Update app.py

app.py

@@ -1,105 +1,105 @@
-# app.py
-from fastapi import FastAPI
-from pydantic import BaseModel
-import numpy as np
-import pandas as pd
-import joblib
-import json
-import tensorflow as tf
-from tensorflow.keras.models import load_model
-from sklearn.preprocessing import MinMaxScaler
-
-app = FastAPI(
-    title="Forex LSTM Prediction API",
-    description="API untuk prediksi harga EUR/USD H+1 menggunakan model LSTM terbaik",
-    version="1.0"
-)
-
-# ==========================================================
-# LOAD MODEL, PARAMETER, DAN SCALER
-# ==========================================================
-MODEL_PATH = "lstm_model.h5"
-SCALER_PATH = "scaler.pkl"
-PARAMS_PATH = "best_params.json"
-
-print("📥 Loading LSTM model...")
-model = load_model(MODEL_PATH)
-
-print("📥 Loading scaler...")
-scaler = joblib.load(SCALER_PATH)
-
-print("📥 Loading best parameters...")
-with open(PARAMS_PATH, "r") as f:
-    best_params = json.load(f)
-
-LOOKBACK = best_params.get("lookback", 7)  # default 7 jika tidak ada
-FEATURE_ORDER = best_params.get("features", [
-    "mood_score", "t_pos", "t_neg", "c_pos", "c_neg",
-    "norm_ema20", "norm_ema50", "norm_close"
-])
-
-print(f"✅ Model loaded. Lookback={LOOKBACK}, Features={FEATURE_ORDER}")
-
-# ==========================================================
-# INPUT SCHEMA
-# ==========================================================
-class LSTMInput(BaseModel):
-    data: list  # list of daily records (latest LOOKBACK hari)
-    # contoh format:
-    # [
-    #   {"date": "2025-10-23", "mood_score": 0.5, "t_pos": 0.3, "t_neg": 0.2, ...},
-    #   ...
-    # ]
-
-# ==========================================================
-# HELPER FUNCTION
-# ==========================================================
-def prepare_input(data):
-    """Convert input list to numpy array sesuai urutan fitur dan lookback"""
-    df = pd.DataFrame(data)
-    missing_cols = [f for f in FEATURE_ORDER if f not in df.columns]
-    if missing_cols:
-        raise ValueError(f"Missing columns: {missing_cols}")
-
-    X = df[FEATURE_ORDER].values[-LOOKBACK:]
-    if X.shape[0] < LOOKBACK:
-        raise ValueError(f"Need at least {LOOKBACK} timesteps, got {X.shape[0]}")
-
-    X = np.expand_dims(X, axis=0)
-    return X, df
-
-# ==========================================================
-# ENDPOINT
-# ==========================================================
-@app.post("/predict")
-def predict_price(input_data: LSTMInput):
-    try:
-        X, df = prepare_input(input_data.data)
-        pred_norm = model.predict(X)[0][0]
-        pred_close = scaler.inverse_transform([[pred_norm]])[0][0]
-
-        last_date = pd.to_datetime(df["date"].iloc[-1])
-        next_date = (last_date + pd.Timedelta(days=1)).strftime("%Y-%m-%d")
-
-        response = {
-            "next_date": next_date,
-            "predicted_norm_close": float(pred_norm),
-            "predicted_close": float(pred_close),
-            "model_info": {
-                "lookback": LOOKBACK,
-                "features_used": FEATURE_ORDER,
-                "source_model": MODEL_PATH
-            }
-        }
-
-        return {"status": "ok", "result": response}
-
-    except Exception as e:
-        return {"status": "error", "message": str(e)}
-
-# ==========================================================
-# ROOT TEST
-# ==========================================================
-@app.get("/")
-def root():
-    return {"message": "Forex LSTM Prediction API is active!"}
+# app.py
+from fastapi import FastAPI
+from pydantic import BaseModel
+import numpy as np
+import pandas as pd
+import joblib
+import json
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from sklearn.preprocessing import MinMaxScaler
+
+app = FastAPI(
+    title="Forex LSTM Prediction API",
+    description="API untuk prediksi harga EUR/USD H+1 menggunakan model LSTM terbaik",
+    version="1.0"
+)
+
+# ==========================================================
+# LOAD MODEL, PARAMETER, DAN SCALER
+# ==========================================================
+MODEL_PATH = "lstm_model.h5"
+SCALER_PATH = "scaler.pkl"
+PARAMS_PATH = "best_params.json"
+
+print("📥 Loading LSTM model...")
+model = load_model(MODEL_PATH, compile = False)
+
+print("📥 Loading scaler...")
+scaler = joblib.load(SCALER_PATH)
+
+print("📥 Loading best parameters...")
+with open(PARAMS_PATH, "r") as f:
+    best_params = json.load(f)
+
+LOOKBACK = best_params.get("lookback", 7)  # default 7 jika tidak ada
+FEATURE_ORDER = best_params.get("features", [
+    "mood_score", "t_pos", "t_neg", "c_pos", "c_neg",
+    "norm_ema20", "norm_ema50", "norm_close"
+])
+
+print(f"✅ Model loaded. Lookback={LOOKBACK}, Features={FEATURE_ORDER}")
+
+# ==========================================================
+# INPUT SCHEMA
+# ==========================================================
+class LSTMInput(BaseModel):
+    data: list  # list of daily records (latest LOOKBACK hari)
+    # contoh format:
+    # [
+    #   {"date": "2025-10-23", "mood_score": 0.5, "t_pos": 0.3, "t_neg": 0.2, ...},
+    #   ...
+    # ]
+
+# ==========================================================
+# HELPER FUNCTION
+# ==========================================================
+def prepare_input(data):
+    """Convert input list to numpy array sesuai urutan fitur dan lookback"""
+    df = pd.DataFrame(data)
+    missing_cols = [f for f in FEATURE_ORDER if f not in df.columns]
+    if missing_cols:
+        raise ValueError(f"Missing columns: {missing_cols}")
+
+    X = df[FEATURE_ORDER].values[-LOOKBACK:]
+    if X.shape[0] < LOOKBACK:
+        raise ValueError(f"Need at least {LOOKBACK} timesteps, got {X.shape[0]}")
+
+    X = np.expand_dims(X, axis=0)
+    return X, df
+
+# ==========================================================
+# ENDPOINT
+# ==========================================================
+@app.post("/predict")
+def predict_price(input_data: LSTMInput):
+    try:
+        X, df = prepare_input(input_data.data)
+        pred_norm = model.predict(X)[0][0]
+        pred_close = scaler.inverse_transform([[pred_norm]])[0][0]
+
+        last_date = pd.to_datetime(df["date"].iloc[-1])
+        next_date = (last_date + pd.Timedelta(days=1)).strftime("%Y-%m-%d")
+
+        response = {
+            "next_date": next_date,
+            "predicted_norm_close": float(pred_norm),
+            "predicted_close": float(pred_close),
+            "model_info": {
+                "lookback": LOOKBACK,
+                "features_used": FEATURE_ORDER,
+                "source_model": MODEL_PATH
+            }
+        }
+
+        return {"status": "ok", "result": response}
+
+    except Exception as e:
+        return {"status": "error", "message": str(e)}
+
+# ==========================================================
+# ROOT TEST
+# ==========================================================
+@app.get("/")
+def root():
+    return {"message": "Forex LSTM Prediction API is active!"}