Update app.py

app.py

@@ -7,11 +7,12 @@ from sklearn.ensemble import RandomForestClassifier
 from sklearn.preprocessing import MinMaxScaler
 import joblib
 import os
-import warnings
+import logging
+import sys
 
-warnings.filterwarnings('ignore')
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+logger = logging.getLogger("crypto_ml")
 
-# 🔧 Konfigurasi
 SYMBOL = "ETH/USDT"
 TIMEFRAME = "4h"
 FEE = 0.0015
@@ -20,13 +21,20 @@ MODEL_PATH = "rf_model.pkl"
 SCALER_PATH = "scaler.pkl"
 
 def fetch_data(symbol: str, timeframe: str, limit: int = 2000) -> pd.DataFrame:
-    exchange = ccxt.binance({"enableRateLimit": True})
+    logger.info("Fetching data from Binance...")
+    exchange = ccxt.binance({
+        "enableRateLimit": True,
+        "timeout": 30000,
+        "options": {"defaultType": "spot"}
+    })
     ohlcv = exchange.fetch_ohlcv(symbol, timeframe, limit=limit)
     df = pd.DataFrame(ohlcv, columns=["timestamp", "open", "high", "low", "close", "volume"])
     df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
+    logger.info(f"Fetched {len(df)} candles.")
     return df
 
 def prepare_dataset(df: pd.DataFrame):
+    logger.info("Preparing dataset & indicators...")
     df = df.copy()
     df.ta.rsi(append=True)
     df.ta.macd(append=True)
@@ -49,12 +57,13 @@ def prepare_dataset(df: pd.DataFrame):
     y = np.array(y_list)
     scaler = MinMaxScaler()
     X_scaled = scaler.fit_transform(X)
+    logger.info(f"Dataset ready: {X.shape[0]} samples, {X.shape[1]} features.")
     return X_scaled, y, idx_list, df, scaler
 
 def run_train():
     try:
         if os.path.exists(MODEL_PATH):
-            return {"message": "Model sudah ada. Hapus rf_model.pkl & scaler.pkl untuk retrain."}
+            return {"status": "⚠️ Info", "message": "Model sudah ada. Hapus file .pkl di Files tab untuk retrain."}
             
         df = fetch_data(SYMBOL, TIMEFRAME)
         X, y, idx, df_full, scaler = prepare_dataset(df)
@@ -63,7 +72,8 @@ def run_train():
         X_train, X_test = X[:split], X[split:]
         y_train, y_test = y[:split], y[split:]
         
-        model = RandomForestClassifier(n_estimators=500, max_depth=None, random_state=42)
+        logger.info("Training RandomForest...")
+        model = RandomForestClassifier(n_estimators=500, max_depth=None, random_state=42, n_jobs=-1)
         model.fit(X_train, y_train)
         acc = float(model.score(X_test, y_test))
         
@@ -89,20 +99,22 @@ def run_train():
         profit_factor = gross_profit / gross_loss if gross_loss > 0 else float("inf")
         
         return {
+            "status": "✅ Success",
             "accuracy_test": round(acc, 4),
             "profit_factor": round(profit_factor, 3),
             "net_profit_pct": round(sum(pnl_list) * 100, 3),
             "wins": wins, "losses": losses,
             "win_rate_pct": round(wins / len(pnl_list) * 100, 2) if pnl_list else 0,
-            "message": "Model trained & backtested successfully"
+            "message": "Model trained & backtested successfully."
         }
     except Exception as e:
-        return {"error": f"Training failed: {str(e)}"}
+        logger.error(f"Training failed: {e}")
+        return {"status": "❌ Error", "message": str(e)}
 
 def run_predict():
     try:
         if not os.path.exists(MODEL_PATH):
-            return {"error": "Model belum dilatih. Klik 'Train Model & Backtest' terlebih dahulu."}
+            return {"status": "❌ Error", "message": "Model belum dilatih. Klik 'Train' terlebih dahulu."}
             
         df = fetch_data(SYMBOL, TIMEFRAME, limit=WINDOW + 10)
         _, _, _, df_full, scaler = prepare_dataset(df)
@@ -113,17 +125,18 @@ def run_predict():
         pred = int(model.predict(latest_scaled)[0])
         
         return {
+            "status": "✅ Prediction",
             "symbol": SYMBOL, "timeframe": TIMEFRAME,
             "prediction": "BUY (Hold 4h)" if pred == 1 else "HOLD/SELL",
             "logic": "Prediksi >0.15% movement dalam 4 jam ke depan"
         }
     except Exception as e:
-        return {"error": f"Prediction failed: {str(e)}"}
+        logger.error(f"Prediction failed: {e}")
+        return {"status": "❌ Error", "message": str(e)}
 
-# 🎨 Gradio UI
 with gr.Blocks(title="Crypto ML Predictor") as demo:
     gr.Markdown("## 📈 Crypto ML Strategy (ETH/USDT 4h)")
-    gr.Markdown("Berdasarkan paper 2105.06827v3. Membutuhkan koneksi internet untuk fetch data Binance.")
+    gr.Markdown("Paper: 2105.06827v3 | Python 3.10 | Gradio Queue Enabled")
     with gr.Row():
         train_btn = gr.Button("🔄 Train Model & Backtest")
         train_out = gr.JSON(label="Hasil Training & Backtest")
@@ -133,7 +146,6 @@ with gr.Blocks(title="Crypto ML Predictor") as demo:
         
     train_btn.click(run_train, outputs=train_out)
     pred_btn.click(run_predict, outputs=pred_out)
-    gr.Markdown("⚠️ *File model bersifat temporary. Retrain otomatis jika Space restart.*")
+    gr.Markdown("💡 *Check 'App Logs' di tab Space untuk debugging real-time.*")
 
-# ⏳ Queue wajib agar training tidak timeout di browser
-demo.queue()
+demo.queue(default_concurrency_limit=1)