app.py

import gradio as gr
import json
from langdetect import detect
from transformers import pipeline
import warnings
warnings.filterwarnings("ignore")

# Load language to model mapping
def load_language_model_map():
    """Load mapping between languages and translation models"""
    return {
        'ar': 'Helsinki-NLP/opus-mt-ar-en',  # Arabic to English
        'fr': 'Helsinki-NLP/opus-mt-fr-en',  # French to English
        'de': 'Helsinki-NLP/opus-mt-de-en',  # German to English
        'es': 'Helsinki-NLP/opus-mt-es-en',  # Spanish to English
        'it': 'Helsinki-NLP/opus-mt-it-en',  # Italian to English
    }

# Language code to full name mapping
LANGUAGE_NAMES = {
    'en': 'English',
    'ar': 'Arabic',
    'fr': 'French', 
    'de': 'German',
    'es': 'Spanish',
    'it': 'Italian',
}

# Initialize translation pipelines
def get_translation_pipelines():
    """Initialize translation pipelines for different target languages from JSON"""
    try:
        with open('lang_model_map.json', 'r', encoding='utf-8') as f:
            data = json.load(f)
            # Extract output language mappings
            output_langs = data['language_to_model_mapping']['output_languages']
            pipelines = {}
            for lang_name, lang_info in output_langs.items():
                # Only load the main target languages to avoid memory issues
                if lang_name in ['Hebrew', 'Arabic', 'Spanish', 'French']:
                    pipelines[lang_name] = pipeline("translation", model=lang_info['model'])
            return pipelines
    except FileNotFoundError:
        # Fallback to hardcoded pipelines if JSON file not found
        print("Warning: lang_model_map.json not found. Using fallback pipelines.")
        return {
            'Hebrew': pipeline("translation", model="Helsinki-NLP/opus-mt-en-he"),
            'Arabic': pipeline("translation", model="Helsinki-NLP/opus-mt-en-ar"), 
            'Spanish': pipeline("translation", model="Helsinki-NLP/opus-mt-en-es"),
            'French': pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
        }

# Global variables for caching pipelines
language_model_map = load_language_model_map()
target_pipelines = get_translation_pipelines()

def detect_language(text):
    """Detect the language of input text"""
    try:
        detected_lang = detect(text)
        return detected_lang, LANGUAGE_NAMES.get(detected_lang, detected_lang)
    except:
        return 'unknown', 'Unknown'

def translate_to_english(text, source_lang):
    """Translate text from source language to English"""
    if source_lang == 'en':
        return text
    
    if source_lang in language_model_map:
        try:
            model_name = language_model_map[source_lang]
            translator = pipeline("translation", model=model_name)
            result = translator(text, max_length=512)
            return result[0]['translation_text']
        except Exception as e:
            return f"Translation error: {str(e)}"
    else:
        return "Translation model not available for this language"

def translate_from_english(text, target_languages):
    """Translate English text to target languages"""
    translations = {}
    
    for lang_name in target_languages:
        if lang_name in target_pipelines:
            try:
                result = target_pipelines[lang_name](text, max_length=512)
                translations[lang_name] = result[0]['translation_text']
            except Exception as e:
                translations[lang_name] = f"Error: {str(e)}"
        else:
            translations[lang_name] = "Model not available"
    
    return translations

def smart_translate(input_text, target_lang1, target_lang2, target_lang3):
    """Main translation function"""
    if not input_text.strip():
        return "Please enter text to translate", "", "", "", "", ""
    
    # Detect source language
    source_lang_code, source_lang_name = detect_language(input_text)
    
    # Translate to English first if not already English
    english_text = translate_to_english(input_text, source_lang_code)
    
    # Get target languages list
    target_languages = []
    if target_lang1: target_languages.append(target_lang1)
    if target_lang2: target_languages.append(target_lang2) 
    if target_lang3: target_languages.append(target_lang3)
    
    # Translate to target languages
    translations = translate_from_english(english_text, target_languages)
    
    # Format results
    result_text = f"**Original Text:** {input_text}\n\n"
    result_text += f"**Detected Language:** {source_lang_name} ({source_lang_code})\n\n"
    
    if source_lang_code != 'en':
        result_text += f"**English Translation:** {english_text}\n\n"
    
    result_text += "**Translations:**\n"
    for lang, translation in translations.items():
        result_text += f"• **{lang}:** {translation}\n"
    
    # Return individual translations for display
    trans1 = translations.get(target_lang1, "") if target_lang1 else ""
    trans2 = translations.get(target_lang2, "") if target_lang2 else ""  
    trans3 = translations.get(target_lang3, "") if target_lang3 else ""
    
    return result_text, source_lang_name, english_text, trans1, trans2, trans3

# Create and launch the Gradio interface
target_options = list(target_pipelines.keys())

with gr.Blocks(title="Smart Multilingual Translator", theme=gr.themes.Soft()) as interface:
    gr.Markdown("""

    # Smart Multilingual Translator

    ### Powered by Hugging Face Transformers

    

    This application automatically detects the language of your input text and translates it to your selected target languages.

    """)
    
    with gr.Row():
        with gr.Column(scale=2):
            input_text = gr.Textbox(
                label="Input Text",
                placeholder="Enter text in any language...",
                lines=5
            )
            
            with gr.Row():
                target_lang1 = gr.Dropdown(
                    choices=target_options,
                    label="Target Language 1",
                    value="Hebrew"
                )
                target_lang2 = gr.Dropdown(
                    choices=target_options,
                    label="Target Language 2", 
                    value="Arabic"
                )
                target_lang3 = gr.Dropdown(
                    choices=target_options,
                    label="Target Language 3",
                    value="Spanish"
                )
            
            translate_btn = gr.Button("🔄 Translate", variant="primary", size="lg")
        
        with gr.Column(scale=3):
            result_display = gr.Markdown(label="Translation Results")
    
    with gr.Row():
        with gr.Column():
            detected_lang = gr.Textbox(label="Detected Language", interactive=False)
        with gr.Column():
            english_trans = gr.Textbox(label="English Translation", interactive=False)
    
    with gr.Row():
        trans1_output = gr.Textbox(label="Translation 1", interactive=False)
        trans2_output = gr.Textbox(label="Translation 2", interactive=False)
        trans3_output = gr.Textbox(label="Translation 3", interactive=False)
    
    # Event handlers
    translate_btn.click(
        fn=smart_translate,
        inputs=[input_text, target_lang1, target_lang2, target_lang3],
        outputs=[result_display, detected_lang, english_trans, trans1_output, trans2_output, trans3_output]
    )
    
    gr.Markdown("""

    ---

    ## Supported Languages

    ### Language Detection (Input)

    Arabic (ar) - العربية

    English (en) - English

    French (fr) - Français

    German (de) - Deutsch

    Italian (it) - Italiano

    Spanish (es) - Español



    ### Target Languages (Output)

    Hebrew (he) - עברית

    Arabic (ar) - العربية

    Spanish (es) - Español

    French (fr) - Français

    

    ### Models Used:

    - **Language Detection:** langdetect

    - **Translation Models:** Helsinki-NLP MarianMT models from Hugging Face

    - **Configuration:** Models loaded from lang_model_map.json

    """)

interface.launch(share=True)