Update app.py

app.py

@@ -1,3 +1,4 @@
+```python
 import sys
 import os
 import logging
@@ -7,910 +8,590 @@ import random
 from datetime import datetime, timedelta
 
 logging.basicConfig(
-    stream=sys.stdout, level=logging.INFO,
+    stream=sys.stdout,
+    level=logging.INFO,
     format="%(asctime)s [%(levelname)s] %(message)s"
 )
+
 logger = logging.getLogger("crypto_ml")
 logger.info("🚀 Starting HF Space container...")
 
+# ============================================================
+# IMPORTS
+# ============================================================
+
 try:
     import gradio as gr
     import yfinance as yf
     import pandas as pd
     import numpy as np
+
     from sklearn.ensemble import RandomForestClassifier
     from sklearn.preprocessing import MinMaxScaler
+
     import joblib
-    import requests
+
     logger.info("✅ All dependencies imported successfully")
+
 except Exception as e:
     logger.error(f"❌ Import failed: {e}")
     sys.exit(1)
 
+# ============================================================
+# CONFIG
+# ============================================================
+
 SYMBOL = "ETH-USD"
 FEE = 0.0015
 WINDOW = 20
+
 MODEL_PATH = "/tmp/rf_model.pkl"
 SCALER_PATH = "/tmp/scaler.pkl"
 
-
 # ============================================================
-# ROBUST DATA FETCHING — Multiple methods
+# CLEAN COLUMNS
 # ============================================================
 
 def _clean_columns(df):
-    """Universal column cleaner — handles any yfinance format."""
+
     if df is None or df.empty:
         return df
 
-    # Step 1: Handle MultiIndex
     if isinstance(df.columns, pd.MultiIndex):
-        logger.info(f"🔧 MultiIndex detected, levels: {[list(l) for l in df.levels]}")
-        # Try droplevel(0) — removes ticker name level
         try:
-            df.columns = df.columns.droplevel(0)
-            logger.info(f"🔧 After droplevel(0): {df.columns.tolist()}")
-        except Exception:
-            # Fallback: take last level
-            try:
-                df.columns = df.columns.get_level_values(-1)
-                logger.info(f"🔧 After get_level_values(-1): {df.columns.tolist()}")
-            except Exception:
-                logger.error("❌ Cannot fix MultiIndex columns")
-
-    # Step 2: Remove duplicates
+            df.columns = df.columns.get_level_values(0)
+        except:
+            df.columns = [
+                c[0] if isinstance(c, tuple) else c
+                for c in df.columns
+            ]
+
+    df.columns = [str(c).strip() for c in df.columns]
+
+    rename_map = {
+        "open": "Open",
+        "high": "High",
+        "low": "Low",
+        "close": "Close",
+        "adj close": "Close",
+        "volume": "Volume"
+    }
+
+    df.columns = [
+        rename_map.get(c.lower(), c)
+        for c in df.columns
+    ]
+
     df = df.loc[:, ~df.columns.duplicated()]
 
-    # Step 3: Standardize names
-    new_cols = []
-    for c in df.columns:
-        name = str(c).strip()
-        # Map common variations
-        name_map = {
-            "open": "Open", "high": "High", "low": "Low",
-            "close": "Close", "volume": "Volume",
-            "adj close": "Close", "adj_close": "Close",
-        }
-        name = name_map.get(name.lower(), name)
-        new_cols.append(name)
-    df.columns = new_cols
-
-    # Step 4: Ensure numeric
-    for col in df.columns:
-        if col in ["Open", "High", "Low", "Close", "Volume"]:
+    for col in ["Open", "High", "Low", "Close", "Volume"]:
+        if col in df.columns:
             df[col] = pd.to_numeric(df[col], errors="coerce")
 
-    # Step 5: Drop NaN in OHLCV
-    required = ["Open", "High", "Low", "Close", "Volume"]
-    available = [c for c in required if c in df.columns]
-    if available:
-        df = df.dropna(subset=available)
+    df = df.dropna()
 
-    logger.info(f"🔧 Cleaned columns: {df.columns.tolist()}, shape: {df.shape}")
-    return df
-
-
-def fetch_method_ticker_history(symbol, period="90d", interval="1h"):
-    """Method 1: yf.Ticker().history() — most reliable on cloud."""
-    try:
-        logger.info(f"📥 Method 1: Ticker.history({symbol}, {period}, {interval})")
-        ticker = yf.Ticker(symbol)
-        df = ticker.history(period=period, interval=interval, auto_adjust=True)
-        if df is not None and not df.empty:
-            logger.info(f"✅ Method 1 success: {len(df)} rows")
-            return df
-    except Exception as e:
-        logger.warning(f"⚠️ Method 1 failed: {e}")
-    return pd.DataFrame()
+    logger.info(f"✅ Cleaned columns: {df.columns.tolist()}")
 
+    return df
 
-def fetch_method_download(symbol, period="90d", interval="1h"):
-    """Method 2: yf.download() with session."""
-    try:
-        logger.info(f"📥 Method 2: yf.download({symbol}, {period}, {interval})")
-        session = requests.Session()
-        session.headers.update({
-            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
-        })
-        df = yf.download(
-            symbol, period=period, interval=interval,
-            progress=False, threads=False,
-            session=session
-        )
-        if df is not None and not df.empty:
-            logger.info(f"✅ Method 2 success: {len(df)} rows")
-            return df
-    except Exception as e:
-        logger.warning(f"⚠️ Method 2 failed: {e}")
-    return pd.DataFrame()
-
-
-def fetch_method_alt_symbol(period="90d", interval="1h"):
-    """Method 3: Try alternate symbol formats."""
-    alt_symbols = ["ETH-USD", "ETHUSD=X", "ETHUSDT=X", "ETH-USD"]
-    for sym in alt_symbols:
-        try:
-            logger.info(f"📥 Method 3: Trying symbol '{sym}'")
-            ticker = yf.Ticker(sym)
-            df = ticker.history(period=period, interval=interval, auto_adjust=True)
-            if df is not None and not df.empty and len(df) > 20:
-                logger.info(f"✅ Method 3 success with '{sym}': {len(df)} rows")
-                return df
-        except Exception as e:
-            logger.warning(f"⚠️ Method 3 symbol '{sym}' failed: {e}")
-        time.sleep(1)
-    return pd.DataFrame()
-
+# ============================================================
+# SYNTHETIC DATA
+# ============================================================
 
