Update src/streamlit_app.py

src/streamlit_app.py

@@ -2,23 +2,40 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 import plotly.express as px
+import plotly.graph_objects as go
 import requests
 import os
 
-# ── Page config ──────────────────────────────────────────────
 st.set_page_config(
     page_title="Portfolio Monitoring Dashboard",
     page_icon="📈",
     layout="wide"
 )
 
-# ── Hugging Face AI (FinBERT sentiment) ──────────────────────
+# ── Custom CSS ────────────────────────────────────────────────
+st.markdown("""
+<style>
+    .block-container { padding-top: 2rem; }
+    .metric-label { font-size: 13px !important; }
+    .stAlert { border-radius: 10px; }
+    div[data-testid="stSidebarContent"] { padding-top: 1.5rem; }
+    .step-box {
+        background: #f8f9fa;
+        border-left: 4px solid #3498db;
+        border-radius: 6px;
+        padding: 0.8rem 1rem;
+        margin-bottom: 0.6rem;
+        font-size: 14px;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# ── Hugging Face AI ───────────────────────────────────────────
 HF_API_KEY = os.environ.get("HF_API_KEY", "")
 
 def analyze_sentiment(text: str) -> str:
-    """Calls FinBERT on Hugging Face to get financial sentiment."""
     if not HF_API_KEY:
-        return "⚠️ No API key"
+        return "⚠️ No API key configured"
     url = "https://api-inference.huggingface.co/models/ProsusAI/finbert"
     headers = {"Authorization": f"Bearer {HF_API_KEY}"}
     try:
@@ -28,214 +45,462 @@ def analyze_sentiment(text: str) -> str:
         if isinstance(result, list) and result:
             top = max(result[0], key=lambda x: x["score"])
             emoji = {"positive": "🟢", "negative": "🔴",
-                     "neutral": "🟡"}.get(top["label"].lower(), "⚪")
+                     "neutral":  "🟡"}.get(top["label"].lower(), "⚪")
             return f"{emoji} {top['label'].capitalize()} ({top['score']:.0%})"
     except Exception:
-        return "❌ Error"
+        return "❌ Connection error"
     return "❓ Unknown"
 
-# ── Load data ─────────────────────────────────────────────────
-@st.cache_data
-def load_data():
-    portfolio     = pd.read_csv("portfolio_output.csv")
-    risk_metrics  = pd.read_csv("risk_metrics_output.csv")
-    daily_returns = pd.read_csv("portfolio_daily_returns_output.csv",
-                                parse_dates=["date"])
-    return portfolio, risk_metrics, daily_returns
-
-try:
-    portfolio, risk_metrics, daily_returns = load_data()
-    data_loaded = True
-except FileNotFoundError:
-    data_loaded = False
-
 # ── Sidebar ───────────────────────────────────────────────────
-st.sidebar.title("Portfolio Monitor")
-st.sidebar.caption("ESCP — Applied Data Science Workshop")
+with st.sidebar:
+    st.image(
+        "https://upload.wikimedia.org/wikipedia/commons/thumb/e/e8/"
+        "Matplotlib_icon.svg/120px-Matplotlib_icon.svg.png",
+        width=50
+    )
+    st.title("Portfolio Monitor")
+    st.caption("ESCP — Applied Data Science Workshop")
+    st.divider()
 
-# ── Main title ────────────────────────────────────────────────
-st.title("📈 Portfolio Monitoring Dashboard")
-st.caption("Real-time portfolio performance, risk alerts & AI-powered news sentiment")
-st.divider()
+    # ── How to use ────────────────────────────────────────────
+    with st.expander("❓ How to use this app", expanded=True):
+        st.markdown("""
+<div class='step-box'>
+<b>Step 1</b> — Run the Google Colab notebook with your transaction data
+</div>
+<div class='step-box'>
+<b>Step 2</b> — Download the 4 CSV files generated by Colab
+</div>
+<div class='step-box'>
+<b>Step 3</b> — Upload them below using the file uploader
+</div>
+<div class='step-box'>
+<b>Step 4</b> — Your dashboard updates automatically 🎉
+</div>
+        """, unsafe_allow_html=True)
 
