from transformers import MBartForConditionalGeneration, MBartTokenizer, MarianMTModel, MarianTokenizer
import streamlit as st

# Load multilingual summarization model and tokenizer
multilingual_summarization_model = MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50')
multilingual_summarization_tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-50')

# Dictionary of languages and their corresponding Hugging Face model codes
LANGUAGES = {
    "English": "en_XX",
    "French": "fr_XX",
    "Spanish": "es_XX",
    "German": "de_DE",
    "Chinese": "zh_CN",
    "Russian": "ru_RU",
    "Arabic": "ar_AR",
    "Portuguese": "pt_PT",
    "Hindi": "hi_IN",
    "Italian": "it_IT",
    "Japanese": "ja_XX",
    "Korean": "ko_KR",
    "Dutch": "nl_NL",
    "Polish": "pl_PL",
    "Turkish": "tr_TR",
    "Swedish": "sv_SE",
    "Greek": "el_EL",
    "Finnish": "fi_FI",
    "Hungarian": "hu_HU",
    "Danish": "da_DK",
    "Norwegian": "no_NO",
    "Czech": "cs_CZ",
    "Romanian": "ro_RO",
    "Thai": "th_TH",
    "Hebrew": "he_IL",
    "Vietnamese": "vi_VN",
    "Indonesian": "id_ID",
    "Malay": "ms_MY",
    "Bengali": "bn_BD",
    "Ukrainian": "uk_UA",
    "Urdu": "ur_PK",
    "Swahili": "sw_KE",
    "Serbian": "sr_SR",
    "Croatian": "hr_HR",
    "Slovak": "sk_SK",
    "Lithuanian": "lt_LT",
    "Latvian": "lv_LV",
    "Estonian": "et_EE",
    "Bulgarian": "bg_BG",
    "Macedonian": "mk_MK",
    "Albanian": "sq_AL",
    "Georgian": "ka_GE",
    "Armenian": "hy_AM",
    "Kazakh": "kk_KZ",
    "Uzbek": "uz_UZ",
    "Tajik": "tg_TJ",
    "Kyrgyz": "ky_KG",
    "Turkmen": "tk_TM"
}

# Function to get the appropriate translation model and tokenizer
def get_translation_model(source_lang, target_lang):
    model_name = f"Helsinki-NLP/opus-mt-{source_lang}-{target_lang}"
    model = MarianMTModel.from_pretrained(model_name)
    tokenizer = MarianTokenizer.from_pretrained(model_name)
    return model, tokenizer

# Function to translate text
def translate_text(text, source_lang, target_lang):
    model, tokenizer = get_translation_model(source_lang, target_lang)
    inputs = tokenizer([text], return_tensors="pt", truncation=True)
    translated_ids = model.generate(inputs['input_ids'], max_length=1024)
    translated_text = tokenizer.decode(translated_ids[0], skip_special_tokens=True)
    return translated_text

# Summarization function with multi-language support
def summarize_text(text, source_language="English", target_language="English"):
    source_lang_code = LANGUAGES[source_language]
    target_lang_code = LANGUAGES[target_language]
    
    # If the input language is not English, translate to English
    if source_lang_code != "en_XX":
        text = translate_text(text, source_lang_code, "en_XX")
    
    # Summarize the text using mBART
    inputs = multilingual_summarization_tokenizer(text, return_tensors='pt', padding=True, truncation=True)
    summary_ids = multilingual_summarization_model.generate(inputs['input_ids'], num_beams=4, max_length=200, early_stopping=True)
    summary = multilingual_summarization_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    
    # Translate summary to the target language if needed
    if target_lang_code != "en_XX":
        summary = translate_text(summary, "en_XX", target_lang_code)
    
    return summary

# Streamlit interface
st.title("Multi-Language Text Summarization Tool")

text = st.text_area("Input Text")
source_language = st.selectbox("Source Language", options=list(LANGUAGES.keys()), index=list(LANGUAGES.keys()).index("English"))
target_language = st.selectbox("Target Language", options=list(LANGUAGES.keys()), index=list(LANGUAGES.keys()).index("English"))

if st.button("Summarize"):
    if text:
        summary = summarize_text(text, source_language, target_language)
        st.subheader("Summary")
        st.write(summary)
    else:
        st.warning("Please enter text to summarize.")