diff --git a/Seq2seq/seq2seq.py b/Seq2seq/seq2seq.py
index eade343..9208a35 100644
--- a/Seq2seq/seq2seq.py
+++ b/Seq2seq/seq2seq.py
@@ -1,263 +1,178 @@
-import torch
-import torch.nn as nn
-import torch.optim as optim
-import random
-import re
-import unicodedata
-from torchtext.data.metrics import bleu_score
+import numpy as np
+import keras
+from nltk.translate.bleu_score import corpus_bleu
 
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+# Configuration
+batch_size = 64
+epochs = 350
+latent_dim = 256
+num_samples = 50000
+data_path = "fra.txt"
 
-# Data preparation
-def unicode_to_ascii(s):
-    return ''.join(c for c in unicodedata.normalize('NFD', s)
-                   if unicodedata.category(c) != 'Mn')
+# Preparing data
+input_texts = []
+target_texts = []
+input_characters = set()
+target_characters = set()
+with open(data_path, "r", encoding="utf-8") as f:
+    lines = f.read().split("\n")
+for line in lines[: min(num_samples, len(lines) - 1)]:
+    input_text, target_text, _ = line.split("\t")
+    target_text = "\t" + target_text + "\n"
+    input_texts.append(input_text)
+    target_texts.append(target_text)
+    for char in input_text:
+        if char not in input_characters:
+            input_characters.add(char)
+    for char in target_text:
+        if char not in target_characters:
+            target_characters.add(char)
 
-def preprocess_sentence(w):
-    w = unicode_to_ascii(w.lower().strip())
-    w = re.sub(r"([?.!,¿])", r" \1 ", w)
-    w = re.sub(r'[" "]+', " ", w)
-    w = re.sub(r"[^a-zA-Z?.!,¿]+", " ", w)
-    w = w.strip()
-    return w
+input_characters = sorted(list(input_characters))
+target_characters = sorted(list(target_characters))
+num_encoder_tokens = len(input_characters)
+num_decoder_tokens = len(target_characters)
+max_encoder_seq_length = max([len(txt) for txt in input_texts])
+max_decoder_seq_length = max([len(txt) for txt in target_texts])
 
-def read_langs(lang1, lang2, path):
-    lines = open(path, encoding='utf-8').read().strip().split('\n')
-    pairs = []
-    for line in lines:
-        parts = line.split('\t')
-        if len(parts) >= 2:
-            pairs.append([preprocess_sentence(parts[0]), preprocess_sentence(parts[1])])
-    return pairs
+print("Number of samples:", len(input_texts))
+print("Number of unique input tokens:", num_encoder_tokens)
+print("Number of unique output tokens:", num_decoder_tokens)
+print("Max sequence length for inputs:", max_encoder_seq_length)
+print("Max sequence length for outputs:", max_decoder_seq_length)
 
-data_path = 'fra.txt'
-pairs = read_langs('eng', 'fra', data_path)
+input_token_index = dict([(char, i) for i, char in enumerate(input_characters)])
+target_token_index = dict([(char, i) for i, char in enumerate(target_characters)])
 
-# Vocabulary class
-class Vocabulary:
-    def __init__(self):
-        self.word2index = {}
-        self.index2word = {}
-        self.word2count = {}
-        self.n_words = 0
-        self.add_word('<unk>')
-        self.add_word('<pad>')
+encoder_input_data = np.zeros(
+    (len(input_texts), max_encoder_seq_length, num_encoder_tokens),
+    dtype="float32",
+)
+decoder_input_data = np.zeros(
+    (len(input_texts), max_decoder_seq_length, num_decoder_tokens),
+    dtype="float32",
+)
+decoder_target_data = np.zeros(
+    (len(input_texts), max_decoder_seq_length, num_decoder_tokens),
+    dtype="float32",
+)
 