-def fetch_method_shorter_period(symbol):
-    """Method 4: Try shorter periods and intervals."""
-    configs = [
-        ("60d", "1h"),
-        ("30d", "1h"),
-        ("7d", "15m"),
-        ("60d", "1d"),
-        ("5d", "5m"),
-    ]
-    for period, interval in configs:
-        try:
-            logger.info(f"📥 Method 4: Ticker.history({symbol}, {period}, {interval})")
-            ticker = yf.Ticker(symbol)
-            df = ticker.history(period=period, interval=interval, auto_adjust=True)
-            if df is not None and not df.empty and len(df) > 20:
-                logger.info(f"✅ Method 4 success ({period}/{interval}): {len(df)} rows")
-                return df
-        except Exception as e:
-            logger.warning(f"⚠️ Method 4 ({period}/{interval}) failed: {e}")
-        time.sleep(1)
-    return pd.DataFrame()
+def generate_synthetic_data(n_bars=500):
 
+    logger.warning("⚠️ Using synthetic data")
 
-def generate_synthetic_data(n_bars=500):
-    """Method 5: Fallback synthetic data so the app ALWAYS works."""
-    logger.info("🎲 Generating synthetic ETH price data as fallback...")
-    random.seed(42)
     np.random.seed(42)
 
     end = datetime.utcnow()
-    start = end - timedelta(hours=n_bars * 4)
-    dates = pd.date_range(start=start, end=end, freq="4h")[:n_bars]
+    dates = pd.date_range(
+        end=end,
+        periods=n_bars,
+        freq="4h"
+    )
+
+    price = 3000
 
-    # Simulate realistic ETH price movement
-    price = 3000.0
-    opens, highs, lows, closes, volumes = [], [], [], [], []
+    opens = []
+    highs = []
+    lows = []
+    closes = []
+    volumes = []
 
     for _ in range(n_bars):
+
         ret = np.random.normal(0.0005, 0.015)
+
         open_p = price
         close_p = price * (1 + ret)
-        high_p = max(open_p, close_p) * (1 + abs(np.random.normal(0, 0.005)))
-        low_p = min(open_p, close_p) * (1 - abs(np.random.normal(0, 0.005)))
-        vol = np.random.lognormal(15, 1.5)
+
+        high_p = max(open_p, close_p) * (
+            1 + abs(np.random.normal(0, 0.005))
+        )
+
+        low_p = min(open_p, close_p) * (
+            1 - abs(np.random.normal(0, 0.005))
+        )
+
+        vol = np.random.lognormal(15, 1)
 
         opens.append(open_p)
         highs.append(high_p)
         lows.append(low_p)
         closes.append(close_p)
         volumes.append(vol)
+
         price = close_p
 
     df = pd.DataFrame({
-        "Open": opens, "High": highs, "Low": lows,
-        "Close": closes, "Volume": volumes
-    }, index=dates[:len(opens)])
+        "Open": opens,
+        "High": highs,
+        "Low": lows,
+        "Close": closes,
+        "Volume": volumes
+    }, index=dates)
 
-    df.index.name = "Datetime"
-    logger.info(f"✅ Synthetic data: {len(df)} rows, price range ${df['Close'].min():.0f}-${df['Close'].max():.0f}")
     return df
 
+# ============================================================
+# DOWNLOAD DATA
+# ============================================================
+
+def safe_download(
+    symbol=SYMBOL,
+    period="90d",
+    interval="1h"
+):
 
-def safe_download(symbol=SYMBOL, period="90d", interval="1h"):
-    """Try all methods, fall back to synthetic data."""
-    # Try real data methods in order
     methods = [
-        lambda: fetch_method_ticker_history(symbol, period, interval),
-        lambda: fetch_method_download(symbol, period, interval),
-        lambda: fetch_method_alt_symbol(period, interval),
-        lambda: fetch_method_shorter_period(symbol),
+        ("Ticker.history", lambda:
+            yf.Ticker(symbol).history(
+                period=period,
+                interval=interval,
+                auto_adjust=True
+            )
+        ),
+
+        ("yf.download", lambda:
+            yf.download(
+                tickers=symbol,
+                period=period,
+                interval=interval,
+                progress=False,
+                auto_adjust=True,
+                threads=False,
+                group_by="column"
+            )
+        )
     ]
 
-    for i, method in enumerate(methods):
+    for name, method in methods:
+
         try:
+
+            logger.info(f"📥 Trying {name}")
+
             df = method()
-            if df is not None and not df.empty and len(df) > 20:
+
+            if df is not None and not df.empty:
+
                 df = _clean_columns(df)
-                if "Close" in df.columns and len(df) > 20:
-                    return df, True  # True = real data
+
+                if len(df) > 30:
+                    logger.info(f"✅ {name} success")
+                    return df, True
+
         except Exception as e:
-            logger.warning(f"⚠️ Method {i+1} exception: {e}")
-        time.sleep(1)
+            logger.warning(f"⚠️ {name} failed: {e}")
 
-    # All real methods failed — use synthetic
-    logger.warning("⚠️ All real data methods failed. Using synthetic data.")
-    df = generate_synthetic_data(500)
-    df = _clean_columns(df)
-    return df, False  # False = synthetic data
+        time.sleep(1)
 
+    return generate_synthetic_data(), False
 
 # ============================================================
-# DATA PREPARATION
+# FEATURE ENGINEERING
 # ============================================================
 
 def fetch_and_prepare():
-    """Fetch OHLCV data, compute indicators, build windowed features."""
+
     df, is_real = safe_download()
 
     if df.empty:
-        raise ValueError("Data kosong setelah semua percobaan. Coba lagi nanti.")
+        raise ValueError("Data kosong")
 
-    data_source = "🔴 LIVE DATA" if is_real else "🟡 SYNTHETIC DATA (Yahoo Finance tidak tersedia)"
-    logger.info(f"📊 Data source: {data_source}")
+    if not isinstance(df.index, pd.DatetimeIndex):
+        df.index = pd.to_datetime(df.index)
 
-    # Resample to 4h if data has enough rows and isn't already 4h
-    if len(df) > 50:
-        try:
-            df = df.resample("4h").agg({
-                "Open": "first", "High": "max", "Low": "min",
-                "Close": "last", "Volume": "sum"
-            }).dropna()
-            logger.info(f"📊 After 4h resample: {len(df)} rows")
-        except Exception as e:
-            logger.warning(f"⚠️ Resample failed: {e}, using raw data")
+    try:
+
+        df = df.resample("4h").agg({
+            "Open": "first",
+            "High": "max",
+            "Low": "min",
+            "Close": "last",
+            "Volume": "sum"
+        }).dropna()
 
-    if len(df) < 30:
-        raise ValueError(f"Data terlalu sedikit setelah resample: {len(df)} baris.")
+    except Exception as e:
+        logger.warning(f"Resample failed: {e}")
+
+    if len(df) < 50:
+        raise ValueError("Data terlalu sedikit")
 
     df.columns = [c.lower() for c in df.columns]
-    df.index.name = "timestamp"
-    df = df.reset_index()
 
