| import torch | |
| def validate_sequence(sequence): | |
| valid_amino_acids = set("ACDEFGHIKLMNPQRSTVWY") # 20 standard amino acids | |
| return all(aa in valid_amino_acids for aa in sequence) and len(sequence) <= 200 | |
| def load_model(): | |
| # Assuming the model is a simple PyTorch model, adjust the path as needed | |
| model = torch.load('model.pth', map_location=torch.device('cpu')) | |
| model.eval() | |
| return model | |
| def predict(model, sequence): | |
| # Dummy tensor conversion, replace with your actual model's input handling | |
| tensor = torch.tensor([ord(char) for char in sequence], dtype=torch.float32) | |
| output = model(tensor) | |
| return output.item() | |