File size: 4,366 Bytes
7ae1348 4b3604b 7ae1348 4b3604b 7ae1348 cfea1fe 7ae1348 6cb18d9 e74ef23 6cb18d9 7ae1348 4b3604b 7ae1348 4b3604b cfea1fe e74ef23 4b3604b 7ae1348 cfea1fe 7ae1348 e74ef23 cfea1fe e74ef23 7ae1348 e74ef23 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
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.")
|