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app.py

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+import torch
+import numpy as np
+import pandas as pd
+import gradio as gr
+import joblib
+from model.ConvGRUTransformerHL import ConvGRUTransformerHL_Attn  # โมเดลของมึง
+
+# ===== Config =====
+LOOKBACK = 3
+FOLDS = [1,2,3]
+MODEL_DIR = "models"  # ปรับ path ตามจริง
+
+# ===== Load scalers =====
+scalers = {}
+for fold in FOLDS:
+    scalers[fold] = {
+        "price": joblib.load(f"{MODEL_DIR}/scaler_fold{fold}/scaler_price.pkl"),
+        "adx": joblib.load(f"{MODEL_DIR}/scaler_fold{fold}/scaler_adx.pkl"),
+        "atr": joblib.load(f"{MODEL_DIR}/scaler_fold{fold}/scaler_atr.pkl"),
+        "tick": joblib.load(f"{MODEL_DIR}/scaler_fold{fold}/scaler_tick_volume.pkl"),
+        "target_high": joblib.load(f"{MODEL_DIR}/scaler_fold{fold}/scaler_target_high.pkl"),
+        "target_low": joblib.load(f"{MODEL_DIR}/scaler_fold{fold}/scaler_target_low.pkl"),
+    }
+
+# ===== Load models =====
+models = {}
+for fold in FOLDS:
+    checkpoint = torch.load(f"{MODEL_DIR}/EURUSDm_transformer_finetuned_fold_{fold}_01.pth", map_location="cpu")
+    model = ConvGRUTransformerHL_Attn(input_dim=10, seq_len=LOOKBACK, kernel_size=LOOKBACK, output_steps=1)
+    model.load_state_dict(checkpoint["model_state_dict"])
+    model.eval()
+    models[fold] = model
+
+# ===== Prediction function =====
+def predict_signal(open_, high_, low_, close_, tick_volume, adx, atr, hour, weekday, month):
+    df = pd.DataFrame([{
+        'open': open_, 'high': high_, 'low': low_, 'close': close_,
+        'tick_volume': tick_volume, 'adx': adx, 'atr': atr,
+        'hour': hour, 'weekday': weekday, 'month': month
+    }])
+
+    # เตรียม X_scaled
+    pred_high_list = []
+    pred_low_list = []
+
+    for fold in FOLDS:
+        s = scalers[fold]
+        price_scaled = s["price"].transform(df[['open','high','low','close']].values)
+        adx_scaled   = s["adx"].transform(df[['adx']].values)
+        atr_scaled   = s["atr"].transform(df[['atr']].values)
+        tick_scaled  = s["tick"].transform(df[['tick_volume']].values)
+        time_data    = df[['hour','weekday','month']].values
+
+        X_scaled = np.concatenate([price_scaled, adx_scaled, atr_scaled, tick_scaled, time_data], axis=1)
+        X_tensor = torch.tensor(X_scaled, dtype=torch.float32).unsqueeze(0)  # (1, seq_len, features)
+
+        # Predict
+        with torch.no_grad():
+            pred_high, pred_low = models[fold](X_tensor)
+        
+        # Inverse transform
+        pred_high_np = s["target_high"].inverse_transform(pred_high.numpy())
+        pred_low_np  = s["target_low"].inverse_transform(pred_low.numpy())
+
+        pred_high_list.append(pred_high_np[0][0])
+        pred_low_list.append(pred_low_np[0][0])
+
+    # เอาค่าเฉลี่ย folds
+    return float(np.mean(pred_high_list)), float(np.mean(pred_low_list))
+
+# ===== Gradio UI =====
+iface = gr.Interface(
+    fn=predict_signal,
+    inputs=["number","number","number","number","number","number","number","number","number","number"],  # 10 features
+    outputs=["number","number"],
+    title="AI Trading Signals",
+    description="Enter latest OHLC + tick + adx + atr + hour + weekday + month to get predicted High/Low"
+)
+
+iface.launch()

requirements.txt

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+os
+joblib
+torch
+sys
+numpy
+pandas
+MetaTrader5
+tqdm