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}"
    try:
        model = MarianMTModel.from_pretrained(model_name)
        tokenizer = MarianTokenizer.from_pretrained(model_name)
        print(f"Loaded translation model for {source_lang} to {target_lang}")
        return model, tokenizer
    except Exception as e:
        print(f"Error loading translation model for {source_lang} to {target_lang}: {e}")
        return None, None

# Function to translate text
def translate_text(text, source_lang, target_lang):
    model, tokenizer = get_translation_model(source_lang, target_lang)
    if model is None or tokenizer is None:
        return "Translation model error."
    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, target_language="English"):
    # Summarize the text using mBART (assuming input text is in English)
    inputs = multilingual_summarization_tokenizer(text, return_tensors='pt', padding=True, truncation=True)
    summary_ids = multilingual_summarization_model.generate(
        inputs['input_ids'],
        num_beams=6,        # Increased beams for better quality
        max_length=1500,   # Increased maximum length for longer summaries
        min_length=400,    # Set a minimum length for the summary
        length_penalty=1.5,  # Adjust length penalty to control the length of the summary
        early_stopping=True
    )
    summary = multilingual_summarization_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    print(f"Generated summary in English: {summary}")
    
    target_lang_code = LANGUAGES.get(target_language, "en_XX")
    
    # Translate summary to the target language if needed
    if target_lang_code != "en_XX":
        summary = translate_text(summary, "en_XX", target_lang_code)
        print(f"Translated summary to {target_language}: {summary}")
    
    return summary

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

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

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