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app.py

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+import gradio as gr
+import yfinance as yf
+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+import warnings
+warnings.filterwarnings("ignore")
+
+# ── Theme colours (dark terminal-finance aesthetic) ──────────────────────────
+BG        = "#0d1117"
+SURFACE   = "#161b22"
+BORDER    = "#30363d"
+GREEN     = "#3fb950"
+RED       = "#f85149"
+BLUE      = "#58a6ff"
+YELLOW    = "#d29922"
+TEXT      = "#e6edf3"
+MUTED     = "#8b949e"
+
+POPULAR_TICKERS = [
+    "AAPL", "MSFT", "GOOGL", "AMZN", "TSLA",
+    "META", "NVDA", "JPM", "BRK-B", "V",
+    "NFLX", "DIS", "BABA", "AMD", "INTC",
+    "UBER", "SPOT", "PYPL", "SQ", "SNAP",
+]
+
+MODELS = {
+    "Linear Regression":        LinearRegression(),
+    "Random Forest":            RandomForestRegressor(n_estimators=100, random_state=42),
+    "Gradient Boosting":        GradientBoostingRegressor(n_estimators=100, random_state=42),
+}
+
+PERIODS = {
+    "6 Months":  "6mo",
+    "1 Year":    "1y",
+    "2 Years":   "2y",
+    "5 Years":   "5y",
+    "10 Years":  "10y",
+}
+
+INTERVALS = {
+    "Daily":   "1d",
+    "Weekly":  "1wk",
+}
+
+# ── Feature engineering ───────────────────────────────────────────────────────
+def make_features(df: pd.DataFrame) -> pd.DataFrame:
+    df = df.copy()
+    df["MA7"]   = df["Close"].rolling(7).mean()
+    df["MA21"]  = df["Close"].rolling(21).mean()
+    df["MA50"]  = df["Close"].rolling(50).mean()
+    df["EMA12"] = df["Close"].ewm(span=12, adjust=False).mean()
+    df["EMA26"] = df["Close"].ewm(span=26, adjust=False).mean()
+    df["MACD"]  = df["EMA12"] - df["EMA26"]
+    df["Vol_MA"] = df["Volume"].rolling(7).mean()
+    df["Return1"] = df["Close"].pct_change(1)
+    df["Return5"] = df["Close"].pct_change(5)
+    df["High_Low"]    = df["High"] - df["Low"]
+    df["Close_Open"]  = df["Close"] - df["Open"]
+    delta = df["Close"].diff()
+    gain  = delta.clip(lower=0).rolling(14).mean()
+    loss  = (-delta.clip(upper=0)).rolling(14).mean()
+    rs    = gain / loss.replace(0, np.nan)
+    df["RSI"] = 100 - (100 / (1 + rs))
+    df["Target"] = df["Close"].shift(-1)
+    return df.dropna()
+
+# ── Core prediction logic ─────────────────────────────────────────────────────
+def predict_stock(ticker, period_label, interval_label, model_name, future_days):
+    ticker  = ticker.strip().upper()
+    period  = PERIODS[period_label]
+    interval = INTERVALS[interval_label]
+
+    try:
+        raw = yf.download(ticker, period=period, interval=interval, progress=False)
+        if raw.empty:
+            return None, None, f"❌  No data found for **{ticker}**. Check the ticker symbol."
+        if isinstance(raw.columns, pd.MultiIndex):
+            raw.columns = raw.columns.get_level_values(0)
+        raw = raw[["Open","High","Low","Close","Volume"]].dropna()
+    except Exception as e:
+        return None, None, f"❌  Data fetch error: {e}"
+
+    if len(raw) < 60:
+        return None, None, f"❌  Not enough data ({len(raw)} rows). Try a longer period."
