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
            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
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 Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# 🌍 Real-Time Sentence Translation")
    
    with gr.Row():
        model_dropdown = gr.Dropdown(
            label="Select Translation Model",
            choices=[option[1] for option in model_options],
            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 "", ""
    
    translate_button.click(
        fn=translate_text,
        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()