# ==============================================================================
# File: handler.py
# Description: Core module for SAIR Modular Arithmetic Challenge.
# Tech Stack: PyTorch 2.0+, Python 3.10+
# Author: Amey Thakur
# Profile: https://github.com/Amey-Thakur
# Repository: https://github.com/Amey-Thakur/SAIR-MODULAR-ARITHMETIC-CHALLENGE
# License: CC-BY-4.0
# Date: 2026-07-15
# ==============================================================================

import torch
import json
import os
import sys

# Note: In a real HF deployment, this file sits at the root of the HF repository
# and the models/tokenizers would be bundled next to it.

class EndpointHandler:
    def __init__(self, path=""):
        # self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        # self.tokenizer = Base10Tokenizer()
        # config = TransformerConfig(...)
        # self.model = TransformerRoPE(config)
        # self.model.load_state_dict(load_file(os.path.join(path, "model.safetensors")))
        # self.model.to(self.device)
        # self.model.eval()
        pass
        
    def __call__(self, data: dict):
        """
        Receives a dictionary with `inputs` (the equation string).
        Returns the predicted modulo output.
        """
        inputs = data.pop("inputs", None)
        if not inputs:
            return {"error": "No inputs provided. Pass an equation like '123*456'."}
            
        # 1. Encode
        # idx = self.tokenizer.encode(inputs)
        # 2. Generate
        # generated = generate(self.model, idx)
        # 3. Decode & Parse Scratchpad
        # answer = extract_answer(generated)
        
        # Mocking output for structural completeness
        answer = "0"
        
        return [{"generated_text": answer}]