-# ── Demo mode if no CSV ───────────────────────────────────────
-if not data_loaded:
-    st.warning(
-        "⚠️ No data files found. Showing demo data. "
-        "Upload your CSV files to see real results."
+    st.divider()
+
+    # ── File uploaders ────────────────────────────────────────
+    st.subheader("📂 Upload your CSV files")
+    st.caption("Generated by the Google Colab notebook")
+
+    f_portfolio = st.file_uploader(
+        "portfolio_output.csv",
+        type="csv", key="portfolio",
+        help="Main portfolio table with positions and P&L"
+    )
+    f_risk = st.file_uploader(
+        "risk_metrics_output.csv",
+        type="csv", key="risk",
+        help="Risk indicators computed from daily returns"
+    )
+    f_returns = st.file_uploader(
+        "portfolio_daily_returns_output.csv",
+        type="csv", key="returns",
+        help="Daily portfolio return series"
     )
+    f_features = st.file_uploader(
+        "asset_features_output.csv",
+        type="csv", key="features",
+        help="Per-asset features used by the ML model"
+    )
+
+    st.divider()
+    use_demo = st.toggle("🎯 Use demo data instead", value=False,
+                         help="Load synthetic data to preview the dashboard")
+
+# ── Load data ─────────────────────────────────────────────────
+@st.cache_data
+def load_demo():
     np.random.seed(42)
+
     portfolio = pd.DataFrame({
-        "Ticker":                 ["AAPL", "MSFT", "NVDA", "GOOGL", "AMZN"],
-        "Friendly name":          ["Apple", "Microsoft", "Nvidia",
-                                   "Alphabet", "Amazon"],
-        "market_value":           [12000, 9500, 8200, 6100, 5400],
-        "invested_amount":        [10000, 8000, 5000, 5500, 6000],
-        "unrealized_pnl":         [2000, 1500, 3200, 600, -600],
-        "cumulative_realized_pnl":[500, 300, 200, 100, 50],
-        "total_pnl":              [2500, 1800, 3400, 700, -550],
-        "weight":                 [0.29, 0.23, 0.20, 0.15, 0.13],
-        "asset_concentration_flag": [False, False, False, False, False],
-        "stressed_value":         [10200, 8075, 6970, 5185, 4590],
-        "stress_test_loss":       [-1800,-1425,-1230, -915, -810],
-        "alert_level":            ["Normal","Normal","Normal",
-                                   "Normal","Warning loss"],
+        "Ticker":        ["AAPL", "MSFT", "NVDA", "GOOGL", "AMZN"],
+        "Friendly name": ["Apple Inc.", "Microsoft Corp.",
+                          "Nvidia Corp.", "Alphabet Inc.", "Amazon.com"],
+        "cumulated_qte": [50, 30, 20, 15, 25],
+        "average_entry": [158.0, 290.0, 410.0, 128.0, 165.0],
+        "last_price":    [213.5, 378.9, 875.4, 172.3, 198.7],
+        "invested_amount":         [7900, 8700, 8200, 1920, 4125],
+        "market_value":            [10675, 11367, 17508, 2585, 4968],
+        "unrealized_pnl":          [2775, 2667, 9308, 665, 843],
+        "cumulative_realized_pnl": [420, 310, 180, 95, 60],
+        "total_pnl":               [3195, 2977, 9488, 760, 903],
+        "weight":                  [0.225, 0.239, 0.368, 0.054, 0.104],
+        "asset_concentration_flag":[False, False, True, False, False],
+        "stressed_value":          [9074, 9662, 14882, 2197, 4222],
+        "stress_test_loss":        [-1601,-1705,-2626,-388,-745],
+        "alert_level":             ["Normal","Normal","Normal","Normal","Normal"],
+        "unrealized_return_pct":   [0.35, 0.31, 1.13, 0.35, 0.20],
+        "volatility_30d":          [0.22, 0.19, 0.41, 0.20, 0.23],
+        "momentum_30d":            [0.05, 0.04, 0.12, 0.03, 0.06],
+        "max_drawdown":            [-0.15,-0.12,-0.30,-0.13,-0.18],
     })
-    portfolio["unrealized_return_pct"] = (
-        portfolio["unrealized_pnl"] / portfolio["invested_amount"]
-    )
 
