Update app.py

app.py

@@ -1,14 +1,16 @@
 import gradio as gr
+
 import numpy as np
+
 from tensorflow.keras.preprocessing.text import Tokenizer
 from tensorflow.keras.preprocessing.sequence import pad_sequences
+
 import re
 from huggingface_hub import from_pretrained_keras
 
-# Load the model from Hugging Face
 model = from_pretrained_keras("vrclc/transliteration")
 
-# Define source and target tokenizers
+# Define source and target tokenizers (replace with your actual tokenizers)
 source_tokens = list('abcdefghijklmnopqrstuvwxyz ')
 source_tokenizer = Tokenizer(char_level=True, filters='')
 source_tokenizer.fit_on_texts(source_tokens)
@@ -16,15 +18,19 @@ source_tokenizer.fit_on_texts(source_tokens)
 malayalam_tokens = [
     # Independent vowels
     'അ', 'ആ', 'ഇ', 'ഈ', 'ഉ', 'ഊ', 'ഋ', 'ൠ', 'ഌ', 'ൡ', 'എ', 'ഏ', 'ഐ', 'ഒ', 'ഓ', 'ഔ',
+
     # Consonants
     'ക', 'ഖ', 'ഗ', 'ഘ', 'ങ', 'ച', 'ഛ', 'ജ', 'ഝ', 'ഞ',
     'ട', 'ഠ', 'ഡ', 'ഢ', 'ണ', 'ത', 'ഥ', 'ദ', 'ധ', 'ന',
     'പ', 'ഫ', 'ബ', 'ഭ', 'മ', 'യ', 'ര', 'ല', 'വ', 'ശ',
     'ഷ', 'സ', 'ഹ', 'ള', 'ഴ', 'റ',
+
     # Chillu letters
     'ൺ', 'ൻ', 'ർ', 'ൽ', 'ൾ',
+
     # Additional characters
     'ം', 'ഃ', '്',
+
     # Vowel modifiers / Signs
     'ാ', 'ി', 'ീ', 'ു', 'ൂ', 'ൃ', 'ൄ', 'െ', 'േ', 'ൈ', 'ൊ', 'ോ', 'ൌ', 'ൗ', ' '
 ]
@@ -33,28 +39,22 @@ malayalam_tokens = [
 target_tokenizer = Tokenizer(char_level=True, filters='')
 target_tokenizer.fit_on_texts(malayalam_tokens)
 
-# Get max sequence length from the model
+# Load your pre-trained model
 max_seq_length = model.get_layer("encoder_input").input_shape[0][1]
 
 def transliterate_with_split_tokens(input_text, model, source_tokenizer, target_tokenizer, max_seq_length):
     """
     Transliterates input text, preserving non-token characters.
     """
-    # Handle empty input
-    if not input_text:
-        return ""
-    
     # Regular expression to split the text into tokens and non-tokens
     tokens_and_non_tokens = re.findall(r"([a-zA-Z]+)|([^a-zA-Z]+)", input_text)
+
     transliterated_text = ""
-    
     for token_or_non_token in tokens_and_non_tokens:
         token = token_or_non_token[0]
         non_token = token_or_non_token[1]
-        
+
         if token:
-            # Convert to lowercase to handle mixed case
-            token = token.lower()
             input_sequence = source_tokenizer.texts_to_sequences([token])[0]
             input_sequence_padded = pad_sequences([input_sequence], maxlen=max_seq_length, padding='post')
             predicted_sequence = model.predict(input_sequence_padded)
@@ -63,54 +63,23 @@ def transliterate_with_split_tokens(input_text, model, source_tokenizer, target_
             transliterated_text += transliterated_word
         elif non_token:
             transliterated_text += non_token
-    
+
     return transliterated_text
 
-# Create Gradio interface with enhanced features
-def create_transliteration_interface():
-    # Define input and output components with more details
-    input_textbox = gr.Textbox(
-        lines=3, 
-        placeholder="Enter English text to transliterate to Malayalam...", 
-        label="Input Text"
-    )
-    
-    output_textbox = gr.Textbox(
-        lines=3, 
-        label="Transliterated Malayalam Text"
-    )
-    
-    # Create the Gradio interface with more comprehensive configuration
-    interface = gr.Interface(
-        fn=transliterate_with_split_tokens,
-        inputs=[
-            gr.Textbox(
-                lines=3, 
-                placeholder="Enter English text to transliterate to Malayalam...", 
-                label="Input Text"
-            )
-        ],
-        outputs=[
-            gr.Textbox(
-                lines=3, 
-                label="Transliterated Malayalam Text"
-            )
-        ],
-        title="🌟 English to Malayalam Transliterator",
-        description="Transliterate English text to Malayalam characters. Simply type or paste your English text, and see the Malayalam transliteration instantly!",
-        article="## How to Use\n1. Enter English text in the input box\n2. The transliteration will appear automatically\n3. Works with words, phrases, and sentences",
-        examples=[
-            ["ente veed"],
-            ["malayalam"],
-            ["hello world"],
-            ["njan pranayam"]
-        ],
-        theme="huggingface"
-    )
-    
-    return interface
-
-# Launch the Gradio interface
-if __name__ == "__main__":
-    iface = create_transliteration_interface()
-    iface.launch()
+def transliterate(input_text):
+  return transliterate_with_split_tokens(input_text, model, source_tokenizer, target_tokenizer, max_seq_length)
+
+input_text = "ente veed "
+transliterated_text = transliterate_with_split_tokens(input_text, model, source_tokenizer, target_tokenizer, max_seq_length)
+
+transliterated_text
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=transliterate,
+    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
+    outputs="text",
+    title="English to Malayalam Transliteration",
+    description="Transliterate English text to Malayalam.",
+)
+
+iface.launch(share=True)