Notes

import torch
def generate_response(input_text):
# Sample input text
#input_text = "How can I prevent fires at home ?"
# Tokenization and adding special tokens using your vocabulary
input_tokens = [START_TOKEN] + input_text.lower().split() + [END_TOKEN]
# Converting tokens to indices using your vocabulary
input_indices = [combined_to_index[token] for token in input_tokens]
# Padding and reshaping
max_input_length = max_sequence_length # Choose an appropriate length
padded_input = input_indices + [combined_to_index[PADDING_TOKEN]] * (max_input_length - len(input_indices))
input_tensor = torch.tensor(padded_input).unsqueeze(0) # Add a batch dimension
# Move the tensor to the appropriate device (GPU if available)
input_tensor = input_tensor.to(device)

# Pass through the model
with torch.no_grad():
model.eval() # Set the model to evaluation mode
output = model(input_tensor, input_tensor) # Pass input_tensor as both src and tgt
predicted_indices = output.argmax(dim=-1) # Get the indices with the highest probability

# Convert predicted indices back to tokens using your vocabulary
predicted_tokens = [index_to_combined[idx.item()] for idx in predicted_indices[0]]
# Remove special tokens and padding tokens
predicted_tokens = [token for token in predicted_tokens if token not in [START_TOKEN, END_TOKEN, PADDING_TOKEN]]
# Convert predicted tokens to a readable sentence
predicted_sentence = " ".join(predicted_tokens)
print("Predicted sentence:", predicted_sentence)