai": (openai_key, "ChatGPT"),
+            "huggingface": (hf_key, "Hugging Face"),
+        }
+        for engine in engines:
+            key, label = engine_key_map[engine]
+            if not key:
+                st.error(f"Please supply a {label} API Key or uncheck the {label} model.")
+                failed_validation = True
+            
+        if not failed_validation:
+            logs = []
+            
+            def log_fn(msg):
+                logs.append(msg)
+                log_container.code("\n".join(logs), language="text")
+                
+            try:
+                log_fn("="*60)
+                log_fn(f"Starting analysis for: '{ticker_input}'")
+                log_fn(f"Time: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+                log_fn(f"Formats: " + ", ".join([f for f, ok in [("Excel", chk_excel), ("Word", chk_word), ("PowerPoint", chk_pptx)] if ok]))
+                log_fn(f"AI Models: {engines or ['None (Data Only)']}")
+                log_fn("="*60)
+                
+                # 1. Resolve Company Info
+                log_fn("\n[1/7] Resolving company metadata …")
+                meta = resolve(ticker_input, log_fn=log_fn)
+                log_fn(f"  ✓ {meta.name} | {meta.ticker} | {meta.exchange}")
+                
+                # 2. Fetch Statements
+                log_fn("\n[2/7] Scrape financial statements …")
+                fin_data = fetch_all(meta, force_refresh=force_refresh, log_fn=log_fn)
+                
+                # 3. Peers Comparison Selection
+                log_fn("\n[3/7] Benchmarking peer selection …")
+                fin_data["peers"] = select_peers(meta, log_fn=log_fn)
+                
+                # 4. Math ratios calculations
+                log_fn("\n[4/7] Computing financial ratios & DuPont decomposition …")
+                fin_data = calculate(fin_data, meta.to_dict())
+                log_fn("  ✓ Completed ratio math computations")
+                
+                # 5. AI Synthesis Narrative
+                log_fn("\n[5/7] Requesting AI Model narrative generation …")
+                narrative = generate_narrative(fin_data, meta.to_dict(), engines, log_fn=log_fn)
+                fin_data.update(narrative)
+                
+                # 6. Generate outputs
+                log_fn("\n[6/7] Building output report files …")
+                fin_data["company"] = {
+                    "name": meta.name, "ticker": meta.ticker, "exchange": meta.exchange,
+                    "currency": fin_data.get("currency", meta.currency),
+                    "units": fin_data.get("units", "millions"),
+                    "fiscal_year_end": "",
+                    "industry": meta.industry, "sector": meta.sector,
+                    "business_description": meta.description,
+                    "report_date": datetime.date.today().isoformat(),
+                    "analysis_period_years": len(fin_data.get("years", [])),
+                    "share_price": meta.share_price, "market_cap": meta.mkt_cap,
+                }
+                fin_data["models_used"] = engines
+                fin_data.setdefault("sources", [])
+                
+                ticker_safe = meta.ticker.replace(".", "_").replace("/", "_")
+                timestamp_suffix = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+                
+                # Exporters path settings
+                excel_bytes = None
+                word_bytes = None
+                pptx_bytes = None
+                
+                out_dir = PARENT_DIR / "outputs"
+                os.makedirs(str(out_dir), exist_ok=True)
+                
+                # Build files in memory / disk
+                if chk_excel:
+                    xlsx_path = out_dir / f"{ticker_safe}_financial_analysis_{timestamp_suffix}.xlsx"
+                    build_workbook(fin_data, str(xlsx_path))
+                    with open(xlsx_path, "rb") as f:
+                        excel_bytes = f.read()
+                    log_fn(f"  ✓ Created Excel analysis: {xlsx_path.name}")
+                    
+                if chk_word:
+                    docx_path = out_dir / f"{ticker_safe}_financial_analysis_{timestamp_suffix}.docx"
+                    build_document(fin_data, str(docx_path))
+                    with open(docx_path, "rb") as f:
+                        word_bytes = f.read()
+                    log_fn(f"  ✓ Created Word summary: {docx_path.name}")
+                    
+                if chk_pptx:
+                    pptx_path = out_dir / f"{ticker_safe}_financial_analysis_{timestamp_suffix}.pptx"
+                    build_presentation(fin_data, str(pptx_path))
+                    with open(pptx_path, "rb") as f:
+                        pptx_bytes = f.read()
+                    log_fn(f"  ✓ Created PowerPoint Presentation: {pptx_path.name}")
+                
+                log_fn(f"\n[7/7] ✅ Financial analysis process complete!")
