Blame for web_app/app.py

"""

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
from dotenv import load_dotenv

# Load environment variables early so keys can be hidden from UI
_ENV_PATH = Path(__file__).parent.parent / ".env"
load_dotenv(_ENV_PATH)

# 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)

st.sidebar.markdown("---")
st.sidebar.markdown("<h3 style='color:#34D399;margin-top:0;'>👤 User Profile</h3>", unsafe_allow_html=True)
username = st.sidebar.text_input("Username / Email", value="", placeholder="name@company.com", help="Mandatory for audit trail")
st.sidebar.markdown("---")

# -- 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", value=os.environ.get("NVIDIA_API_KEY", ""), type="password", placeholder="nvapi-...", 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", value=os.environ.get("GROQ_API_KEY", ""), type="password", placeholder="gsk_...", 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", value=os.environ.get("GEMINI_API_KEY", ""), type="password", placeholder="AIza...", 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)
hf_key = st.sidebar.text_input("Hugging Face API Key", value=os.environ.get("HF_API_KEY", ""), type="password", placeholder="hf_...", 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", value=os.environ.get("CLAUDE_API_KEY", ""), type="password", placeholder="sk-ant-...", 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", value=os.environ.get("OPENAI_API_KEY", ""), type="password", placeholder="sk-proj-...", key="inp_openai")

st.sidebar.markdown("---")

# -- 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)

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:
    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 yq
            res = yq.search(search_query)
            quotes = [q for q in res.get("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("<br>", unsafe_allow_html=True)

progress_bar_container = st.empty()
status_container = st.empty()
metrics_container = st.empty()
download_container = st.empty()

# -- 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:
            try:
                progress_bar = progress_bar_container.progress(0, text="Initializing analysis...")
                with status_container.status("Analysis Execution Status", expanded=True) as status_box:
                    def log_fn(msg):
                        status_box.write(msg)
                    
                    log_fn(f"Starting analysis for: '{ticker_input}'")
                    log_fn(f"Time: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
                    
                    # 1. Resolve Company Info
                    progress_bar.progress(14, text="[1/7] Resolving company metadata...")
                    log_fn("\n[1/7] Resolving company metadata ...")
                    meta = resolve(ticker_input, log_fn=log_fn)
                    log_fn(f"  [OK] {meta.name} | {meta.ticker} | {meta.exchange}")
                    
                    # 2. Fetch Statements
                    progress_bar.progress(28, text="[2/7] Scrape financial 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
                    progress_bar.progress(42, text="[3/7] Benchmarking peer selection...")
                    log_fn("\n[3/7] Benchmarking peer selection ...")
                    fin_data["peers"] = select_peers(meta, log_fn=log_fn)
                    
                    # 4. Math ratios calculations
                    progress_bar.progress(57, text="[4/7] Computing financial ratios...")
                    log_fn("\n[4/7] Computing financial ratios & DuPont decomposition ...")
                    fin_data = calculate(fin_data, meta.to_dict())
                    log_fn("  [OK] Completed ratio math computations")
                    
                    # 5. AI Synthesis Narrative
                    progress_bar.progress(71, text="[5/7] Requesting AI Model 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
                    progress_bar.progress(85, text="[6/7] Building output report files...")
                    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"  [OK] 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"  [OK] 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"  [OK] Created PowerPoint Presentation: {pptx_path.name}")
                    
                    progress_bar.progress(100, text="[7/7] Financial analysis process complete!")
                    log_fn(f"\n[7/7] ✅ Financial analysis process complete!")
                    status_box.update(state="complete", expanded=False)
                
                # -- Display Success Metrics
                st.toast('Analysis successfully completed!', icon='🎉')
                with metrics_container.container():
                    st.markdown("### 📊 Quick Insights", unsafe_allow_html=True)
                    mc1, mc2, mc3, mc4 = st.columns(4)
                    mc1.metric("Company", meta.name, meta.ticker)
                    
                    # Format Price
                    price_val = meta.share_price
                    price_str = f"{fin_data.get('currency', '')} {price_val:,.2f}" if price_val else "N/A"
                    mc2.metric("Share Price", price_str)
                    
                    # Format Market Cap
                    mc_val = meta.mkt_cap
                    mc_str = "N/A"
                    if mc_val:
                        if mc_val > 1e12:
                            mc_str = f"{fin_data.get('currency', '')} {mc_val/1e12:,.2f} Trillion"
                        elif mc_val > 1e9:
                            mc_str = f"{fin_data.get('currency', '')} {mc_val/1e9:,.2f} Billion"
                        else:
                            mc_str = f"{fin_data.get('currency', '')} {mc_val/1e6:,.2f} Million"
                    mc3.metric("Market Cap", mc_str)
                    
                    # Data quality badge
                    dq = fin_data.get("data_quality", 0)
                    if dq >= 80:
                        dq_color, dq_label = "#22c55e", "Excellent"
                    elif dq >= 55:
                        dq_color, dq_label = "#f59e0b", "Partial"
                    else:
                        dq_color, dq_label = "#ef4444", "Low"
                    mc4.markdown(
                        f"""<div style='background:rgba(255,255,255,0.05);border-radius:12px;

                        padding:14px 12px;text-align:center;border:1px solid rgba(255,255,255,0.1)'>

                        <div style='font-size:0.75rem;color:#9CA3AF;margin-bottom:4px'>Data Quality</div>

                        <div style='font-size:1.6rem;font-weight:700;color:{dq_color}'>{dq}%</div>

                        <div style='font-size:0.75rem;color:{dq_color}'>{dq_label}</div>

                        </div>""",
                        unsafe_allow_html=True
                    )
                    
                    # Source attribution
                    src = fin_data.get("source", "")
                    if src:
                        st.caption(f"Data sources: {src}")
                    st.markdown("<br>", unsafe_allow_html=True)
                
                # -- 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.")