-    dates = pd.date_range(end=pd.Timestamp.today(), periods=120, freq="B")
+    dates   = pd.date_range(end=pd.Timestamp.today(), periods=120, freq="B")
+    rets    = np.random.normal(0.0006, 0.011, 120)
+    rets[15] = -0.032; rets[47] = -0.028; rets[83] = 0.031
     daily_returns = pd.DataFrame({
         "date": dates,
-        "portfolio_daily_returns": np.random.normal(0.0005, 0.012, 120)
+        "portfolio_daily_returns": rets
     })
+
+    r = daily_returns["portfolio_daily_returns"]
+    sharpe = (r.mean() / r.std()) * np.sqrt(252)
     risk_metrics = pd.DataFrame({
-        "Metric": ["Mean daily return", "Daily volatility",
-                   "Annualized volatility", "Worst daily return",
-                   "Best daily return", "Sharpe ratio"],
-        "Value":  [0.0005, 0.012, 0.190, -0.032, 0.028, 0.66]
+        "Metric": ["Mean daily return","Daily volatility",
+                   "Annualized volatility","Worst day",
+                   "Best day","Sharpe ratio (approx.)"],
+        "Value":  [f"{r.mean():.4%}", f"{r.std():.4%}",
+                   f"{r.std()*np.sqrt(252):.2%}", f"{r.min():.4%}",
+                   f"{r.max():.4%}", f"{sharpe:.2f}"]
     })
 
+    return portfolio, daily_returns, risk_metrics
+
+def load_uploaded(f_portfolio, f_risk, f_returns):
+    portfolio     = pd.read_csv(f_portfolio)
+    risk_metrics  = pd.read_csv(f_risk)
+    daily_returns = pd.read_csv(f_returns, parse_dates=["date"])
+    return portfolio, daily_returns, risk_metrics
+
+# ── Decide which data to use ────��─────────────────────────────
+all_uploaded = all([f_portfolio, f_risk, f_returns])
+
+if use_demo:
+    portfolio, daily_returns, risk_metrics = load_demo()
+    data_source = "demo"
+elif all_uploaded:
+    try:
+        portfolio, daily_returns, risk_metrics = load_uploaded(
+            f_portfolio, f_risk, f_returns
+        )
+        data_source = "real"
+    except Exception as e:
+        st.error(f"❌ Error reading files: {e}")
+        st.stop()
+else:
+    data_source = "none"
+
+# ── Main title ────────────────────────────────────────────────
+st.title("📈 Portfolio Monitoring Dashboard")
+st.caption(
+    "Real-time portfolio performance, risk alerts & "
+    "AI-powered news sentiment analysis"
+)
+
+if data_source == "demo":
+    st.info("🎯 **Demo mode** — showing synthetic data. "
+            "Upload your CSV files in the sidebar to see your real portfolio.")
+elif data_source == "real":
+    st.success("✅ **Your portfolio data is loaded.** "
+               "All figures below reflect your real transactions.")
+else:
+    # ── Onboarding screen ──────────────────────────────────────
+    st.divider()
+    st.subheader("👋 Welcome! Let's get started.")
+
+    col1, col2, col3, col4 = st.columns(4)
+
+    with col1:
+        st.markdown("""
+        ### 1️⃣ Run Colab
+        Open the **Google Colab notebook** and run all cells with your transaction data.
+        """)
+    with col2:
+        st.markdown("""
+        ### 2️⃣ Download CSVs
+        After execution, download the **4 CSV files** from the Colab file panel (📁 on the left).
+        """)
+    with col3:
+        st.markdown("""
+        ### 3️⃣ Upload here
+        Use the **file uploaders in the sidebar** (👈) to upload your 4 CSV files.
+        """)
+    with col4:
+        st.markdown("""
+        ### 4️⃣ Explore
+        Your **dashboard updates instantly** with your real portfolio data and live analytics.
+        """)
+
+    st.divider()
+    st.markdown("""
+    #### 📁 The 4 files you need to upload:
+    | File | Content |
+    |------|---------|
+    | `portfolio_output.csv` | Your positions, P&L, weights, alerts |
+    | `risk_metrics_output.csv` | Volatility, Sharpe ratio, best/worst day |
+    | `portfolio_daily_returns_output.csv` | Daily return series for the chart |
+    | `asset_features_output.csv` | Per-asset features from the ML model |
+
+    > 💡 **Don't have the files yet?** Toggle **"Use demo data"** in the sidebar
+    > to preview the full dashboard with synthetic data.
+    """)
+    st.stop()
+
+st.divider()
+
+# ── Computed fields ───────────────────────────────────────────
+if "unrealized_return_pct" not in portfolio.columns:
+    portfolio["unrealized_return_pct"] = np.where(
+        portfolio["invested_amount"] > 0,
+        portfolio["unrealized_pnl"] / portfolio["invested_amount"],
+        np.nan
+    )
+
 # ── Ticker filter ─────────────────────────────────────────────
 ticker_list = ["All"] + sorted(portfolio["Ticker"].dropna().unique().tolist())
-selected = st.sidebar.selectbox("Filter by asset", ticker_list)
+selected = st.selectbox(
+    "🔍 Filter by asset",
+    ticker_list,
+    help="Select a specific asset or view the full portfolio"
+)
 pv = portfolio if selected == "All" else portfolio[portfolio["Ticker"] == selected]
 
