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from transformers import AutoTokenizer
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():
# Load your model as before
model = torch.load('solubility_model.pth', map_location=torch.device('cpu'))
model.eval()
return model
def predict(model, sequence):
tokenizer = AutoTokenizer.from_pretrained('facebook/esm2_t6_8M_UR50D')
tokenized_input = tokenizer(sequence, return_tensors="pt", truncation=True, padding=True)
output = model(**tokenized_input)
return output.item() |