+
+    df = make_features(raw)
+    feature_cols = [
+        "MA7","MA21","MA50","EMA12","EMA26","MACD",
+        "Vol_MA","Return1","Return5","High_Low","Close_Open","RSI",
+        "Open","High","Low","Volume",
+    ]
+
+    X = df[feature_cols].values
+    y = df["Target"].values
+
+    split = int(len(X) * 0.8)
+    X_train, X_test = X[:split], X[split:]
+    y_train, y_test = y[:split], y[split:]
+
+    scaler = MinMaxScaler()
+    X_train_s = scaler.fit_transform(X_train)
+    X_test_s  = scaler.transform(X_test)
+
+    model = MODELS[model_name]
+    model.fit(X_train_s, y_train)
+    y_pred = model.predict(X_test_s)
+
+    mae  = mean_absolute_error(y_test, y_pred)
+    rmse = np.sqrt(mean_squared_error(y_test, y_pred))
+    r2   = r2_score(y_test, y_pred)
+    acc  = max(0.0, r2) * 100
+
+    # ── Future forecast ───────────────────────────────────────────────────────
+    future_days = int(future_days)
+    last_features = X[-1].reshape(1, -1)
+    future_prices = []
+    cur = last_features.copy()
+    for _ in range(future_days):
+        cur_s  = scaler.transform(cur)
+        nxt    = model.predict(cur_s)[0]
+        future_prices.append(float(nxt))
+        cur[0, 0] = nxt          # crude: update Close proxy
+
+    last_date  = df.index[-1]
+    freq       = "B" if interval == "1d" else "W"
+    fut_dates  = pd.date_range(last_date, periods=future_days + 1, freq=freq)[1:]
+
+    # ── Candlestick + prediction chart ───────────────────────────────────────
+    fig = make_subplots(
+        rows=3, cols=1,
+        shared_xaxes=True,
+        row_heights=[0.55, 0.25, 0.20],
+        vertical_spacing=0.04,
+        subplot_titles=("Price & Prediction", "Volume", "RSI"),
+    )
+
+    test_dates = df.index[split:]
+
+    # Candlestick (all history)
+    fig.add_trace(go.Candlestick(
+        x=raw.index, open=raw["Open"], high=raw["High"],
+        low=raw["Low"],  close=raw["Close"],
+        increasing_line_color=GREEN, decreasing_line_color=RED,
+        name="Price", showlegend=False,
+    ), row=1, col=1)
+
+    # Moving averages
+    for col, color, label in [("MA21", BLUE, "MA 21"), ("MA50", YELLOW, "MA 50")]:
+        fig.add_trace(go.Scatter(
+            x=df.index, y=df[col], line=dict(color=color, width=1.2),
+            name=label, opacity=0.85,
+        ), row=1, col=1)
+
+    # Test-set predictions
+    fig.add_trace(go.Scatter(
+        x=test_dates, y=y_pred,
+        line=dict(color="#a371f7", width=1.8, dash="dot"),
+        name="Model (test)", opacity=0.9,
+    ), row=1, col=1)
+
+    # Future forecast
+    fig.add_trace(go.Scatter(
+        x=list(fut_dates), y=future_prices,
+        line=dict(color="#f0883e", width=2),
+        name=f"Forecast ({future_days}d)",
+        mode="lines+markers",
+        marker=dict(size=5),
+    ), row=1, col=1)
+
+    # Vertical "today" line
+    fig.add_vline(x=str(last_date.date()), line_width=1,