+st.divider()
+
 # ── KPI Cards ─────────────────────────────────────────────────
 st.subheader("Portfolio Summary")
+
 c1, c2, c3, c4 = st.columns(4)
-c1.metric("💰 Invested",       f"{pv['invested_amount'].sum():,.0f} €")
-c2.metric("📊 Market Value",   f"{pv['market_value'].sum():,.0f} €")
-c3.metric("📈 Total P&L",      f"{pv['total_pnl'].sum():,.0f} €")
-c4.metric("🏦 Positions",      int(pv["Ticker"].nunique()))
+c1.metric(
+    "💰 Total Invested",
+    f"{pv['invested_amount'].sum():,.0f} €",
+    help="Sum of all buy amounts including fees"
+)
+c2.metric(
+    "📊 Market Value",
+    f"{pv['market_value'].sum():,.0f} €",
+    delta=f"{pv['unrealized_pnl'].sum():,.0f} € unrealized",
+    help="Current value based on latest prices"
+)
+c3.metric(
+    "📈 Total P&L",
+    f"{pv['total_pnl'].sum():,.0f} €",
+    help="Realized + unrealized gains and losses"
+)
+c4.metric(
+    "🏦 Active Positions",
+    int(pv["Ticker"].nunique()),
+    help="Number of assets currently held"
+)
 st.divider()
 
-# ── Charts row 1 ──────────────────────────────────────────────
+# ── Allocation + Bar ──────────────────────────────────────────
 col_l, col_r = st.columns(2)
 
 with col_l:
     st.subheader("🥧 Portfolio Allocation")
-    fig_pie = px.pie(pv, names="Ticker", values="market_value",
-                     hole=0.35)
+    st.caption("Current weights by market value")
+    fig_pie = px.pie(
+        pv, names="Ticker", values="market_value",
+        hole=0.4,
+        color_discrete_sequence=px.colors.qualitative.Set2
+    )
     fig_pie.update_traces(textinfo="percent+label")
+    fig_pie.update_layout(showlegend=False, margin=dict(t=10, b=10))
     st.plotly_chart(fig_pie, use_container_width=True)
 
