Update app.py

app.py

@@ -9,9 +9,12 @@ from sklearn.preprocessing import MinMaxScaler
 import joblib
 import os
 import warnings
+import uvicorn
+
 warnings.filterwarnings('ignore')
 
-app = FastAPI(title="Crypto ML Strategy (Paper 2105.06827v3)")
+# 🔹 1. FastAPI App
+fastapi_app = FastAPI(title="Crypto ML Strategy (Paper 2105.06827v3)")
 
 SYMBOL = "ETH/USDT"
 TIMEFRAME = "4h"
@@ -29,16 +32,11 @@ def fetch_data(symbol: str, timeframe: str, limit: int = 2000) -> pd.DataFrame:
 
 def prepare_dataset(df: pd.DataFrame):
     df = df.copy()
-    # Nonaktifkan warning pandas-ta vs pandas 2.x
-    import warnings
-    warnings.filterwarnings("ignore", category=FutureWarning)
-    
     df.ta.rsi(append=True)
     df.ta.macd(append=True)
     df.ta.bbands(append=True)
     df.ta.cci(append=True)
     df.dropna(inplace=True)
-    
     df["next_close"] = df["close"].shift(-1)
     df["label"] = (df["next_close"] > df["close"] * (1 + FEE)).astype(int)
     df.dropna(inplace=True)
@@ -56,7 +54,7 @@ def prepare_dataset(df: pd.DataFrame):
     X_scaled = scaler.fit_transform(X)
     return X_scaled, y, idx_list, df, scaler
 
-@app.get("/api/train")
+@fastapi_app.get("/api/train")
 def train_and_backtest():
     try:
         if os.path.exists(MODEL_PATH):
@@ -105,7 +103,7 @@ def train_and_backtest():
     except Exception as e:
         raise HTTPException(500, f"Training failed: {str(e)}")
 
-@app.get("/api/predict")
+@fastapi_app.get("/api/predict")
 def predict_next_move():
     if not os.path.exists(MODEL_PATH):
         raise HTTPException(400, "Model belum dilatih. Panggil /api/train terlebih dahulu.")
@@ -124,7 +122,7 @@ def predict_next_move():
         "logic": "Prediksi >0.15% movement dalam 4 jam ke depan"
     }
 
-# 🎨 Gradio UI
+# 🔹 2. Gradio UI
 def run_train():
     return train_and_backtest()
 
@@ -140,9 +138,14 @@ with gr.Blocks(title="Crypto ML Predictor") as demo:
     with gr.Row():
         pred_btn = gr.Button("🔮 Prediksi Pergerakan Berikutnya")
         pred_out = gr.JSON(label="Sinyal Prediksi")
+        
     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.*")
 
-# ⚙️ Mount Gradio ke FastAPI
-app = gr.mount_gradio_app(app, demo, path="/")
+# 🔹 3. Mount & Run Server
+app = gr.mount_gradio_app(fastapi_app, demo, path="/")
+
+# ✅ CRITICAL: HF Spaces butuh server yang blocking di port 7860
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)