-    # ── RSI 14 ──
+    # ========================================================
+    # RSI
+    # ========================================================
+
     delta = df["close"].diff()
-    gain = delta.where(delta > 0, 0.0).rolling(14, min_periods=1).mean()
-    loss = (-delta.where(delta < 0, 0.0)).rolling(14, min_periods=1).mean()
+
+    gain = delta.where(delta > 0, 0).rolling(14).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(14).mean()
+
     rs = gain / loss.replace(0, np.nan)
-    df["rsi"] = (100 - (100 / (1 + rs))).fillna(50)
-
-    # ── Bollinger Bands ──
-    sma20 = df["close"].rolling(20, min_periods=1).mean()
-    std20 = df["close"].rolling(20, min_periods=1).std().fillna(0)
-    df["bb_upper"] = sma20 + std20 * 2
-    df["bb_lower"] = sma20 - std20 * 2
-    bb_width = (df["bb_upper"] - df["bb_lower"]).replace(0, np.nan)
-    df["bb_pct"] = ((df["close"] - df["bb_lower"]) / bb_width).fillna(0.5).clip(0, 1)
-
-    # ── MACD ──
-    ema12 = df["close"].ewm(span=12, adjust=False).mean()
-    ema26 = df["close"].ewm(span=26, adjust=False).mean()
+
+    df["rsi"] = (
+        100 - (100 / (1 + rs))
+    ).fillna(50)
+
+    # ========================================================
+    # BOLLINGER
+    # ========================================================
+
+    sma20 = df["close"].rolling(20).mean()
+    std20 = df["close"].rolling(20).std()
+
+    df["bb_upper"] = sma20 + 2 * std20
+    df["bb_lower"] = sma20 - 2 * std20
+
+    bb_width = (
+        df["bb_upper"] - df["bb_lower"]
+    ).replace(0, np.nan)
+
+    df["bb_pct"] = (
+        (df["close"] - df["bb_lower"]) / bb_width
+    ).fillna(0.5)
+
+    # ========================================================
+    # MACD
+    # ========================================================
+
+    ema12 = df["close"].ewm(span=12).mean()
+    ema26 = df["close"].ewm(span=26).mean()
+
     df["macd"] = ema12 - ema26
-    df["macd_signal"] = df["macd"].ewm(span=9, adjust=False).mean()
-    df["macd_hist"] = df["macd"] - df["macd_signal"]
+    df["macd_signal"] = df["macd"].ewm(span=9).mean()
+    df["macd_hist"] = (
+        df["macd"] - df["macd_signal"]
+    )
+
+    # ========================================================
+    # EMA
+    # ========================================================
 
-    # ── EMAs ──
-    df["ema_9"] = df["close"].ewm(span=9, adjust=False).mean()
-    df["ema_21"] = df["close"].ewm(span=21, adjust=False).mean()
+    df["ema_9"] = df["close"].ewm(span=9).mean()
+    df["ema_21"] = df["close"].ewm(span=21).mean()
 
-    # ── Momentum / Volume ──
-    df["vol_change"] = df["volume"].pct_change().fillna(0).replace([np.inf, -np.inf], 0)
-    df["return_1"] = df["close"].pct_change(1).fillna(0).replace([np.inf, -np.inf], 0)
-    df["return_4"] = df["close"].pct_change(4).fillna(0).replace([np.inf, -np.inf], 0)
+    # ========================================================
+    # RETURNS
+    # ========================================================
+
+    df["vol_change"] = (
+        df["volume"].pct_change()
+    )
+
+    df["return_1"] = (
+        df["close"].pct_change(1)
+    )
+
+    df["return_4"] = (
+        df["close"].pct_change(4)
+    )
+
+    # ========================================================
+    # LABEL
+    # ========================================================
 
-    # ── Label ──
     df["next_close"] = df["close"].shift(-1)
-    df["label"] = (df["next_close"] > df["close"] * (1 + FEE)).astype(int)
 
-    # Final cleanup
-    df = df.replace([np.inf, -np.inf], np.nan)
-    df = df.dropna().reset_index(drop=True)
+    df["label"] = (
+        df["next_close"] >
+        df["close"] * (1 + FEE)
+    ).astype(int)
 
-    if len(df) < WINDOW + 5:
-        raise ValueError(
-            f"Data tidak cukup setelah preprocessing: {len(df)} baris. "
-            f"Butuh minimal {WINDOW + 5}."
-        )
+    # ========================================================
+    # CLEANUP
+    # ========================================================
+
+    df = df.replace([np.inf, -np.inf], np.nan)
+    df = df.dropna()
 
     feat_cols = [
-        "open", "high", "low", "close", "volume",
-        "rsi", "bb_upper", "bb_lower", "bb_pct",
-        "macd", "macd_signal", "macd_hist",
-        "ema_9", "ema_21", "vol_change", "return_1", "return_4"
+        "open",
+        "high",
+        "low",
+        "close",
+        "volume",
+        "rsi",
+        "bb_upper",
+        "bb_lower",
+        "bb_pct",
+        "macd",
+        "macd_signal",
+        "macd_hist",
+        "ema_9",
+        "ema_21",
+        "vol_change",
+        "return_1",
+        "return_4"
     ]
 
-    missing_feats = [f for f in feat_cols if f not in df.columns]
-    if missing_feats:
-        raise ValueError(f"Missing feature columns: {missing_feats}")
+    X_list = []
+    y_list = []
 
-    X_list, y_list = [], []
     for i in range(WINDOW, len(df)):
-        window_data = df.iloc[i - WINDOW:i][feat_cols].values
-        if window_data.shape[0] == WINDOW and not np.isnan(window_data).any() and not np.isinf(window_data).any():
-            X_list.append(window_data.flatten())
-            y_list.append(int(df.iloc[i]["label"]))
 
-    if len(X_list) < 10:
-        raise ValueError(f"Tidak cukup sampel valid: {len(X_list)}.")
+        window_data = df.iloc[
+            i-WINDOW:i
+        ][feat_cols].values
 
-    X = np.array(X_list, dtype=np.float64)
-    y = np.array(y_list, dtype=np.int32)
-    X = np.nan_to_num(X, nan=0.0, posinf=0.0, neginf=0.0)
+        if np.isnan(window_data).any():
+            continue
 
-    n_buy = int(np.sum(y == 1))
-    n_sell = int(np.sum(y == 0))
-    logger.info(f"✅ Features ready: X={X.shape}, BUY={n_buy}, SELL={n_sell}, source={data_source}")
-    return X, y, df, feat_cols, is_real
+        X_list.append(window_data.flatten())
+        y_list.append(df.iloc[i]["label"])
 
+    X = np.array(X_list, dtype=np.float32)
+    y = np.array(y_list)
 
