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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
model = from_pretrained_keras("vrclc/transliteration")
# 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)
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
'ാ', 'ി', 'ീ', 'ു', 'ൂ', 'ൃ', 'ൄ', 'െ', 'േ', 'ൈ', 'ൊ', 'ോ', 'ൌ', 'ൗ', ' '
]
# Create tokenizer for Malayalam tokens
target_tokenizer = Tokenizer(char_level=True, filters='')
target_tokenizer.fit_on_texts(malayalam_tokens)
# 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.
"""
# 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:
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)
predicted_indices = np.argmax(predicted_sequence, axis=-1)[0]
transliterated_word = ''.join([target_tokenizer.index_word[idx] for idx in predicted_indices if idx != 0])
transliterated_text += transliterated_word
elif non_token:
transliterated_text += non_token
return transliterated_text
def transliterate(input_text):
return transliterate_with_split_tokens(input_text, model, source_tokenizer, target_tokenizer, max_seq_length)
# 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 Manglish 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,
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 Manglish (Romanised Malayalam) text to Malayalam characters. Simply type or paste your Manglish text, and see the Malayalam transliteration instantly!",
article="## How to Use\n1. Enter Manglish text in the input box\n2. The transliteration will appear automatically\n3. Works with words, phrases, and sentences",
examples=[
["ente veed"],
["malayalam padikkano? 😃"],
["india ente rajyamanu"]
],
cache_examples=False,
theme="huggingface"
)
return interface
# Launch the Gradio interface
if __name__ == "__main__":
iface = create_transliteration_interface()
iface.launch() |