import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Using device: {device}\n")

print("Loading RL-optimized PlasmidGPT-GRPO model...")
model = AutoModelForCausalLM.from_pretrained(
    ".",
    trust_remote_code=True
).to(device)
model.eval()

tokenizer = AutoTokenizer.from_pretrained(
    ".",
    trust_remote_code=True
)

print("Generating optimized plasmid sequences...\n")

start_sequence = 'ATGGCTAGCGAATTCGGCGCGCCT'
print(f"Start sequence: {start_sequence}\n")

input_ids = tokenizer.encode(start_sequence, return_tensors='pt').to(device)

outputs = model.generate(
    input_ids,
    max_length=400,
    num_return_sequences=3,
    temperature=0.8,
    do_sample=True,
    top_k=50,
    top_p=0.95,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id
)

print("=" * 80)
for i, output in enumerate(outputs, 1):
    sequence = tokenizer.decode(output, skip_special_tokens=True)
    print(f"\nPlasmid {i}:")
    print(f"  Length: {len(sequence)} bp")
    print(f"  First 100 bp: {sequence[:100]}")
    print(f"  Last 100 bp: {sequence[-100:]}")
print("\n" + "=" * 80)

print("\nNote: These sequences are generated by an RL-optimized model trained to:")
print("  ✓ Include proper genetic elements (ori, promoters, CDS, markers)")
print("  ✓ Avoid repeat regions > 50 bp")
print("  ✓ Generate compact, functional plasmids")
print("  ✓ Organize genes in proper cassettes (promoter → CDS → terminator)")