generate.py

# generate.py
import torch
from transformers import BertTokenizer
from evo_model import EvoDecoderModel

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load tokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

# Initialize model architecture
vocab_size = tokenizer.vocab_size
model = EvoDecoderModel(vocab_size=vocab_size)
model.load_state_dict(torch.load("evo_decoder_model.pt", map_location=device))
model.to(device)
model.eval()

def generate_response(prompt, max_length=128, use_web=False):
    with torch.no_grad():
        input_ids = tokenizer(prompt, return_tensors="pt", padding=False, truncation=True).input_ids.to(device)
        input_ids = input_ids[:, :128]  # ✅ clip to trained length

        logits = model(input_ids)
        next_token_logits = logits[:, -1, :]  # take last token's logits
        predicted_id = torch.argmax(next_token_logits, dim=-1)

        output_ids = torch.cat([input_ids, predicted_id.unsqueeze(0)], dim=1)
        decoded = tokenizer.decode(output_ids[0], skip_special_tokens=True)

        return decoded