-def get_market_info():
-    """Quick snapshot for price header."""
-    try:
-        df, is_real = safe_download(SYMBOL, period="5d", interval="1h")
-        if df.empty or len(df) < 2:
-            return {"price": 0, "change_pct": 0, "high_24h": 0, "low_24h": 0, "volume": 0, "is_real": False}
-
-        cur = float(df["Close"].iloc[-1])
-        prev = float(df["Close"].iloc[0])
-        tail_n = min(6, len(df))
-        return {
-            "price": cur,
-            "change_pct": ((cur - prev) / prev * 100) if prev > 0 else 0,
-            "high_24h": float(df["High"].tail(tail_n).max()),
-            "low_24h": float(df["Low"].tail(tail_n).min()),
-            "volume": float(df["Volume"].tail(tail_n).sum()),
-            "is_real": is_real,
-        }
-    except Exception as e:
-        logger.error(f"Market info error: {e}")
-        return {"price": 0, "change_pct": 0, "high_24h": 0, "low_24h": 0, "volume": 0, "is_real": False}
+    X = np.nan_to_num(X)
 
+    return X, y, df, feat_cols, is_real
 
-# ── Training ──
+# ============================================================
+# TRAIN
+# ============================================================
 
 def run_train():
+
     try:
-        logger.info("🧠 Starting training...")
-        X, y, df_full, feat_cols, is_real = fetch_and_prepare()
 
-        n_buy = int(np.sum(y == 1))
-        n_sell = int(np.sum(y == 0))
-        if n_buy < 3 or n_sell < 3:
-            raise ValueError(f"Kelas tidak seimbang: BUY={n_buy}, SELL={n_sell}.")
+        X, y, df, feat_cols, is_real = fetch_and_prepare()
 
         scaler = MinMaxScaler()
+
         X_scaled = scaler.fit_transform(X)
 
-        split = max(int(len(X) * 0.88), 10)
-        if split >= len(X) - 3:
-            split = len(X) - 3
+        split = int(len(X_scaled) * 0.88)
+
+        X_train = X_scaled[:split]
+        X_test = X_scaled[split:]
 
-        X_train, X_test = X_scaled[:split], X_scaled[split:]
-        y_train, y_test = y[:split], y[split:]
+        y_train = y[:split]
+        y_test = y[split:]
 
         model = RandomForestClassifier(
-            n_estimators=100, max_depth=8,
-            min_samples_split=5, min_samples_leaf=2,
-            random_state=42, n_jobs=1,
-            class_weight="balanced"
+            n_estimators=100,
+            max_depth=8,
+            random_state=42,
+            class_weight="balanced",
+            n_jobs=1
         )
+
         model.fit(X_train, y_train)
 
-        acc = float(model.score(X_test, y_test))
-        y_pred = model.predict(X_test)
-        buy_sig = int(np.sum(y_pred == 1))
-        sell_sig = int(np.sum(y_pred == 0))
-
-        wins = int(np.sum((y_pred == 1) & (y_test == 1)))
-        win_rate = (wins / max(buy_sig, 1)) * 100
-
-        # PnL
-        start_idx = split + WINDOW
-        end_idx = start_idx + len(y_test)
-        if end_idx <= len(df_full):
-            test_closes = df_full.iloc[start_idx:end_idx]["close"].values
-        else:
-            test_closes = df_full.iloc[-(len(y_test) + 1):]["close"].values
-
-        pnl = 0.0
-        for i in range(min(len(y_pred), len(test_closes) - 1)):
-            if y_pred[i] == 1:
-                ret = (test_closes[i + 1] - test_closes[i]) / test_closes[i] - FEE
-                pnl += ret
-        pnl_pct = pnl * 100
+        acc = model.score(X_test, y_test)
+
+        # ====================================================
+        # FIX FEATURE IMPORTANCE
+        # ====================================================
 
         importances = model.feature_importances_
-        top_n = min(5, len(feat_cols))
-        top5_idx = np.argsort(importances)[-top_n:][::-1]
-        top5 = [(feat_cols[i], float(importances[i])) for i in top5_idx]
+
+        imp_matrix = importances.reshape(
+            WINDOW,
+            len(feat_cols)
+        )
+
+        mean_importance = imp_matrix.mean(axis=0)
+
+        top_idx = np.argsort(mean_importance)[-5:][::-1]
+
+        top_features = [
+            (
+                feat_cols[i],
+                float(mean_importance[i])
+            )
+            for i in top_idx
+        ]
 
         joblib.dump(model, MODEL_PATH)
         joblib.dump(scaler, SCALER_PATH)
 
-        logger.info(f"✅ Training done — acc={acc:.4f}")
-        return _html_train(acc, win_rate, pnl_pct, len(X_train),
-                           len(X_test), buy_sig, sell_sig, top5, df_full, is_real)
+        txt = f"""
+✅ MODEL TRAINED
+
+Accuracy: {acc:.4f}
+
+Top Features:
+
+"""
+
+        for name, score in top_features:
+            txt += f"\n{name}: {score:.4f}"
+
+        return txt
+
     except Exception as e:
-        tb = traceback.format_exc()
-        logger.error(f"❌ Train failed: {e}")
-        logger.error(tb)
-        return _html_error(str(e), tb)
 
+        logger.error(traceback.format_exc())
 
-# ── Prediction ──
+        return f"❌ ERROR:\n\n{e}"
+
+# ============================================================
+# PREDICT
+# ============================================================
 
 def run_predict():
+
     try:
+
         if not os.path.exists(MODEL_PATH):
-            return _html_warn("Model belum di-training. Klik <b>⚡ Train Model</b> terlebih dahulu.")
+            return "⚠️ Train model terlebih dahulu"
+
+        X, y, df, feat_cols, is_real = fetch_and_prepare()
 
-        X, y, df_full, feat_cols, is_real = fetch_and_prepare()
         scaler = joblib.load(SCALER_PATH)
         model = joblib.load(MODEL_PATH)
 
-        if len(df_full) < WINDOW:
-            return _html_warn(f"Data tidak cukup: {len(df_full)} baris, butuh {WINDOW}.")
+        latest = df.iloc[
+            -WINDOW:
+        ][feat_cols].values.flatten()
+
+        latest = latest.reshape(1, -1)
+
+        latest = np.nan_to_num(latest)
 
-        latest = df_full.iloc[-WINDOW:][feat_cols].values.flatten().reshape(1, -1)
-        latest = np.nan_to_num(latest, nan=0.0, posinf=0.0, neginf=0.0)
         latest_scaled = scaler.transform(latest)
 
-        pred = int(model.predict(latest_scaled)[0])
-        proba = model.predict_proba(latest_scaled)[0]
-        confidence = float(max(proba)) * 100
+        pred = model.predict(latest_scaled)[0]
 
-        row = df_full.iloc[-1]
-        price = float(row["close"])
-        rsi = float(row.get("rsi", 50))
-        macd_val = float(row.get("macd", 0))
-        bb_pct = float(row.get("bb_pct", 0.5))
+        proba = model.predict_proba(
+            latest_scaled
+        )[0]
+
+        confidence = float(np.max(proba)) * 100
 
         signal = "BUY" if pred == 1 else "SELL"
-        logger.info(f"🔮 {signal} conf={confidence:.1f}%")
-        return _html_signal(signal, confidence, price, rsi, macd_val, bb_pct, is_real)
-    except Exception as e:
-        tb = traceback.format_exc()
-        logger.error(f"❌ Predict failed: {e}")
-        logger.error(tb)
-        return _html_error(str(e), tb)
 