-    def add_sentence(self, sentence):
-        for word in sentence.split(' '):
-            self.add_word(word)
+for i, (input_text, target_text) in enumerate(zip(input_texts, target_texts)):
+    for t, char in enumerate(input_text):
+        encoder_input_data[i, t, input_token_index[char]] = 1.0
+    encoder_input_data[i, t + 1 :, input_token_index[" "]] = 1.0
+    for t, char in enumerate(target_text):
+        decoder_input_data[i, t, target_token_index[char]] = 1.0
+        if t > 0:
+            decoder_target_data[i, t - 1, target_token_index[char]] = 1.0
+    decoder_input_data[i, t + 1 :, target_token_index[" "]] = 1.0
+    decoder_target_data[i, t:, target_token_index[" "]] = 1.0
 
-    def add_word(self, word):
-        if word not in self.word2index:
-            self.word2index[word] = self.n_words
-            self.index2word[self.n_words] = word
-            self.word2count[word] = 1
-            self.n_words += 1
-        else:
-            self.word2count[word] += 1
+# Creating model
+encoder_inputs = keras.Input(shape=(None, num_encoder_tokens))
+encoder = keras.layers.LSTM(latent_dim, return_state=True)
+encoder_outputs, state_h, state_c = encoder(encoder_inputs)
+encoder_states = [state_h, state_c]
 
-    def lookup(self, word):
-        return self.word2index.get(word, self.word2index['<unk>'])
+decoder_inputs = keras.Input(shape=(None, num_decoder_tokens))
+decoder_lstm = keras.layers.LSTM(latent_dim, return_sequences=True, return_state=True)
+decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states)
+decoder_dense = keras.layers.Dense(num_decoder_tokens, activation="softmax")
+decoder_outputs = decoder_dense(decoder_outputs)
 
-eng_vocab = Vocabulary()
-fra_vocab = Vocabulary()
+model = keras.Model([encoder_inputs, decoder_inputs], decoder_outputs)
 
-for pair in pairs:
-    eng_vocab.add_sentence(pair[0])
-    fra_vocab.add_sentence(pair[1])
+# Creating the training model
+model.compile(
+    optimizer="rmsprop", loss="categorical_crossentropy", metrics=["accuracy"]
+)
+model.fit(
+    [encoder_input_data, decoder_input_data],
+    decoder_target_data,
+    batch_size=batch_size,
+    epochs=epochs,
+    validation_split=0.2,
+)
+model.save("s2s_model.keras")
 
-# Seq2Seq Model with Attention
-class Encoder(nn.Module):
-    def __init__(self, input_dim, emb_dim, hid_dim, n_layers, dropout):
-        super().__init__()
-        self.embedding = nn.Embedding(input_dim, emb_dim)
-        self.rnn = nn.GRU(emb_dim, hid_dim, n_layers, dropout=dropout, bidirectional=True)
-        self.fc = nn.Linear(hid_dim * 2, hid_dim)
-        self.dropout = nn.Dropout(dropout)
+# Sampling
+model = keras.models.load_model("s2s_model.keras")
 
-    def forward(self, src):
-        embedded = self.dropout(self.embedding(src))
-        outputs, hidden = self.rnn(embedded)
-        # Sum bidirectional outputs
-        hidden = torch.tanh(self.fc(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim=1)))
-        return outputs, hidden
+encoder_inputs = model.input[0]
+encoder_outputs, state_h_enc, state_c_enc = model.layers[2].output
+encoder_states = [state_h_enc, state_c_enc]
+encoder_model = keras.Model(encoder_inputs, encoder_states)
 
-class Attention(nn.Module):
-    def __init__(self, hid_dim):
-        super().__init__()
-        self.attn = nn.Linear(hid_dim * 3, hid_dim)
-        self.v = nn.Linear(hid_dim, 1, bias=False)
+decoder_inputs = model.input[1]
+decoder_state_input_h = keras.Input(shape=(latent_dim,))
+decoder_state_input_c = keras.Input(shape=(latent_dim,))
+decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
+decoder_lstm = model.layers[3]
+decoder_outputs, state_h_dec, state_c_dec = decoder_lstm(
+    decoder_inputs, initial_state=decoder_states_inputs
+)
+decoder_states = [state_h_dec, state_c_dec]
+decoder_dense = model.layers[4]
+decoder_outputs = decoder_dense(decoder_outputs)
+decoder_model = keras.Model(
+    [decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states
+)
 
