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app.py

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+# 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!"}

best_model_lstm.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3ed1efb761ce2aac4c09ff5e8beaf55e86d76c299b04477e59ad6a099dd53a7d
+size 247104

best_params.json

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+{
+    "lstm_units_1": 64,
+    "dropout_1": 0.1,
+    "lstm_units_2": 64,
+    "dropout_2": 0.3,
+    "dense_units": 16,
+    "optimizer": "adam"
+}

requirments.txt

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+fastapi
+uvicorn
+tensorflow
+numpy
+pandas
+joblib
+scikit-learn

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ab8a87f6714de029e030c4a283abb1736134e4c0dfda779d1afa6792abd10eb7
+size 630