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import gradio as gr
from transformers import MarianMTModel, MarianTokenizer
import torch
import nltk
# Download punkt for sentence tokenization
nltk.download('punkt')
nltk.download('punkt_tab')
from nltk.tokenize import sent_tokenize
# Cache for storing models and tokenizers
models_cache = {}
def load_model(model_name):
"""
Load and cache the MarianMT model and tokenizer.
"""
if model_name not in models_cache:
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
if torch.cuda.is_available():
model = model.to('cuda')
models_cache[model_name] = (model, tokenizer)
return models_cache[model_name]
def translate_text(model_name, text):
"""
Translate input text sentence by sentence using the specified model.
"""
if not model_name or not text:
return "Please select a model and provide text for translation."
try:
# Load the model and tokenizer
model, tokenizer = load_model(model_name)
# Split text into sentences
sentences = sent_tokenize(text)
translated_sentences = []
for sentence in sentences:
# Tokenize the sentence
print(f"Sentence: {sentence}\n")
tokens = tokenizer(sentence, return_tensors="pt", padding=True)
if torch.cuda.is_available():
tokens = {k: v.to('cuda') for k, v in tokens.items()}
# Generate translation for the sentence
translated = model.generate(**tokens)
translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
translated_sentences.append(translated_text)
# Join translated sentences back into a single string
return " ".join(translated_sentences)
except Exception as e:
return f"Error: {str(e)}"
# Model options (display name, model name)
model_options = [
("English to Turkish", "Helsinki-NLP/opus-mt-tc-big-en-tr"),
("Turkish to English", "Helsinki-NLP/opus-mt-tc-big-tr-en"),
("English to French", "Helsinki-NLP/opus-mt-tc-big-en-fr"),
("French to English", "Helsinki-NLP/opus-mt-tc-big-fr-en"),
("English to German", "Helsinki-NLP/opus-mt-en-de"),
("German to English", "Helsinki-NLP/opus-mt-de-en"),
("English to Spanish", "Helsinki-NLP/opus-mt-tc-big-en-es"),
("Spanish to English", "Helsinki-NLP/opus-mt-es-en"),
("English to Arabic", "Helsinki-NLP/opus-mt-tc-big-en-ar"),
("Arabic to English", "Helsinki-NLP/opus-mt-tc-big-ar-en"),
("English to Urdu", "Helsinki-NLP/opus-mt-en-ur"),
("Urdu to English", "Helsinki-NLP/opus-mt-ur-en"),
("English to Hindi", "Helsinki-NLP/opus-mt-en-hi"),
("Hindi to English", "Helsinki-NLP/opus-mt-hi-en"),
("English to Chinese", "Helsinki-NLP/opus-mt-en-zh"),
("Chinese to English", "Helsinki-NLP/opus-mt-zh-en")
]
# Create mapping for display names to model names
model_mapping = {name: model for name, model in model_options}
with gr.Blocks() as demo:
gr.Markdown("# 🌍 Real-Time Sentence Translation")
with gr.Row():
model_dropdown = gr.Dropdown(
label="Select Translation Model",
choices=[name for name, _ in model_options], # Show only names
type="value",
)
with gr.Row():
input_text = gr.Textbox(
label="Enter text (complete sentences)",
lines=5,
placeholder="Type here...",
)
with gr.Row():
translate_button = gr.Button("Translate")
clear_button = gr.Button("Clear")
output_text = gr.Textbox(label="Translated Text", interactive=False)
def clear_inputs():
return "", ""
def translate_with_mapping(selected_name, text):
model_name = model_mapping.get(selected_name, "")
return translate_text(model_name, text)
translate_button.click(
fn=translate_with_mapping,
inputs=[model_dropdown, input_text],
outputs=output_text,
)
clear_button.click(
fn=clear_inputs,
inputs=[],
outputs=[input_text, output_text],
)
# Run the Gradio app
demo.launch()
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