+                  line_dash="dash", line_color=MUTED, row=1, col=1)
+
+    # Volume bars
+    colors_v = [GREEN if c >= o else RED
+                for c, o in zip(raw["Close"], raw["Open"])]
+    fig.add_trace(go.Bar(
+        x=raw.index, y=raw["Volume"],
+        marker_color=colors_v, showlegend=False, name="Volume",
+    ), row=2, col=1)
+
+    # RSI
+    fig.add_trace(go.Scatter(
+        x=df.index, y=df["RSI"],
+        line=dict(color=BLUE, width=1.5),
+        name="RSI", showlegend=False,
+    ), row=3, col=1)
+    fig.add_hline(y=70, line_dash="dash", line_color=RED,   line_width=0.8, row=3, col=1)
+    fig.add_hline(y=30, line_dash="dash", line_color=GREEN, line_width=0.8, row=3, col=1)
+
+    fig.update_layout(
+        paper_bgcolor=BG, plot_bgcolor=SURFACE,
+        font=dict(family="'JetBrains Mono', monospace", color=TEXT, size=12),
+        legend=dict(bgcolor=SURFACE, bordercolor=BORDER, borderwidth=1,
+                    x=0.01, y=0.99, font=dict(size=11)),
+        margin=dict(l=10, r=10, t=40, b=10),
+        xaxis_rangeslider_visible=False,
+        height=700,
+    )
+    for row in [1, 2, 3]:
+        fig.update_xaxes(
+            gridcolor=BORDER, showgrid=True,
+            zeroline=False, row=row, col=1,
+        )
+        fig.update_yaxes(
+            gridcolor=BORDER, showgrid=True,
+            zeroline=False, row=row, col=1,
+        )
+
+    # ── Metrics card (markdown) ───────────────────────────────────────────────
+    cur_price = float(raw["Close"].iloc[-1])
+    fst_fcast = future_prices[0]  if future_prices else cur_price
+    lst_fcast = future_prices[-1] if future_prices else cur_price
+    delta_pct = (lst_fcast - cur_price) / cur_price * 100
+    arrow     = "▲" if delta_pct >= 0 else "▼"
+    clr_tag   = "🟢" if delta_pct >= 0 else "🔴"
+
+    rsi_now = float(df["RSI"].iloc[-1])
+    rsi_sig = ("Overbought ⚠️" if rsi_now > 70
+               else "Oversold 💡"  if rsi_now < 30
+               else "Neutral ✅")
+
+    stats_md = f"""
+## {ticker} — {model_name}
+
+| Metric | Value |
+|--------|-------|
+| **Current Price** | `${cur_price:,.2f}` |
+| **Next-Period Forecast** | `${fst_fcast:,.2f}` |
+| **{future_days}-Day Forecast** | `${lst_fcast:,.2f}` |
+| **Expected Change** | `{clr_tag} {arrow} {abs(delta_pct):.2f}%` |
+
+---
+
+### Model Performance (test set)
+| | |
+|---|---|
+| MAE  | `${mae:.4f}` |
+| RMSE | `${rmse:.4f}` |
+| R²   | `{r2:.4f}` |
+| Accuracy proxy | `{acc:.1f}%` |
+
+---
+
+### Technical Signals
+| Indicator | Value | Signal |
+|-----------|-------|--------|
+| RSI (14) | `{rsi_now:.1f}` | {rsi_sig} |
+| MACD | `{float(df['MACD'].iloc[-1]):.4f}` | {'Bullish 📈' if float(df['MACD'].iloc[-1]) > 0 else 'Bearish 📉'} |
+| MA21 vs MA50 | — | {'Golden Cross ✨' if float(df['MA21'].iloc[-1]) > float(df['MA50'].iloc[-1]) else 'Death Cross 💀'} |
+
+> ⚠️ **Disclaimer:** This tool is for educational purposes only. Not financial advice.