 with col_r:
     st.subheader("📊 Market Value by Asset")
-    color_col = "alert_level" if "alert_level" in pv.columns else "Ticker"
+    st.caption("Color = alert level based on unrealized return")
+    color_map = {
+        "Normal":        "#2ecc71",
+        "Warning loss":  "#f39c12",
+        "Critical loss": "#e74c3c"
+    }
     fig_bar = px.bar(
         pv.sort_values("market_value", ascending=False),
-        x="Ticker", y="market_value", color=color_col,
-        color_discrete_map={
-            "Normal": "#2ecc71",
-            "Warning loss": "#f39c12",
-            "Critical loss": "#e74c3c"
-        }
+        x="Ticker", y="market_value",
+        color="alert_level",
+        color_discrete_map=color_map,
+        labels={"market_value": "Market Value (€)",
+                "alert_level":  "Alert Level"}
     )
+    fig_bar.update_layout(margin=dict(t=10, b=10))
     st.plotly_chart(fig_bar, use_container_width=True)
 
 # ── Cumulative return ─────────────────────────────────────────
 st.subheader("📉 Cumulative Portfolio Return")
+st.caption("Based on daily weighted returns since portfolio inception")
+
 daily_returns["cumulative_return"] = (
     (1 + daily_returns["portfolio_daily_returns"]).cumprod() - 1
 )
-fig_line = px.line(daily_returns, x="date", y="cumulative_return",
-                   labels={"cumulative_return": "Cumulative Return",
-                           "date": "Date"})
-fig_line.add_hline(y=0, line_dash="dash", line_color="black")
-fig_line.update_traces(line_color="#3498db")
+fig_line = px.line(
+    daily_returns, x="date", y="cumulative_return",
+    labels={"cumulative_return": "Cumulative Return", "date": "Date"}
+)
+fig_line.add_hline(y=0, line_dash="dash", line_color="gray",
+                   annotation_text="Break-even")
+fig_line.update_traces(line_color="#3498db", line_width=2.5)
+fig_line.update_layout(margin=dict(t=10, b=10))
 st.plotly_chart(fig_line, use_container_width=True)
 
-# ── Unrealized return scatter ──────────────────────────────────
-st.subheader("⚡ Unrealized Return by Asset")
-if "unrealized_return_pct" in pv.columns:
-    fig_ret = px.bar(
-        pv.sort_values("unrealized_return_pct"),
-        x="Ticker", y="unrealized_return_pct",
-        color=pv["unrealized_return_pct"].apply(
-            lambda x: "Gain" if x >= 0 else "Loss"
-        ),
-        color_discrete_map={"Gain": "#2ecc71", "Loss": "#e74c3c"},
-        labels={"unrealized_return_pct": "Unrealized Return %"}
-    )
-    fig_ret.add_hline(y=0, line_dash="dash", line_color="black")
-    st.plotly_chart(fig_ret, use_container_width=True)
+# ── Unrealized return ─────────────────────────────────────────
+st.subheader("⚡ Unrealized Return % by Asset")
+st.caption("Percentage gain or loss on current open positions")
+
+pv2 = pv.copy()
+pv2["color"] = pv2["unrealized_return_pct"].apply(
+    lambda x: "Gain 🟢" if x >= 0 else "Loss 🔴"
+)
+fig_ret = px.bar(
+    pv2.sort_values("unrealized_return_pct"),
+    x="Ticker", y="unrealized_return_pct",
+    color="color",
+    color_discrete_map={"Gain 🟢": "#2ecc71", "Loss 🔴": "#e74c3c"},
+    text=pv2["unrealized_return_pct"].apply(lambda x: f"{x:.1%}"),
+    labels={"unrealized_return_pct": "Unrealized Return %", "color": ""}
+)
+fig_ret.add_hline(y=0, line_dash="dash", line_color="gray")
+fig_ret.update_layout(margin=dict(t=10, b=10))
+st.plotly_chart(fig_ret, use_container_width=True)
+st.divider()
 
 # ── Risk metrics ──────────────────────────────────────────────
 st.subheader("🔬 Risk Metrics")
+st.caption("Computed from the daily return series of your portfolio")
 st.dataframe(risk_metrics, use_container_width=True, hide_index=True)
+st.divider()
 
