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# generate.py — Generates EvoDecoder responses with optional live web context
import torch
from transformers import AutoTokenizer
from evo_model import EvoDecoderModel
from search_utils import web_search
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
vocab_size = tokenizer.vocab_size
model = EvoDecoderModel(vocab_size=vocab_size).to(device)
model.load_state_dict(torch.load("evo_decoder_model.pt", map_location=device))
model.eval()
def generate_response(prompt, use_web=False, max_length=100, top_k=40):
if use_web:
context = web_search(prompt)
prompt = f"Relevant Info: {context}\nUser: {prompt}\nAssistant:"
else:
prompt = f"User: {prompt}\nAssistant:"
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
for _ in range(max_length):
with torch.no_grad():
logits = model(input_ids)
next_token_logits = logits[:, -1, :]
top_k_probs, top_k_indices = torch.topk(next_token_logits, top_k)
probs = torch.softmax(top_k_probs, dim=-1)
next_token = top_k_indices[0, torch.multinomial(probs, 1)]
next_token = next_token.unsqueeze(0).unsqueeze(0) # (1,1)
input_ids = torch.cat([input_ids, next_token], dim=1)
if next_token.item() == tokenizer.eos_token_id:
break
output = tokenizer.decode(input_ids[0], skip_special_tokens=True)
return output.split("Assistant:")[-1].strip()
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