+        price = float(df.iloc[-1]["close"])
 
-def refresh_price():
-    return _html_price_header(get_market_info())
+        return f"""
+🔮 SIGNAL: {signal}
 
+Confidence: {confidence:.2f}%
+
+Price: ${price:,.2f}
+
+RSI: {df.iloc[-1]['rsi']:.2f}
+MACD: {df.iloc[-1]['macd']:.2f}
+"""
+
+    except Exception as e:
+
+        logger.error(traceback.format_exc())
+
+        return f"❌ ERROR:\n\n{e}"
 
 # ============================================================
-# HTML BUILDERS
+# MARKET INFO
 # ============================================================
 
-def _html_data_badge(is_real):
-    if is_real:
-        return '<span style="background:rgba(14,203,129,.15);color:#0ECB81;font-size:9px;padding:2px 8px;border-radius:10px;font-weight:600;">🔴 LIVE</span>'
-    else:
-        return '<span style="background:rgba(240,185,11,.15);color:#F0B90B;font-size:9px;padding:2px 8px;border-radius:10px;font-weight:600;">🟡 SIMULATED</span>'
+def get_market_info():
 
+    try:
 
-def _html_price_header(info):
-    p = info.get("price", 0)
-    chg = info.get("change_pct", 0)
-    hi, lo = info.get("high_24h", 0), info.get("low_24h", 0)
-    vol = info.get("volume", 0)
-    is_real = info.get("is_real", False)
+        df, is_real = safe_download(
+            SYMBOL,
+            period="5d",
+            interval="1h"
+        )
 
-    badge = _html_data_badge(is_real)
+        price = float(df["Close"].iloc[-1])
 
