from transformers import AutoTokenizer
import torch
import torch.nn.functional as F

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(model_name):
    # Load the model based on the provided name
    model = torch.load(f'{model_name}_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)
    probabilities = F.softmax(output.logits, dim=-1)
    predicted_label = torch.argmax(probabilities, dim=-1)
    confidence = probabilities.max().item() * 0.85
    return predicted_label.item(), confidence