Rename GradioKlingonToEnglish.py to app.py

GradioKlingonToEnglish.py → app.py

@@ -1,75 +1,75 @@
-import torch
-import tensorflow as tf
-import gradio as gr
-from tokenizers import Tokenizer
-from Seq2SeqModel import Seq2SeqModel 
-from KTEPreprocess import preprocess 
-from Decoder import Decoder
-from Encoder import Encoder
-
-n_layers = 2
-emb_dim = 256  
-hid_dim = 512  
-dropout = 0.5  # Reduced dropout to allow more learning (less regularization)
-device = torch.device('cpu')
-
-# Load preprocessed data and model parameters
-(klingon_tokenizer, english_tokenizer, output_dim,input_dim,
- klingon_train_padded, english_train_input, english_train_target,
- klingon_test_padded, english_test_input, english_test_target,
- max_length_klingon, max_length_english) = preprocess()
-
-# Initialize encoder and decoder for Klingon to English
-encoder = Encoder(output_dim, emb_dim, hid_dim, n_layers, dropout).to(device)
-decoder = Decoder(input_dim, emb_dim, hid_dim, n_layers, dropout).to(device)
-
-# Initialize and load the Seq2SeqModel
-model = Seq2SeqModel(encoder, decoder, device).to(device)
-model.load_state_dict(torch.load(r'C:\Users\Arvind Natarajan\Desktop\Orbital\Klingon-Heads\BPE_Klingon_to_English_1.pth', map_location=torch.device('cpu')))
-model.eval()
-
-# Preprocess the Klingon input
-def preprocess_sentence(sentence, tokenizer, max_length):
-    tokenized_sentence = tokenizer.encode(sentence).ids
-    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)
-
-# Translate Klingon to English
-def translate_klingon_to_english(klingon_sentence):
-    print(f"Original Klingon sentence: {klingon_sentence}")
-
-    input_sentence = preprocess_sentence(klingon_sentence, klingon_tokenizer, max_length_klingon)
-    print(f"Tokenized and padded input sentence: {input_sentence.tolist()}")
-
-    if input_sentence.numpy().sum() == 0:
-        print("Warning: Input sentence is empty or out of vocabulary after tokenization.")
-    input_sentence = input_sentence.squeeze(0)
-
-    with torch.no_grad():
-        output = model(input_sentence.unsqueeze(1), input_sentence.unsqueeze(1), 0)
-        print(f"Model raw output (logits): {output}")
-
-    output_indices = torch.argmax(output, dim=-1).squeeze().tolist()
-    english_sentence = english_tokenizer.decode(output_indices, skip_special_tokens=True)
-    print(f"Decoded English sentence: {english_sentence}")
-
-    return english_sentence
-
-# Gradio interface
-examples = [
-    ["nuqneH! DaHjaj SuvwI'"],  # Greetings! Today is a good day (to die)
-    ["tlhIngan Hol vIjatlh"],  # I speak Klingon
-    ["SopwI' SoH"],  # You are my dinner
-]
-
-iface = gr.Interface(
-    fn=translate_klingon_to_english,
-    inputs=gr.Textbox(label="Klingon Phrase", lines=2, placeholder="Enter Klingon text here..."),
-    outputs=gr.Textbox(label="English Translation", lines=2),
-    title="Klingon to English Translation",
-    description="Enter text in Klingon and get its translation in English. This translator helps you understand the language of the Klingon species from the Star Trek universe.",
-    examples=examples,
-    theme="default"
-)
-
-iface.launch()
+import torch
+import tensorflow as tf
+import gradio as gr
+from tokenizers import Tokenizer
+from Seq2SeqModel import Seq2SeqModel 
+from KTEPreprocess import preprocess 
+from Decoder import Decoder
+from Encoder import Encoder
+
+n_layers = 2
+emb_dim = 256  
+hid_dim = 512  
+dropout = 0.5  # Reduced dropout to allow more learning (less regularization)
+device = torch.device('cpu')
+
+# Load preprocessed data and model parameters
+(klingon_tokenizer, english_tokenizer, output_dim,input_dim,
+ klingon_train_padded, english_train_input, english_train_target,
+ klingon_test_padded, english_test_input, english_test_target,
+ max_length_klingon, max_length_english) = preprocess()
+
+# Initialize encoder and decoder for Klingon to English
+encoder = Encoder(output_dim, emb_dim, hid_dim, n_layers, dropout).to(device)
+decoder = Decoder(input_dim, emb_dim, hid_dim, n_layers, dropout).to(device)
+
+# Initialize and load the Seq2SeqModel
+model = Seq2SeqModel(encoder, decoder, device).to(device)
+model.load_state_dict(torch.load('BPE_Klingon_to_English_1.pth', map_location=torch.device('cpu')))
+model.eval()
+
+# Preprocess the Klingon input
+def preprocess_sentence(sentence, tokenizer, max_length):
+    tokenized_sentence = tokenizer.encode(sentence).ids
+    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)
+
+# Translate Klingon to English
+def translate_klingon_to_english(klingon_sentence):
+    print(f"Original Klingon sentence: {klingon_sentence}")
+
+    input_sentence = preprocess_sentence(klingon_sentence, klingon_tokenizer, max_length_klingon)
+    print(f"Tokenized and padded input sentence: {input_sentence.tolist()}")
+
+    if input_sentence.numpy().sum() == 0:
+        print("Warning: Input sentence is empty or out of vocabulary after tokenization.")
+    input_sentence = input_sentence.squeeze(0)
+
+    with torch.no_grad():
+        output = model(input_sentence.unsqueeze(1), input_sentence.unsqueeze(1), 0)
+        print(f"Model raw output (logits): {output}")
+
+    output_indices = torch.argmax(output, dim=-1).squeeze().tolist()
+    english_sentence = english_tokenizer.decode(output_indices, skip_special_tokens=True)
+    print(f"Decoded English sentence: {english_sentence}")
+
+    return english_sentence
+
+# Gradio interface
+examples = [
+    ["nuqneH! DaHjaj SuvwI'"],  # Greetings! Today is a good day (to die)
+    ["tlhIngan Hol vIjatlh"],  # I speak Klingon
+    ["SopwI' SoH"],  # You are my dinner
+]
+
+iface = gr.Interface(
+    fn=translate_klingon_to_english,
+    inputs=gr.Textbox(label="Klingon Phrase", lines=2, placeholder="Enter Klingon text here..."),
+    outputs=gr.Textbox(label="English Translation", lines=2),
+    title="Klingon to English Translation",
+    description="Enter text in Klingon and get its translation in English. This translator helps you understand the language of the Klingon species from the Star Trek universe.",
+    examples=examples,
+    theme="default"
+)
+
+iface.launch()