-    if p == 0:
         return f"""
-        <div style="display:flex;align-items:center;gap:28px;padding:14px 24px;
-                    background:#1E2329;border-bottom:1px solid #2B3139;
-                    font-family:'Inter',sans-serif;">
-          <span style="color:#F0B90B;font-size:17px;font-weight:800;">ETH / USDT</span>
-          <span style="color:#5E6673;font-size:13px;">⏳ Loading price data...</span>
-          {badge}
-        </div>"""
-
-    cc = "#0ECB81" if chg >= 0 else "#F6465D"
-    sign = "+" if chg >= 0 else ""
-    vs = f"${vol/1e6:.1f}M" if vol >= 1e6 else f"${vol/1e3:.0f}K"
-    return f"""
-    <div style="display:flex;align-items:center;gap:28px;padding:14px 24px;
-                background:#1E2329;border-bottom:1px solid #2B3139;flex-wrap:wrap;
-                font-family:'Inter',sans-serif;">
-      <div style="display:flex;align-items:baseline;gap:8px;">
-        <span style="color:#F0B90B;font-size:17px;font-weight:800;">ETH / USDT</span>
-        <span style="color:#5E6673;font-size:11px;">Perpetual</span>
-        {badge}
-      </div>
-      <div style="display:flex;align-items:baseline;gap:8px;">
-        <span style="color:{cc};font-size:26px;font-weight:800;">${p:,.2f}</span>
-        <span style="color:{cc};font-size:13px;font-weight:600;">{sign}{chg:.2f}%</span>
-      </div>
-      <div style="display:flex;gap:22px;flex-wrap:wrap;">
-        <div><div style="color:#5E6673;font-size:10px;text-transform:uppercase;">24h High</div>
-             <div style="color:#EAECEF;font-size:13px;font-weight:500;">${hi:,.2f}</div></div>
-        <div><div style="color:#5E6673;font-size:10px;text-transform:uppercase;">24h Low</div>
-             <div style="color:#EAECEF;font-size:13px;font-weight:500;">${lo:,.2f}</div></div>
-        <div><div style="color:#5E6673;font-size:10px;text-transform:uppercase;">24h Volume</div>
-             <div style="color:#EAECEF;font-size:13px;font-weight:500;">{vs}</div></div>
-      </div>
-    </div>"""
-
-
-def _html_signal(signal, confidence, price, rsi, macd_val, bb_pct, is_real=True):
-    buy = signal == "BUY"
-    sc = "#0ECB81" if buy else "#F6465D"
-    sbg = "rgba(14,203,129,.12)" if buy else "rgba(246,70,93,.12)"
-    sbd = "rgba(14,203,129,.35)" if buy else "rgba(246,70,93,.35)"
-    icon = "▲" if buy else "▼"
-    tag = "LONG" if buy else "SHORT"
-    cw = min(confidence, 100)
-    cc = "#0ECB81" if confidence >= 60 else ("#F0B90B" if confidence >= 40 else "#F6465D")
-
-    rc = "#0ECB81" if 30 <= rsi <= 70 else ("#F6465D" if rsi > 70 else "#F0B90B")
-    rs = "Overbought" if rsi > 70 else ("Oversold" if rsi < 30 else "Neutral")
-    mc = "#0ECB81" if macd_val > 0 else "#F6465D"
-    ms = "Bullish" if macd_val > 0 else "Bearish"
-    bc = "#F6465D" if bb_pct > .8 else ("#0ECB81" if bb_pct < .2 else "#848E9C")
-    bs = "Upper" if bb_pct > .8 else ("Lower" if bb_pct < .2 else "Mid")
-    ts = datetime.utcnow().strftime("%Y-%m-%d %H:%M UTC")
-
-    badge = _html_data_badge(is_real)
-
-    return f"""
-    <div style="background:#1E2329;border-radius:8px;border:1px solid {sbd};overflow:hidden;
-                font-family:'Inter',sans-serif;">
-      <div style="background:{sbg};padding:22px 20px;text-align:center;border-bottom:1px solid {sbd};">
-        <div style="font-size:44px;color:{sc};font-weight:900;letter-spacing:5px;">{icon} {signal}</div>
-        <div style="color:{sc};font-size:13px;margin-top:2px;">{tag} SIGNAL · 4H TIMEFRAME</div>
-      </div>
-      <div style="padding:14px 20px;border-bottom:1px solid #2B3139;">
-        <div style="display:flex;justify-content:space-between;margin-bottom:6px;">
-          <span style="color:#848E9C;font-size:11px;">CONFIDENCE</span>
-          <span style="color:{cc};font-size:13px;font-weight:700;">{confidence:.1f}%</span>
-        </div>
-        <div style="background:#2B3139;border-radius:4px;height:7px;overflow:hidden;">
-          <div style="background:{cc};height:100%;width:{cw}%;border-radius:4px;
-                      transition:width .6s ease;"></div>
-        </div>
-      </div>
-      <div style="display:grid;grid-template-columns:1fr 1fr 1fr;border-bottom:1px solid #2B3139;">
-        <div style="padding:10px 14px;border-right:1px solid #2B3139;text-align:center;">
-          <div style="color:#5E6673;font-size:9px;text-transform:uppercase;margin-bottom:3px;">RSI(14)</div>
-          <div style="color:{rc};font-size:17px;font-weight:700;">{rsi:.1f}</div>
-          <div style="color:{rc};font-size:9px;">{rs}</div></div>
-        <div style="padding:10px 14px;border-right:1px solid #2B3139;text-align:center;">
-          <div style="color:#5E6673;font-size:9px;text-transform:uppercase;margin-bottom:3px;">MACD</div>
-          <div style="color:{mc};font-size:17px;font-weight:700;">{macd_val:.2f}</div>
-          <div style="color:{mc};font-size:9px;">{ms}</div></div>
-        <div style="padding:10px 14px;text-align:center;">
-          <div style="color:#5E6673;font-size:9px;text-transform:uppercase;margin-bottom:3px;">BB %B</div>
-          <div style="color:{bc};font-size:17px;font-weight:700;">{bb_pct:.2f}</div>
-          <div style="color:{bc};font-size:9px;">{bs}</div></div>
-      </div>
-      <div style="padding:10px 20px;display:flex;justify-content:space-between;align-items:center;">
-        <span style="color:#5E6673;font-size:10px;">⏰ {ts}</span>
-        <div style="display:flex;align-items:center;gap:8px;">
-          {badge}
-          <span style="color:#5E6673;font-size:10px;">Entry ≈ ${price:,.2f}</span>
-        </div>
-      </div>
-    </div>"""
-
-
-def _html_train(acc, win_rate, pnl_pct, n_tr, n_te, buys, sells, top5, df_full, is_real=True):
-    ac = "#0ECB81" if acc >= .58 else ("#F0B90B" if acc >= .50 else "#F6465D")
-    wc = "#0ECB81" if win_rate >= 55 else ("#F0B90B" if win_rate >= 45 else "#F6465D")
-    pc = "#0ECB81" if pnl_pct >= 0 else "#F6465D"
-    ps = "+" if pnl_pct >= 0 else ""
-    ts = datetime.utcnow().strftime("%Y-%m-%d %H:%M UTC")
-    badge = _html_data_badge(is_real)
-
-    fi = ""
-    for name, imp in top5:
-        w = min(imp * 500, 100)
-        fi += f"""<div style="display:flex;justify-content:space-between;align-items:center;
-                   padding:5px 0;border-bottom:1px solid #2B3139;">
-          <span style="color:#EAECEF;font-size:11px;">{name.upper()}</span>
-          <div style="display:flex;align-items:center;gap:6px;">
-            <div style="background:#2B3139;border-radius:2px;height:5px;width:70px;overflow:hidden;">
-              <div style="background:#F0B90B;height:100%;width:{w}%;border-radius:2px;"></div></div>
-            <span style="color:#848E9C;font-size:10px;min-width:36px;text-align:right;">{imp*100:.1f}%</span>
-          </div></div>"""
-
-    n_recent = min(5, len(df_full))
-    recent = df_full.tail(n_recent)
-    rows = ""
-    for _, r in recent.iterrows():
-        ts_val = r.get("timestamp", None)
-        t = pd.to_datetime(ts_val).strftime("%m/%d %H:%M") if pd.notna(ts_val) else ""
-        c = float(r.get("close", 0))
-        ch = float(r.get("return_1", 0)) * 100
-        ch2 = "#0ECB81" if ch >= 0 else "#F6465D"
-        rv = float(r.get("rsi", 50))
-        rows += f"""<tr style="border-bottom:1px solid #2B3139;">
-          <td style="color:#848E9C;font-size:10px;padding:5px 6px;">{t}</td>
-          <td style="color:#EAECEF;font-size:10px;padding:5px 6px;text-align:right;">${c:,.2f}</td>
-          <td style="color:{ch2};font-size:10px;padding:5px 6px;text-align:right;">{ch:+.2f}%</td>
-          <td style="color:#848E9C;font-size:10px;padding:5px 6px;text-align:right;">{rv:.1f}</td></tr>"""
-
-    data_notice = ""
-    if not is_real:
-        data_notice = """
-        <div style="background:rgba(240,185,11,.08);border:1px solid rgba(240,185,11,.25);
-                    border-radius:4px;padding:8px 12px;margin:8px 18px;font-size:11px;">
-          <span style="color:#F0B90B;font-weight:600;">⚠️ Yahoo Finance tidak tersedia.</span>
-          <span style="color:#848E9C;"> Model dilatih menggunakan data simulasi. 
-          Sinyal mungkin tidak akurat untuk trading real.</span>
-        </div>"""
-
-    return f"""
-    <div style="background:#1E2329;border-radius:8px;border:1px solid #2B3139;overflow:hidden;
-                font-family:'Inter',sans-serif;">
-      <div style="padding:14px 20px;border-bottom:1px solid #2B3139;display:flex;
-                  justify-content:space-between;align-items:center;">
-        <div style="display:flex;align-items:center;gap:8px;">
-          <span style="color:#F0B90B;font-size:13px;font-weight:700;">✅ MODEL TRAINED</span>
-          {badge}
-        </div>
-        <span style="color:#5E6673;font-size:10px;">{ts}</span>
-      </div>
-      {data_notice}
-      <div style="display:grid;grid-template-columns:1fr 1fr 1fr 1fr;border-bottom:1px solid #2B3139;">
-        <div style="padding:10px 14px;border-right:1px solid #2B3139;text-align:center;">
-          <div style="color:#5E6673;font-size:9px;text-transform:uppercase;margin-bottom:2px;">Test Acc</div>
-          <div style="color:{ac};font-size:18px;font-weight:700;">{acc*100:.1f}%</div></div>
-        <div style="padding:10px 14px;border-right:1px solid #2B3139;text-align:center;">
-          <div style="color:#5E6673;font-size:9px;text-transform:uppercase;margin-bottom:2px;">Win Rate</div>
-          <div style="color:{wc};font-size:18px;font-weight:700;">{win_rate:.1f}%</div></div>
-        <div style="padding:10px 14px;border-right:1px solid #2B3139;text-align:center;">
-          <div style="color:#5E6673;font-size:9px;text-transform:uppercase;margin-bottom:2px;">Sim PnL</div>
-          <div style="color:{pc};font-size:18px;font-weight:700;">{ps}{pnl_pct:.2f}%</div></div>
-        <div style="padding:10px 14px;text-align:center;">
-          <div style="color:#5E6673;font-size:9px;text-transform:uppercase;margin-bottom:2px;">Signals</div>
-          <div style="font-size:18px;font-weight:700;">
-            <span style="color:#0ECB81;">{buys}B</span>
-            <span style="color:#5E6673;">/</span>
-            <span style="color:#F6465D;">{sells}S</span></div></div>
-      </div>
-      <div style="display:grid;grid-template-columns:1fr 1fr;border-bottom:1px solid #2B3139;">
-        <div style="padding:14px 18px;border-right:1px solid #2B3139;">
-          <div style="color:#848E9C;font-size:10px;text-transform:uppercase;margin-bottom:6px;">
-            Top Feature Importance</div>{fi}
-        </div>
-        <div style="padding:14px 18px;">
-          <div style="color:#848E9C;font-size:10px;text-transform:uppercase;margin-bottom:6px;">
-            Recent 4H Candles</div>
-          <table style="width:100%;border-collapse:collapse;">
-            <thead><tr style="border-bottom:1px solid #363C45;">
-              <th style="color:#5E6673;font-size:9px;text-transform:uppercase;padding:4px 6px;text-align:left;">Time</th>
-              <th style="color:#5E6673;font-size:9px;text-transform:uppercase;padding:4px 6px;text-align:right;">Close</th>
-              <th style="color:#5E6673;font-size:9px;text-transform:uppercase;padding:4px 6px;text-align:right;">Chg</th>
-              <th style="color:#5E6673;font-size:9px;text-transform:uppercase;padding:4px 6px;text-align:right;">RSI</th>
-            </tr></thead><tbody>{rows}</tbody></table>
-        </div>
-      </div>
-      <div style="padding:8px 18px;display:flex;justify-content:space-between;">
-        <span style="color:#5E6673;font-size:10px;">Train: {n_tr} · Test: {n_te}</span>
-        <span style="color:#5E6673;font-size:10px;">Split 88% / 12%</span>
-      </div>
-    </div>"""
-
-
-def _html_error(msg, tb=""):
-    tb_lines = tb.strip().split("\n")[-6:] if tb else []
-    tb_html = ""
-    if tb_lines:
-        tb_text = "<br>".join(tb_lines)
-        tb_html = f"""<details style="margin-top:10px;text-align:left;">
-          <summary style="color:#5E6673;font-size:10px;cursor:pointer;">Show Details</summary>
-          <pre style="color:#848E9C;font-size:9px;background:#2B3139;padding:8px;border-radius:4px;
-                     overflow-x:auto;margin-top:4px;white-space:pre-wrap;">{tb_text}</pre>
-        </details>"""
-
-    return f"""<div style="background:#1E2329;border-radius:8px;border:1px solid rgba(246,70,93,.35);
-              padding:20px;text-align:center;font-family:'Inter',sans-serif;">
-      <div style="color:#F6465D;font-size:18px;font-weight:700;margin-bottom:6px;">❌ ERROR</div>
-      <div style="color:#848E9C;font-size:12px;word-break:break-word;">{msg}</div>
-      {tb_html}
-    </div>"""
-
-
-def _html_warn(msg):
-    return f"""<div style="background:#1E2329;border-radius:8px;border:1px solid rgba(240,185,11,.35);
-              padding:20px;text-align:center;font-family:'Inter',sans-serif;">
-      <div style="color:#F0B90B;font-size:18px;font-weight:700;margin-bottom:6px;">⚠️</div>
-      <div style="color:#848E9C;font-size:12px;">{msg}</div>
-    </div>"""
+ETH PRICE
 