+                log_fn("="*60)
+                
+                # ── Generate download buttons in UI
+                with download_container.container():
+                    st.success("Download ready!")
+                    dl_col1, dl_col2, dl_col3, dl_col4 = st.columns(4)
+                    
+                    download_files = []
+                    
+                    if excel_bytes:
+                        dl_col1.download_button(
+                            label="📥 Download Excel",
+                            data=excel_bytes,
+                            file_name=f"{ticker_safe}_analysis.xlsx",
+                            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+                            use_container_width=True
+                        )
+                        download_files.append((f"{ticker_safe}_analysis.xlsx", excel_bytes))
+                        
+                    if word_bytes:
+                        dl_col2.download_button(
+                            label="📄 Download Word",
+                            data=word_bytes,
+                            file_name=f"{ticker_safe}_report.docx",
+                            mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+                            use_container_width=True
+                        )
+                        download_files.append((f"{ticker_safe}_report.docx", word_bytes))
+                        
+                    if pptx_bytes:
+                        dl_col3.download_button(
+                            label="📊 Download PPTX",
+                            data=pptx_bytes,
+                            file_name=f"{ticker_safe}_deck.pptx",
+                            mime="application/vnd.openxmlformats-officedocument.presentationml.presentation",
+                            use_container_width=True
+                        )
+                        download_files.append((f"{ticker_safe}_deck.pptx", pptx_bytes))
+
+                    if len(download_files) > 1:
+                        import zipfile
+                        import io
+                        zip_buffer = io.BytesIO()
+                        with zipfile.ZipFile(zip_buffer, "a", zipfile.ZIP_DEFLATED, False) as zip_file:
+                            for file_name, data in download_files:
+                                zip_file.writestr(file_name, data)
+                        dl_col4.download_button(
+                            label="📦 Download All (ZIP)",
+                            data=zip_buffer.getvalue(),
+                            file_name=f"{ticker_safe}_all_reports.zip",
+                            mime="application/zip",
+                            use_container_width=True
+                        )
+                # Write search activity log to audit table
+                selected_formats = [f for f, ok in [("xlsx", chk_excel), ("docx", chk_word), ("pptx", chk_pptx)] if ok]
+                save_audit_log(
+                    user_email=username,
+                    company_ticker=meta.ticker,
+                    company_name=meta.name,
+                    selected_formats=selected_formats,
+                    ai_models=engines,
+                    status="completed"
+                )
+                
+            except Exception as e:
+                log_fn(f"\n❌ Pipeline failed: {e}")
+                import traceback
+                log_fn(traceback.format_exc())
+                st.error(f"Analysis failed: {e}")
+                
+                # Save audit log failure
+                try:
+                    selected_formats = [f for f, ok in [("xlsx", chk_excel), ("docx", chk_word), ("pptx", chk_pptx)] if ok]
+                    save_audit_log(
+                        user_email=username,
+                        company_ticker=ticker_input,
+                        company_name="N/A",
+                        selected_formats=selected_formats,
+                        ai_models=engines,
+                        status="failed"
+                    )
+                except:
+                    pass
+
+# ── Audit History Panel Table ───────────────────────────────────────────
+st.markdown("---")
+st.markdown("<h3 style='color:#60A5FA;margin-top:0;'>📜 Query Audit Trail & History</h3>", unsafe_allow_html=True)
+st.markdown("<p style='font-size:0.85rem;color:#9CA3AF;'>Past analysis runs requested by team members.</p>", unsafe_allow_html=True)
+
+history_logs = get_audit_logs()
+if history_logs:
+    st.table(history_logs)
+else:
+    st.info("No queries have been run yet.")