Update app.py

app.py

@@ -1,83 +1,83 @@
-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('./backend/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)
-
+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)
+