| import torch | |
| import json | |
| from typing import Any, List | |
| class PyTorchModel: | |
| def __init__(self): | |
| # Load model info | |
| with open("model_info.json", "r") as f: | |
| self.model_info = json.load(f) | |
| # Note: You'll need to reconstruct the model architecture | |
| # and load the state dict | |
| # self.model = YourModelClass() # Define your model class | |
| # self.model.load_state_dict(torch.load("pytorch_model.bin")) | |
| # self.model.eval() | |
| def predict(self, inputs: List[Any]): | |
| """Make predictions""" | |
| # Implement your prediction logic | |
| # with torch.no_grad(): | |
| # outputs = self.model(processed_inputs) | |
| # return outputs | |
| pass | |