Create app.py

app.py

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+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.")