+${price:,.2f}
 
-# ============================================================
-# TRADINGVIEW & CSS
-# ============================================================
-
-TRADINGVIEW_HTML = """
-<div style="width:100%;height:440px;background:#1E2329;border-radius:8px;overflow:hidden;
-            border:1px solid #2B3139;">
-  <iframe src="https://s.tradingview.com/widgetembed/?frameElementId=tv&symbol=BINANCE%3AETHUSDT&interval=240&hidesidetoolbar=0&symboledit=1&saveimage=1&toolbarbg=%231E2329&studies=%5B%7B%22id%22%3A%22MASimple%40tv-basicstudies%22%7D%2C%7B%22id%22%3A%22RSI%40tv-basicstudies%22%7D%5D&theme=dark&style=1&timezone=UTC&withdateranges=1&showpopupbutton=0&studies_overrides=%7B%7D&overrides=%7B%7D&enabled_features=%5B%5D&disabled_features=%5B%5D&locale=en"
-    style="border:none;width:100%;height:100%;" allowfullscreen></iframe>
-</div>
+Source:
+{"LIVE DATA" if is_real else "SIMULATED"}
 """
 
-BINANCE_CSS = """
-@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap');
-
-:root {
-  --bg-primary: #181A20;
-  --bg-secondary: #1E2329;
-  --bg-card: #2B3139;
-  --text-primary: #EAECEF;
-  --text-secondary: #848E9C;
-  --text-muted: #5E6673;
-  --accent: #F0B90B;
-  --accent-hover: #F8D12F;
-  --green: #0ECB81;
-  --red: #F6465D;
-  --border: #2B3139;
-  --border-light: #363C45;
-}
-
-.gradio-container {
-  max-width: 1200px !important;
-  margin: 0 auto !important;
-  padding: 0 !important;
-  background: var(--bg-primary) !important;
-  font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important;
-}
-.contain, .block { background: var(--bg-primary) !important; }
-footer { display: none !important; }
-
-button {
-  font-family: 'Inter', sans-serif !important;
-  font-weight: 700 !important;
-  border-radius: 6px !important;
-  transition: all .2s ease !important;
-  text-transform: uppercase !important;
-  letter-spacing: .5px !important;
-  font-size: 13px !important;
-  cursor: pointer !important;
-}
-button.primary {
-  background: var(--accent) !important;
-  color: var(--bg-primary) !important;
-  border: none !important;
-  box-shadow: 0 2px 8px rgba(240,185,11,.25) !important;
-}
-button.primary:hover {
-  background: var(--accent-hover) !important;
-  box-shadow: 0 4px 16px rgba(240,185,11,.4) !important;
-  transform: translateY(-1px) !important;
-}
-button.secondary {
-  background: var(--bg-card) !important;
-  color: var(--text-primary) !important;
-  border: 1px solid var(--border-light) !important;
-}
-button.secondary:hover {
-  background: var(--border-light) !important;
-  border-color: var(--accent) !important;
-}
-
-input, textarea, select {
-  background: var(--bg-card) !important;
-  color: var(--text-primary) !important;
-  border: 1px solid var(--border-light) !important;
-  border-radius: 6px !important;
-}
-
-::-webkit-scrollbar { width: 5px; }
-::-webkit-scrollbar-track { background: var(--bg-primary); }
-::-webkit-scrollbar-thumb { background: var(--border-light); border-radius: 3px; }
-::-webkit-scrollbar-thumb:hover { background: var(--text-muted); }
-
-.gap { gap: 12px !important; }
-"""
+    except Exception as e:
+        return str(e)
 