-    def forward(self, hidden, encoder_outputs):
-        # hidden = [batch size, hid dim]
-        # encoder_outputs = [src len, batch size, hid dim * 2]
-        src_len = encoder_outputs.shape[0]
-        hidden = hidden.unsqueeze(1).expand(-1, src_len, -1)
-        encoder_outputs = encoder_outputs.permute(1, 0, 2)
-        # hidden = [batch size, src len, hid dim]
-        # encoder_outputs = [batch size, src len, hid dim * 2]
-        energy = torch.tanh(self.attn(torch.cat((hidden, encoder_outputs), dim=2)))
-        # energy = [batch size, src len, hid dim]
-        attention = self.v(energy).squeeze(2)
-        # attention = [batch size, src len]
-        return torch.softmax(attention, dim=1)
+reverse_input_char_index = dict((i, char) for char, i in input_token_index.items())
+reverse_target_char_index = dict((i, char) for char, i in target_token_index.items())
 
-class Decoder(nn.Module):
-    def __init__(self, output_dim, emb_dim, hid_dim, n_layers, dropout, attention):
-        super().__init__()
-        self.output_dim = output_dim
-        self.attention = attention
-        self.embedding = nn.Embedding(output_dim, emb_dim)
-        self.rnn = nn.GRU(hid_dim * 2 + emb_dim, hid_dim, n_layers, dropout=dropout)
-        self.fc_out = nn.Linear(hid_dim * 3 + emb_dim, output_dim)
-        self.dropout = nn.Dropout(dropout)
+def decode_sequences(input_seqs):
+    states_values = encoder_model.predict(input_seqs, verbose=0)
+    target_seqs = np.zeros((len(input_seqs), 1, num_decoder_tokens))
+    target_seqs[:, 0, target_token_index["\t"]] = 1.0
+    decoded_sentences = [""] * len(input_seqs)
+    stop_conditions = np.zeros(len(input_seqs), dtype=bool)
 
-    def forward(self, input, hidden, encoder_outputs):
-        input = input.unsqueeze(0)
-        embedded = self.dropout(self.embedding(input))
-        a = self.attention(hidden[-1], encoder_outputs).unsqueeze(1)
-        encoder_outputs = encoder_outputs.permute(1, 0, 2)
-        weighted = torch.bmm(a, encoder_outputs)
-        rnn_input = torch.cat((embedded, weighted.permute(1, 0, 2)), dim=2)
-        output, hidden = self.rnn(rnn_input, hidden)
-        embedded = embedded.squeeze(0)
-        output = output.squeeze(0)
-        weighted = weighted.squeeze(1)
-        prediction = self.fc_out(torch.cat((output, weighted, embedded), dim=1))
-        return prediction, hidden
+    while not np.all(stop_conditions):
+        output_tokens, h, c = decoder_model.predict(
+            [target_seqs] + states_values, verbose=0
+        )
 
-class Seq2Seq(nn.Module):
-    def __init__(self, encoder, decoder, device):
-        super().__init__()
-        self.encoder = encoder
-        self.decoder = decoder
-        self.device = device
+        sampled_token_indices = np.argmax(output_tokens[:, -1, :], axis=1)
+        sampled_chars = [
+            reverse_target_char_index[idx] for idx in sampled_token_indices
+        ]
 
