Create train.py

src/train.py

@@ -0,0 +1,26 @@
+# scripts/train.py
+from src.preprocessing import load_and_preprocess_data
+from src.feature_engineering import tokenize_texts
+from src.model import train_model, evaluate_model
+from src.utils import plot_confusion_matrix
+
+def main():
+    # Load and preprocess data
+    train_df, test_df = load_and_preprocess_data(sample=True)
+    
+    # Tokenize
+    train_encodings = tokenize_texts(train_df["text"])
+    test_encodings = tokenize_texts(test_df["text"])
+    
+    # Train model
+    model, label_map = train_model(
+        train_encodings, train_df["category"], test_encodings, test_df["category"]
+    )
+    
+    # Evaluate
+    report, cm = evaluate_model(model, test_encodings, test_df["category"])
+    print("Classification Report:\n", report)
+    plot_confusion_matrix(cm, list(label_map.keys()))
+
+if __name__ == "__main__":
+    main()