+"""
+
+    return fig, stats_md, ""
+
+# ── UI layout ─────────────────────────────────────────────────────────────────
+css = f"""
+@import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@300;400;600&family=Inter:wght@400;600&display=swap');
+
+* {{ box-sizing: border-box; }}
+
+body, .gradio-container {{
+    background: {BG} !important;
+    color: {TEXT} !important;
+    font-family: 'Inter', sans-serif !important;
+}}
+
+/* Header */
+.app-header {{
+    background: {SURFACE};
+    border-bottom: 1px solid {BORDER};
+    padding: 28px 32px 20px;
+    margin-bottom: 24px;
+}}
+.app-header h1 {{
+    font-family: 'JetBrains Mono', monospace;
+    font-size: 2rem;
+    font-weight: 600;
+    color: {TEXT};
+    margin: 0 0 4px;
+    letter-spacing: -0.5px;
+}}
+.app-header p {{
+    color: {MUTED};
+    font-size: 0.9rem;
+    margin: 0;
+}}
+.accent {{ color: {GREEN}; }}
+
+/* Cards */
+.card {{
+    background: {SURFACE} !important;
+    border: 1px solid {BORDER} !important;
+    border-radius: 8px !important;
+    padding: 16px !important;
+}}
+
+/* Controls */
+label {{
+    color: {MUTED} !important;
+    font-size: 0.78rem !important;
+    font-weight: 600 !important;
+    text-transform: uppercase !important;
+    letter-spacing: 0.08em !important;
+    margin-bottom: 4px !important;
+}}
+input, select, .svelte-1gfkn6j {{
+    background: {BG} !important;
+    border: 1px solid {BORDER} !important;
+    color: {TEXT} !important;
+    border-radius: 6px !important;
+    font-family: 'JetBrains Mono', monospace !important;
+}}
+input:focus {{ border-color: {BLUE} !important; outline: none !important; }}
+
+/* Run button */
+.run-btn button {{
+    background: {GREEN} !important;
+    color: #0d1117 !important;
+    font-weight: 700 !important;
+    font-size: 0.95rem !important;
+    border: none !important;
+    border-radius: 6px !important;
+    padding: 12px 0 !important;
+    width: 100% !important;
+    cursor: pointer !important;
+    font-family: 'JetBrains Mono', monospace !important;
+    letter-spacing: 0.05em !important;
+    transition: opacity .15s;
+}}
+.run-btn button:hover {{ opacity: 0.85; }}
+
+/* Metrics markdown */
+.stats-box {{
+    background: {BG} !important;
+    border: 1px solid {BORDER} !important;
+    border-radius: 8px !important;
+    padding: 20px !important;
+    font-family: 'JetBrains Mono', monospace !important;
+    font-size: 0.82rem !important;
+}}
+.stats-box table {{ width: 100%; border-collapse: collapse; }}
+.stats-box td, .stats-box th {{
+    padding: 6px 10px;
+    border-bottom: 1px solid {BORDER};
+    text-align: left;
+}}
+.stats-box th {{ color: {MUTED}; font-weight: 600; }}
+.stats-box code {{
+    background: {SURFACE};
+    padding: 2px 6px;
+    border-radius: 4px;
+    color: {BLUE};
+}}
+
+/* Error box */
+.error-box textarea {{
+    background: transparent !important;
+    color: {RED} !important;
+    border: none !important;
+    font-family: 'JetBrains Mono', monospace !important;
+    font-size: 0.85rem !important;
+}}
+
+/* Ticker pills */
+.ticker-pills {{
+    display: flex; flex-wrap: wrap; gap: 6px;
+    margin-top: 8px;
+}}
+.ticker-pills button {{
+    background: {SURFACE} !important;
+    border: 1px solid {BORDER} !important;
+    color: {TEXT} !important;
+    border-radius: 4px !important;
+    padding: 3px 10px !important;
+    font-size: 0.75rem !important;
+    font-family: 'JetBrains Mono', monospace !important;
+    cursor: pointer !important;
+    transition: border-color .15s;
+}}
+.ticker-pills button:hover {{ border-color: {GREEN} !important; color: {GREEN} !important; }}
+
+/* Plotly panel */
+.plot-panel {{ border: 1px solid {BORDER}; border-radius: 8px; overflow: hidden; }}
+
+/* Slider */
+input[type=range] {{ accent-color: {BLUE}; }}
+
+/* Footer */
+.footer {{
+    text-align: center;
+    color: {MUTED};
+    font-size: 0.75rem;
+    margin-top: 32px;
