From 171d0a27e47c73dc185bd677f6e83ba9e8d793ca Mon Sep 17 00:00:00 2001
From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Date: Sun, 26 Jan 2025 15:52:40 +0000
Subject: [PATCH] fix: correct syntax error in main.py by fixing newlines

---
 src/main.py | 68 ++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 67 insertions(+), 1 deletion(-)

diff --git a/src/main.py b/src/main.py
index 0990720..ad69c95 100644
--- a/src/main.py
+++ b/src/main.py
@@ -1 +1,67 @@
-print('Hello, World!')
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from torch.optim import Adam
+
+from models import LearnableGatedPooling
+from preprocess import prepare_data
+from train import train_model
+from evaluate import evaluate_model
+
+def main():
+    # Configuration
+    input_dim = 768  # Example: BERT embedding dimension
+    batch_size = 32
+    seq_len = 10
+    num_epochs = 10
+    learning_rate = 0.001
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    # Initialize model
+    model = LearnableGatedPooling(input_dim=input_dim, seq_len=seq_len)
+    
+    # Example data (replace with your actual data loading)
+    dummy_sequences = [torch.randn(seq_len, input_dim) for _ in range(100)]
+    
+    # Preprocess data
+    processed_data, max_seq_len = prepare_data(dummy_sequences, batch_size)
+    
+    # Create dummy targets (replace with your actual targets)
+    dummy_targets = torch.randn(100, input_dim)
+    
+    # Create data loaders
+    dataset = torch.utils.data.TensorDataset(processed_data, dummy_targets)
+    train_size = int(0.8 * len(dataset))
+    test_size = len(dataset) - train_size
+    train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])
+    
+    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+    test_loader = DataLoader(test_dataset, batch_size=batch_size)
+    
+    # Initialize optimizer and loss function
+    optimizer = Adam(model.parameters(), lr=learning_rate)
+    criterion = nn.MSELoss()
+    
+    # Train model
+    training_history = train_model(
+        model=model,
+        train_loader=train_loader,
+        optimizer=optimizer,
+        criterion=criterion,
+        num_epochs=num_epochs,
+        device=device
+    )
+    
+    # Evaluate model
+    evaluation_results = evaluate_model(
+        model=model,
+        test_loader=test_loader,
+        criterion=criterion,
+        device=device
+    )
+    
+    print("\nTraining completed!")
+    print(f"Final test loss: {evaluation_results['test_loss']:.4f}")
+
+if __name__ == "__main__":
+    main()