 # ============================================================
-# GRADIO UI
+# UI
 # ============================================================
 
-logger.info("🎨 Building Gradio UI...")
-
-with gr.Blocks(
-    title="CryptoML Terminal — ETH/USDT",
-    css=BINANCE_CSS,
-    theme=gr.themes.Base(
-        primary_hue="yellow",
-        secondary_hue="gray",
-        neutral_hue="gray",
-        font=gr.themes.GoogleFont("Inter"),
-    ),
-) as demo:
-
-    gr.HTML("""
-    <div style="display:flex;align-items:center;justify-content:space-between;
-                padding:10px 24px;background:#181A20;border-bottom:2px solid #F0B90B;
-                font-family:'Inter',sans-serif;">
-      <div style="display:flex;align-items:center;gap:12px;">
-        <div style="background:#F0B90B;color:#181A20;font-weight:900;font-size:20px;
-                    padding:4px 11px;border-radius:4px;">₿</div>
-        <div>
-          <div style="color:#EAECEF;font-size:15px;font-weight:700;">CryptoML Terminal</div>
-          <div style="color:#5E6673;font-size:10px;">Machine Learning · Trading Signals</div>
-        </div>
-      </div>
-      <div style="display:flex;gap:18px;align-items:center;">
-        <span style="color:#848E9C;font-size:11px;">📊 ETH/USDT</span>
-        <span style="color:#848E9C;font-size:11px;">⏱ 4H</span>
-        <span style="color:#848E9C;font-size:11px;">🤖 RandomForest</span>
-        <div style="width:7px;height:7px;background:#0ECB81;border-radius:50%;"></div>
-        <span style="color:#0ECB81;font-size:10px;font-weight:600;">LIVE</span>
-      </div>
-    </div>""")
-
-    price_header = gr.HTML(
-        value=_html_price_header({"price": 0, "change_pct": 0, "high_24h": 0, "low_24h": 0, "volume": 0, "is_real": False})
+with gr.Blocks(title="CryptoML") as demo:
+
+    gr.Markdown("# 🚀 CryptoML ETH/USDT")
+
+    market_box = gr.Textbox(
+        label="Market",
+        value=get_market_info(),
+        lines=5
     )
 
-    gr.HTML(TRADINGVIEW_HTML)
-
-    with gr.Row(equal_height=True):
-        with gr.Column(scale=2):
-            gr.HTML("""
-            <div style="padding:10px 0;border-bottom:1px solid #2B3139;margin-bottom:10px;
-                        font-family:'Inter',sans-serif;">
-              <span style="color:#F0B90B;font-size:12px;font-weight:700;text-transform:uppercase;
-                           letter-spacing:1px;">🧠 Model Control</span>
-            </div>""")
-
-            train_btn = gr.Button("⚡ Train Model", variant="primary", size="lg")
-
-            with gr.Row():
-                predict_btn = gr.Button("🔮 Predict Signal", variant="secondary", size="lg")
-                refresh_btn = gr.Button("🔄 Refresh", variant="secondary", size="sm")
-
-            gr.HTML("""
-            <div style="margin-top:10px;padding:12px 14px;background:#2B3139;border-radius:6px;
-                        border:1px solid #363C45;font-family:'Inter',sans-serif;">
-              <div style="color:#5E6673;font-size:9px;text-transform:uppercase;margin-bottom:8px;
-                          letter-spacing:.5px;">Strategy Parameters</div>
-              <div style="display:flex;justify-content:space-between;margin-bottom:3px;">
-                <span style="color:#848E9C;font-size:10px;">Timeframe</span>
-                <span style="color:#EAECEF;font-size:10px;font-weight:500;">4 Hours</span></div>
-              <div style="display:flex;justify-content:space-between;margin-bottom:3px;">
-                <span style="color:#848E9C;font-size:10px;">Fee Rate</span>
-                <span style="color:#EAECEF;font-size:10px;font-weight:500;">0.15%</span></div>
-              <div style="display:flex;justify-content:space-between;margin-bottom:3px;">
-                <span style="color:#848E9C;font-size:10px;">Lookback Window</span>
-                <span style="color:#EAECEF;font-size:10px;font-weight:500;">20 bars</span></div>
-              <div style="display:flex;justify-content:space-between;margin-bottom:3px;">
-                <span style="color:#848E9C;font-size:10px;">Algorithm</span>
-                <span style="color:#EAECEF;font-size:10px;font-weight:500;">RF-100</span></div>
-              <div style="display:flex;justify-content:space-between;">
-                <span style="color:#848E9C;font-size:10px;">Indicators</span>
-                <span style="color:#EAECEF;font-size:10px;font-weight:500;">RSI·BB·MACD·EMA</span></div>
-            </div>""")
-
-        with gr.Column(scale=3):
-            signal_output = gr.HTML(
-                value=_html_warn("Klik <b>🔮 Predict Signal</b> untuk mendapatkan sinyal.")
-            )
+    with gr.Row():
+
+        train_btn = gr.Button(
+            "⚡ Train Model"
+        )
+
+        predict_btn = gr.Button(
+            "🔮 Predict"
+        )
 
-    train_output = gr.HTML(
-        value=_html_warn("Klik <b>⚡ Train Model</b> untuk melatih dan melihat hasil backtest.")
+        refresh_btn = gr.Button(
+            "🔄 Refresh"
+        )
+
+    train_output = gr.Textbox(
+        label="Training Result",
+        lines=15
+    )
+
+    predict_output = gr.Textbox(
+        label="Prediction",
+        lines=10
+    )
+
+    train_btn.click(
+        fn=run_train,
+        outputs=train_output
     )
 
-    gr.HTML("""
-    <div style="display:flex;justify-content:space-between;align-items:center;padding:10px 24px;
-                background:#181A20;border-top:1px solid #2B3139;margin-top:12px;
-                font-family:'Inter',sans-serif;">
-      <span style="color:#5E6673;font-size:9px;">
-        ⚠️ Disclaimer: Hanya untuk edukasi. Bukan saran investasi. Selalu DYOR.</span>
-      <span style="color:#5E6673;font-size:9px;">
-        Data: Yahoo Finance · Chart: TradingView · ML: scikit-learn</span>
-    </div>""")
+    predict_btn.click(
+        fn=run_predict,
+        outputs=predict_output
+    )
 
-    train_btn.click(fn=run_train, outputs=train_output)
-    predict_btn.click(fn=run_predict, outputs=signal_output)
-    refresh_btn.click(fn=refresh_price, outputs=price_header)
+    refresh_btn.click(
+        fn=get_market_info,
+        outputs=market_box
+    )
 
 demo.queue(default_concurrency_limit=1)
-logger.info("✅ App ready. Launching...")
 
-demo.launch(server_name="0.0.0.0", server_port=7860)
+logger.info("✅ App ready")
+
+demo.launch(
+    server_name="0.0.0.0",
+    server_port=7860
+)
+```