app.py

import torch
import tensorflow as tf
import gradio as gr
import re

from Seq2SeqModel import Seq2SeqModel 
from DataPPwithspecial import preprocess 
from Decoder import Decoder
from Encoder import Encoder
# Model parameters
n_layers = 2
emb_dim = 256
hid_dim = 512
dropout = 0.5
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # Use GPU if available, otherwise use CPU

# Load preprocessed data and model parameters
(english_tokenizer, klingon_tokenizer, max_english_length,
    _, _, _, _, _, _) = preprocess()  # We don't need training data for inference
input_dim = len(english_tokenizer.word_index) + 1  # Add 1 for the padding token
output_dim = len(klingon_tokenizer.word_index) + 1  # Add 1 for the padding token

# Initialize encoder and decoder
encoder = Encoder(input_dim, emb_dim, hid_dim, n_layers, dropout).to(device)
decoder = Decoder(output_dim, emb_dim, hid_dim, n_layers, dropout).to(device)

# Initialize the Seq2SeqModel
model = Seq2SeqModel(encoder, decoder, device).to(device)

# Load the saved model
model.load_state_dict(torch.load('English_to_Klingon.pth'))
model.eval()  # Set the model to evaluation mode

#tokenize the English input
def preprocess_sentence(sentence, tokenizer, max_length):
    # Tokenize the sentence
    tokenized_sentence = tokenizer.texts_to_sequences([sentence])
    # Pad the sequence
    padded_sentence = tf.keras.preprocessing.sequence.pad_sequences(tokenized_sentence, maxlen=max_length, padding='post')
    return torch.tensor(padded_sentence, dtype=torch.long).to(device)

# Translation function for Gradio
def translate_english_to_klingon(english_sentence):
    # Preprocess the input English sentence
    input_sentence = preprocess_sentence(english_sentence, english_tokenizer, max_english_length)

    # Remove the extra dimension added by unsqueeze(1)
    input_sentence = input_sentence.squeeze(0)

    # Perform inference
    with torch.no_grad():
        # Pass input as both input and target with teacher forcing ratio 0
        output = model(input_sentence.unsqueeze(1), input_sentence.unsqueeze(1), 0)

    # Convert output indices to Klingon words
    output_indices = torch.argmax(output, dim=-1).squeeze().tolist()
    klingon_sentence = ' '.join([klingon_tokenizer.index_word[idx] for idx in output_indices if idx != 0])  # Remove padding token
    #regex to remove eos
    klingon_sentence = re.sub(r'\beos\b', '', klingon_sentence).strip()
    return klingon_sentence


# Create Gradio interface
examples = [
    ["Hello, how are you?"],
    ["What is your name?"],
    ["I love learning new languages."],
    ["Where is the nearest starbase?"],
    ["Can you tell me more about your planet?"]
]

iface = gr.Interface(
    fn=translate_english_to_klingon,
    inputs=gr.Textbox(label = "English Phrase",lines=2, placeholder="Enter English text here..."),
    outputs=gr.Textbox(label="Klingon Translation",lines=2),
    title="English to Klingon Translation",
    description="Enter text in English and get its translation in Klingon. This translator helps you convert everyday English phrases into the fictional language spoken by the Klingon species in the Star Trek universe. Try one of the example sentences to see how it works!",
    examples=examples,
    theme="default"
)

iface.launch(share = True)