"""
ML Classifier Training Script (TO BE IMPLEMENTED)
This would train a RoBERTa model on GSM8K dataset
"""
import torch
from transformers import RobertaTokenizer, RobertaForSequenceClassification
from torch.utils.data import DataLoader
import json

# TODO: Implement this for full research version
class MathErrorClassifier:
    def __init__(self):
        self.model_name = "roberta-base"
        self.tokenizer = RobertaTokenizer.from_pretrained(self.model_name)
        self.model = RobertaForSequenceClassification.from_pretrained(
            self.model_name,
            num_labels=2  # VALID or ERROR
        )
    
    def prepare_gsm8k_data(self):
        """
        Download and prepare GSM8K dataset
        https://github.com/openai/grade-school-math
        """
        # TODO: Implementation
        pass
    
    def train(self, train_data, epochs=3):
        """
        Train the classifier
        """
        # TODO: Implementation
        pass
    
    def evaluate(self, test_data):
        """
        Evaluate on test set
        """
        # TODO: Implementation
        pass
    
    def save_model(self, path):
        """
        Save trained model
        """
        self.model.save_pretrained(path)
        self.tokenizer.save_pretrained(path)

if __name__ == "__main__":
    print("⚠️  ML Training Script - Not Yet Implemented")
    print("This would require:")
    print("1. GSM8K dataset download")
    print("2. GPU for training")
    print("3. 1-2 weeks training time")
    print("\nCurrent system uses simulation for demo purposes.")