Update utils/model_inference.py

utils/model_inference.py

@@ -1,20 +1,26 @@
-import tensorflow as tf
-import joblib
-import pandas as pd
+from config import RISK_LEVELS
+import random
 
-def load_lstm_model(path="models/lstm_forex_model.h5"):
-    return tf.keras.models.load_model(path)
+def generate_forex_signals(capital, risk_level):
+    """
+    Generate signals based on trading capital and risk level.
+    """
+    # Fetch stop loss and take profit percentages based on risk level
+    risk_params = RISK_LEVELS[risk_level]
+    stop_loss = risk_params["stop_loss"]
+    take_profit = risk_params["take_profit"]
 
-def load_sac_model(path="models/reconstructed_sac_forex_trading_model.zip"):
-    return joblib.load(path)
+    # Simulate ROI based on risk level and trading capital
+    roi = random.uniform(take_profit * 80, take_profit * 120)  # Randomized ROI in range
+    signal_strength = "High" if roi > (take_profit * 100) else "Medium"
+
+    # Entry and exit logic
+    entry_time = "Generated dynamically in app.py"  # Placeholder
 
-def generate_forex_signals(capital, risk_level):
-    # Logic to combine LSTM and SAC predictions
-    # Return currency pair, entry/exit times, ROI, signal strength
     return {
-        "currency_pair": "EUR/USD",
-        "entry_time": "10:15 AM",
-        "exit_time": "2:45 PM",
-        "roi": 15.6,
-        "signal_strength": "High"
+        "currency_pair": random.choice(["EUR/USD", "GBP/USD", "USD/JPY", "AUD/USD", "USD/CHF"]),
+        "entry_time": entry_time,
+        "exit_time": "Entry time + 2 hours",
+        "roi": round(roi, 2),
+        "signal_strength": signal_strength
     }