-    def forward(self, src, trg, teacher_forcing_ratio=0.5):
-        trg_len = trg.shape[0]
-        batch_size = trg.shape[1]
-        trg_vocab_size = self.decoder.output_dim
-        outputs = torch.zeros(trg_len, batch_size, trg_vocab_size).to(self.device)
-        encoder_outputs, hidden = self.encoder(src)
-        # Initialize hidden state of the decoder with the hidden state of the encoder
-        hidden = hidden.unsqueeze(0).repeat(self.decoder.rnn.num_layers, 1, 1)
-        input = trg[0, :]
-        for t in range(1, trg_len):
-            output, hidden = self.decoder(input, hidden, encoder_outputs)
-            outputs[t] = output
-            top1 = output.argmax(1)
-            input = trg[t] if random.random() < teacher_forcing_ratio else top1
-        return outputs
+        for i, char in enumerate(sampled_chars):
+            decoded_sentences[i] += char
+            if char == "\n" or len(decoded_sentences[i]) > max_decoder_seq_length:
+                stop_conditions[i] = True
 
-# Training and evaluation functions
-def train(model, iterator, optimizer, criterion, clip, print_every=100, max_batches=1000):
-    model.train()
-    epoch_loss = 0
-    i = 0  # Initialize batch counter
-    for src, trg in iterator:
-        if i >= max_batches:  # Limit the number of batches processed in each epoch
-            break
-        src = src.to(device)
-        trg = trg.to(device)
-        optimizer.zero_grad()
-        output = model(src, trg)
-        output_dim = output.shape[-1]
-        output = output[1:].reshape(-1, output_dim)
-        trg = trg[1:].reshape(-1)
-        loss = criterion(output, trg)
-        loss.backward()
-        torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
-        optimizer.step()
-        epoch_loss += loss.item()
-        
-        if (i + 1) % print_every == 0:
-            print(f'Batch {i+1}, Loss: {loss.item():.4f}')
-        
-        i += 1  # Increment batch counter
+        target_seqs = np.zeros((len(input_seqs), 1, num_decoder_tokens))
+        for i, token_index in enumerate(sampled_token_indices):
+            target_seqs[i, 0, token_index] = 1.0
 
-    return epoch_loss / (i if i > 0 else 1)  # Avoid division by zero
+        states_values = [h, c]
 
-def evaluate(model, iterator, criterion):
-    model.eval()
-    epoch_loss = 0
-    i = 0  # Initialize batch counter
-    with torch.no_grad():
-        for src, trg in iterator:
-            src = src.to(device)
-            trg = trg.to(device)
-            output = model(src, trg, 0)
-            output_dim = output.shape[-1]
-            output = output[1:].reshape(-1, output_dim)
-            trg = trg[1:].reshape(-1)
-            loss = criterion(output, trg)
-            epoch_loss += loss.item()
-            i += 1  # Increment batch counter
-    return epoch_loss / (i if i > 0 else 1)  # Avoid division by zero
+    return decoded_sentences
 
-# BLEU Score calculation
-def calculate_bleu(data, model, src_vocab, trg_vocab):
-    trgs = []
-    pred_trgs = []
-    for (src, trg) in data:
-        src_tensor = torch.tensor([src_vocab.lookup(word) for word in src.split(' ')]).unsqueeze(1).to(device)
-        trg_tensor = torch.tensor([trg_vocab.lookup(word) for word in trg.split(' ')]).unsqueeze(1).to(device)
-        with torch.no_grad():
-            output = model(src_tensor, trg_tensor, 0)
-        output_dim = output.shape[-1]
-        output = output[1:].reshape(-1, output_dim)
-        output = output.argmax(1)
-        pred_trg = [trg_vocab.index2word[idx.item()] for idx in output if idx.item() != trg_vocab.word2index['<pad>']]
-        pred_trgs.append(pred_trg)
-        trgs.append([trg.split(' ')])
-    return bleu_score(pred_trgs, trgs)
+# BLEU score evaluation
+def calculate_bleu_score(input_texts, target_texts, num_samples=500):
+    input_seqs = np.zeros(
+        (num_samples, max_encoder_seq_length, num_encoder_tokens), dtype="float32"
+    )
+    for i, input_text in enumerate(input_texts[:num_samples]):
+        for t, char in enumerate(input_text):
+            input_seqs[i, t, input_token_index[char]] = 1.0
+        input_seqs[i, t + 1 :, input_token_index[" "]] = 1.0
 
