Update README.md

Author: hemangjoshi37a

README.md

@@ -39,21 +39,68 @@ pip install TrendMaster
 Here's how to integrate TrendMaster into your Python projects:
 
 ```python
-from trendmaster import TrendMaster
-
-# Initialize TrendMaster
-test_symbol = 'SBIN'
-tm = TrendMaster(symbol_name_stk=test_symbol)
-
-# Load data
-data = tm.load_data(symbol=test_symbol)
-
-# Train the model
-tm.train(test_symbol, transformer_params={'epochs': 1})
-
-# Perform inference
-predictions = tm.inferencer.predict_future(val_data=data, future_steps=100, symbol=test_symbol)
-print(predictions)
+# Example usage of merged_module.py
+
+from trendmaster import (
+    DataLoader,
+    TransAm,
+    Trainer,
+    Inferencer,
+    set_seed,
+    plot_results,
+    plot_predictions
+)
+
+import pyotp
+
+# Set seed for reproducibility
+set_seed(42)
+
+user_id = 'YOUR_ZERODHA_USER_ID'
+password = 'YOUR_ZERODHA_PASSWORD'  # Replace with your password
+totp_key = 'YOUR_ZERODHA_2FA_KEY'   # Replace with your TOTP secret key
+
+# Generate the TOTP code for two-factor authentication
+totp = pyotp.TOTP(totp_key)
+twofa = totp.now()
+
+# Initialize DataLoader and authenticate
+data_loader = DataLoader()
+kite = data_loader.authenticate(user_id=user_id, password=password, twofa=twofa)
+
+# Prepare data
+train_data, test_data = data_loader.prepare_data(
+    symbol='RELIANCE',
+    from_date='2023-01-01',
+    to_date='2023-02-27',
+    input_window=30,
+    output_window=10,
+    train_test_split=0.8
+)
+import torch
+# Initialize model, trainer, and train the model
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print(f'Training of {device} device.')
+model = TransAm(num_layers=2, dropout=0.2).to(device)
+
+trainer = Trainer(model, device, learning_rate=0.001)
+train_losses, val_losses = trainer.train(train_data, test_data, epochs=2, batch_size=64)
+
+# Save the trained model
+trainer.save_model('transam_model.pth')
+
+# Initialize inferencer and make predictions
+inferencer = Inferencer(model, device, data_loader)
+predictions = inferencer.predict(
+    symbol='RELIANCE',
+    from_date='2023-02-27',
+    to_date='2023-12-31',
+    input_window=30,
+    future_steps=10
+)
+
+# Evaluate the model
+test_loss = inferencer.evaluate(test_data, batch_size=32)
 ```
 
 ## 📊 Sample Results