+    padding: 16px;
+    border-top: 1px solid {BORDER};
+}}
+"""
+
+# ── Build Gradio app ──────────────────────────────────────────────────────────
+with gr.Blocks(css=css, title="StockSense — ML Stock Predictor") as demo:
+
+    # Header
+    gr.HTML("""
+    <div class="app-header">
+      <h1>📈 Stock<span class="accent">Sense</span></h1>
+      <p>Machine-learning powered stock analysis &amp; price forecasting · Educational use only</p>
+    </div>
+    """)
+
+    with gr.Row():
+        # ── Left panel: controls ─────────────────────────────────────────────
+        with gr.Column(scale=1, elem_classes="card"):
+
+            gr.HTML("<div style='font-size:0.78rem;color:#8b949e;font-weight:600;text-transform:uppercase;letter-spacing:.08em;margin-bottom:8px'>Quick Pick</div>")
+            ticker_pills_html = "".join(
+                f'<button onclick="document.querySelector(\'#ticker_input input\').value=\'{t}\';'
+                f'document.querySelector(\'#ticker_input input\').dispatchEvent(new Event(\'input\'))">{t}</button>'
+                for t in POPULAR_TICKERS
+            )
+            gr.HTML(f'<div class="ticker-pills">{ticker_pills_html}</div>')
+
+            ticker_input = gr.Textbox(
+                label="Ticker Symbol",
+                placeholder="e.g. AAPL, TSLA, MSFT …",
+                value="AAPL",
+                elem_id="ticker_input",
+            )
+
+            with gr.Row():
+                period_input = gr.Dropdown(
+                    label="History Period",
+                    choices=list(PERIODS.keys()),
+                    value="2 Years",
+                )
+                interval_input = gr.Dropdown(
+                    label="Interval",
+                    choices=list(INTERVALS.keys()),
+                    value="Daily",
+                )
+
+            model_input = gr.Dropdown(
+                label="ML Model",
+                choices=list(MODELS.keys()),
+                value="Random Forest",
+            )
+
+            future_input = gr.Slider(
+                label="Forecast Horizon (days)",
+                minimum=1, maximum=90, step=1, value=30,
+            )
+
+            run_btn = gr.Button("▶  Run Prediction", elem_classes="run-btn")
+            error_box = gr.Textbox(
+                visible=True, interactive=False,
+                show_label=False, elem_classes="error-box",
+            )
+
+            # Stats card below button
+            stats_md = gr.Markdown(
+                value="*Run a prediction to see metrics here.*",
+                elem_classes="stats-box",
+            )
+
+        # ── Right panel: chart ───────────────────────────────────────────────
+        with gr.Column(scale=3):
+            chart = gr.Plot(elem_classes="plot-panel", label="")
+
+    # Footer
+    gr.HTML(f"""
+    <div class="footer">
+        StockSense · Built with Gradio &amp; scikit-learn ·
+        Data via Yahoo Finance ·
+        <span style="color:{RED}">Not financial advice</span>
+    </div>
+    """)
+
+    # ── Wire up ───────────────────────────────────────────────────────────────
+    def run(ticker, period, interval, model, future):
+        fig, stats, err = predict_stock(ticker, period, interval, model, future)
+        return (
+            fig if fig else go.Figure(),
+            stats if stats else "",
+            err,
+        )
+
+    run_btn.click(
+        fn=run,
+        inputs=[ticker_input, period_input, interval_input, model_input, future_input],
+        outputs=[chart, stats_md, error_box],
+    )
+
+    # Auto-run on load
+    demo.load(
+        fn=run,
+        inputs=[ticker_input, period_input, interval_input, model_input, future_input],
+        outputs=[chart, stats_md, error_box],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,6 @@
+gradio>=4.0.0
+yfinance>=0.2.36
+pandas>=2.0.0
+numpy>=1.24.0
+scikit-learn>=1.3.0
+plotly>=5.17.0