 # ── Stress test ───────────────────────────────────────────────
-if "stressed_value" in pv.columns:
-    st.subheader("💥 Stress Test (−15% market shock)")
-    sk1, sk2, sk3 = st.columns(3)
-    sk1.metric("Current Value",  f"{pv['market_value'].sum():,.0f} €")
-    sk2.metric("Stressed Value", f"{pv['stressed_value'].sum():,.0f} €",
-               delta=f"{pv['stress_test_loss'].sum():,.0f} €")
-    sk3.metric("Estimated Loss", f"{pv['stress_test_loss'].sum():,.0f} €")
+st.subheader("💥 Stress Test — Simulated −15% Market Shock")
+st.caption(
+    "Estimates your portfolio loss if all positions dropped by 15% simultaneously. "
+    "This is a simplified scenario — real crashes are rarely uniform."
+)
+
+sk1, sk2, sk3 = st.columns(3)
+sk1.metric("Current Value",
+           f"{pv['market_value'].sum():,.0f} €")
+sk2.metric("Stressed Value",
+           f"{pv['stressed_value'].sum():,.0f} €",
+           delta=f"{pv['stress_test_loss'].sum():,.0f} €")
+sk3.metric("Estimated Loss",
+           f"{pv['stress_test_loss'].sum():,.0f} €")
+
+fig_stress = px.bar(
+    pv, x="Ticker",
+    y=["market_value", "stressed_value"],
+    barmode="group",
+    color_discrete_map={
+        "market_value":   "#3498db",
+        "stressed_value": "#e74c3c"
+    },
+    labels={"value": "Value (€)", "variable": "Scenario"}
+)
+fig_stress.update_layout(margin=dict(t=10, b=10))
+st.plotly_chart(fig_stress, use_container_width=True)
+st.divider()
 
 # ── Alert table ───────────────────────────────────────────────
 st.subheader("🚨 Risk Alert Table")
-alert_cols = [c for c in ["Ticker", "market_value", "weight",
-                           "unrealized_return_pct", "alert_level",
-                           "asset_concentration_flag"] if c in pv.columns]
-st.dataframe(pv[alert_cols].sort_values("market_value", ascending=False),
-             use_container_width=True, hide_index=True)
+st.caption(
+    "🟢 Normal = above −10%  |  "
+    "🟠 Warning = below −10%  |  "
+    "🔴 Critical = below −20%"
+)
 
-st.divider()
+alert_cols = [c for c in [
+    "Ticker", "Friendly name", "market_value", "weight",
+    "unrealized_return_pct", "alert_level", "asset_concentration_flag"
+] if c in pv.columns]
+
+alert_df = pv[alert_cols].copy()
 
-# ── AI Sentiment Analysis ─────────────────────────────────────
-st.subheader("🤖 AI Sentiment Analysis (FinBERT)")
-st.caption("Powered by Hugging Face — ProsusAI/finbert")
+if "market_value" in alert_df.columns:
+    alert_df["market_value"] = alert_df["market_value"].apply(
+        lambda x: f"{x:,.0f} €"
+    )
+if "weight" in alert_df.columns:
+    alert_df["weight"] = alert_df["weight"].apply(lambda x: f"{x:.1%}")
+if "unrealized_return_pct" in alert_df.columns:
+    alert_df["unrealized_return_pct"] = alert_df["unrealized_return_pct"].apply(
+        lambda x: f"{x:.1%}"
+    )
 
-news_examples = {
-    "AAPL": "Apple reports record quarterly earnings driven by iPhone sales",
-    "MSFT": "Microsoft faces antitrust investigation in European markets",
-    "NVDA": "Nvidia surges on strong AI chip demand forecast",
-    "GOOGL": "Alphabet announces major layoffs amid cost-cutting efforts",
-    "AMZN": "Amazon expands logistics network with new warehouse openings",
-}
+alert_df.columns = [c.replace("_", " ").title() for c in alert_df.columns]
+st.dataframe(
+    alert_df.sort_values("Market Value", ascending=False),
+    use_container_width=True,
+    hide_index=True
+)
+st.divider()
 
