temp.py

import json
import torch
from main_model import Seq2Seq , generate_answer


with open("./config.json", "r") as f:
    config = json.load(f)

vocab_size = config["vocab_size"]
embedding_dim = config["embedding_dim"]
hidden_dim = config["hidden_dim"]
max_len = config["max_len"]

# Initialize Model
model = Seq2Seq(vocab_size, embedding_dim, hidden_dim)
model.load_state_dict(torch.load("./seq2seq_model.pth",weights_only=True))
model.eval()  # Set model to evaluation mode

with open("./ma_vocab.json", "r") as f:
    vocab = json.load(f)

# Create mappings
word2idx = vocab
idx2word = {idx: word for word, idx in vocab.items()}


question = "what is MA?"
answer = generate_answer(model, question, vocab=word2idx)
print("Answer:", answer)