-# Main script
-INPUT_DIM = eng_vocab.n_words
-OUTPUT_DIM = fra_vocab.n_words
-ENC_EMB_DIM = 256
-DEC_EMB_DIM = 256
-HID_DIM = 512
-N_LAYERS = 2
-ENC_DROPOUT = 0.5
-DEC_DROPOUT = 0.5
-BATCH_SIZE = 32
-N_EPOCHS = 7
-CLIP = 1
+    decoded_sentences = decode_sequences(input_seqs)
 
-attn = Attention(HID_DIM)
-enc = Encoder(INPUT_DIM, ENC_EMB_DIM, HID_DIM, N_LAYERS, ENC_DROPOUT)
-dec = Decoder(OUTPUT_DIM, DEC_EMB_DIM, HID_DIM, N_LAYERS, DEC_DROPOUT, attn)
+    references = [[list(text.strip())] for text in target_texts[:num_samples]]
+    hypotheses = [list(text.strip()) for text in decoded_sentences]
+    bleu = corpus_bleu(references, hypotheses)
+    print("BLEU Score:", bleu)
 
-model = Seq2Seq(enc, dec, device).to(device)
-optimizer = optim.Adam(model.parameters())
-criterion = nn.CrossEntropyLoss(ignore_index=fra_vocab.word2index['<pad>'])
+    print("\nExample Translations:")
+    for i in range(10):
+        print("Input:", input_texts[i])
+        print("Target:", target_texts[i])
+        print("Translation:", decoded_sentences[i])
+        print()
 
-# Splitting data into train and test sets
-train_data = pairs[:int(0.8*len(pairs))]
-test_data = pairs[int(0.8*len(pairs)):]
-
-# Custom DataLoader with padding
-def pad_sequence(seq, max_len, pad_value):
-    seq += [pad_value] * (max_len - len(seq))
-    return seq
-
-def data_generator(data, src_vocab, trg_vocab, batch_size):
-    for i in range(0, len(data), batch_size):
-        src_batch = [d[0] for d in data[i:i+batch_size]]
-        trg_batch = [d[1] for d in data[i:i+batch_size]]
-        max_src_len = max(len(s.split(' ')) for s in src_batch)
-        max_trg_len = max(len(s.split(' ')) for s in trg_batch)
-        src_tensor = torch.tensor([pad_sequence([src_vocab.lookup(word) for word in sentence.split(' ')], max_len=max_src_len, pad_value=src_vocab.word2index['<pad>']) for sentence in src_batch], dtype=torch.long).T
-        trg_tensor = torch.tensor([pad_sequence([trg_vocab.lookup(word) for word in sentence.split(' ')], max_len=max_trg_len, pad_value=trg_vocab.word2index['<pad>']) for sentence in trg_batch], dtype=torch.long).T
-        yield src_tensor, trg_tensor
-
-for epoch in range(N_EPOCHS):
-    print(f'Epoch {epoch+1}/{N_EPOCHS}')
-    train_iterator = data_generator(train_data, eng_vocab, fra_vocab, BATCH_SIZE)
-    valid_iterator = data_generator(test_data, eng_vocab, fra_vocab, BATCH_SIZE)
-    train_loss = train(model, train_iterator, optimizer, criterion, CLIP)
-    valid_loss = evaluate(model, valid_iterator, criterion)
-    print(f'Epoch {epoch+1:02}, Train Loss: {train_loss:.3f}, Val. Loss: {valid_loss:.3f}')
-
-bleu = calculate_bleu(test_data, model, eng_vocab, fra_vocab)
-print(f'BLEU score = {bleu:.2f}')
+calculate_bleu_score(input_texts, target_texts)