-st.info(
-    "Enter a news headline below and click Analyze to get "
-    "an AI-powered sentiment score using FinBERT, "
-    "a model trained specifically on financial text."
+# ── AI Sentiment ──────────────────────────────────────────────
+st.subheader("🤖 AI News Sentiment Analysis")
+st.caption(
+    "Powered by **FinBERT** (ProsusAI/finbert) — a model trained specifically "
+    "on financial news text via Hugging Face Inference API"
 )
 
+if not HF_API_KEY:
+    st.warning(
+        "⚠️ **AI not activated.** To enable, go to your Space → "
+        "Settings → Repository secrets → add `HF_API_KEY` with your "
+        "Hugging Face token."
+    )
+else:
+    st.success("✅ FinBERT connected and ready.")
+
 col_input, col_btn = st.columns([4, 1])
 with col_input:
     headline = st.text_input(
-        "News headline",
-        value="Apple reports record quarterly earnings driven by iPhone sales"
+        "📰 Enter a financial news headline to analyze",
+        value="Apple reports record quarterly earnings driven by iPhone sales",
+        placeholder="e.g. Nvidia shares surge on AI chip demand..."
     )
 with col_btn:
     st.write("")
     st.write("")
-    run_sentiment = st.button("🔍 Analyze")
+    run = st.button("🔍 Analyze", use_container_width=True)
 
-if run_sentiment and headline:
+if run and headline:
     with st.spinner("Calling FinBERT model..."):
-        sentiment = analyze_sentiment(headline)
-    st.success(f"**Sentiment result:** {sentiment}")
-
-# Pre-loaded examples
-if st.checkbox("Show sentiment for example headlines"):
-    results = []
-    for ticker, text in news_examples.items():
-        with st.spinner(f"Analyzing {ticker}..."):
-            sent = analyze_sentiment(text)
-        results.append({"Ticker": ticker, "Headline": text, "Sentiment": sent})
-    st.dataframe(pd.DataFrame(results), use_container_width=True, hide_index=True)
+        result = analyze_sentiment(headline)
+    st.success(f"**Sentiment result:** {result}")
+
+with st.expander("📰 Analyze example headlines for each asset in your portfolio"):
+    examples = {
+        "AAPL":  "Apple reports record quarterly earnings driven by iPhone sales",
+        "MSFT":  "Microsoft faces antitrust investigation in European markets",
+        "NVDA":  "Nvidia surges on strong AI chip demand forecast",
+        "GOOGL": "Alphabet announces major layoffs amid cost-cutting efforts",
+        "AMZN":  "Amazon expands logistics network with new warehouse openings",
+    }
+    if st.button("▶️ Run all sentiment analyses"):
+        rows = []
+        for ticker, text in examples.items():
+            with st.spinner(f"Analyzing {ticker}..."):
+                sent = analyze_sentiment(text)
+            rows.append({
+                "Ticker":    ticker,
+                "Headline":  text,
+                "Sentiment": sent
+            })
+        st.dataframe(
+            pd.DataFrame(rows),
+            use_container_width=True,
+            hide_index=True
+        )
 
 st.divider()
 
 # ── Download ──────────────────────────────────────────────────
+st.subheader("⬇️ Export")
+st.caption("Download your current portfolio snapshot as a CSV file")
 st.download_button(
-    label="⬇️ Download Portfolio Table (CSV)",
+    label="Download Portfolio Table (CSV)",
     data=portfolio.to_csv(index=False).encode("utf-8"),
     file_name="portfolio_monitoring_output.csv",
-    mime="text/csv"
+    mime="text/csv",
+    use_container_width=False
 )
 
-st.caption("ESCP Business School — Applied Data Science Workshop | Group Project")
+st.divider()
+st.caption(
+    "ESCP Business School — Applied Data Science Workshop | "
+    "Group Project | Portfolio Monitoring Tool"
+)