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from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import sys
def frase_a_semilla(frase):
frase = frase.lower()
if "dom(kin)" in frase:
return "MKKK"
elif "mot(nls)" in frase:
return "MPRRR"
elif "mot(pest)" in frase:
return "MDGQL"
elif "tf(gata1)" in frase:
return "MKTFG"
elif "*ack" in frase or "*ac" in frase:
return "MKQAK"
elif "*p" in frase or "*phos" in frase:
return "MKRP"
elif "localize(nucleus)" in frase:
return "MPKRK"
elif "localize(membrane)" in frase:
return "MAIFL"
else:
return "M"
if __name__ == "__main__":
frase = sys.argv[1] if len(sys.argv) > 1 else "^p:Dom(Kin)'-Mot(NLS)*AcK@147=Localize(Nucleus)"
semilla = frase_a_semilla(frase)
print("🧪 Semilla generada desde la frase:", semilla)
tokenizer = AutoTokenizer.from_pretrained("nferruz/ProtGPT2", do_lower_case=False)
model = AutoModelForCausalLM.from_pretrained("nferruz/ProtGPT2")
inputs = tokenizer(semilla, return_tensors="pt", padding=True)
input_ids = inputs["input_ids"]
attention_mask = inputs.get("attention_mask", torch.ones_like(input_ids))
with torch.no_grad():
salida = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
max_length=100,
min_length=20,
do_sample=True,
top_k=50,
temperature=0.9,
pad_token_id=tokenizer.eos_token_id,
num_return_sequences=1
)
print("🧬 Proteína generada:")
print(tokenizer.decode(salida[0], skip_special_tokens=True)) |