summarization and translation

app.py

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+
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from langdetect import detect
+
+def load_models():
+    tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50")
+    model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50")
+    summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
+    translator = pipeline("translation", model=model, tokenizer=tokenizer)
+    return tokenizer, summarizer, translator
+
+tokenizer, summarizer, translator = load_models()
+
+import streamlit as st
+LANGUAGE_CODES = {
+    "en": "en_XX",  # English
+    "fr": "fr_XX",  # French
+    "de": "de_DE",  # German
+    "ru": "ru_RU",  # Russian
+    "hi": "hi_IN",  # Hindi
+    "mr": "mr_IN",  # Marathi
+    "ja": "ja_XX",  # Japanese
+   
+}
+
+def detect_language(text):
+    lang_code = detect(text)
+    return lang_code
+
+
+def summarize_text(text, lang_code):
+    mbart_lang_code = LANGUAGE_CODES.get(lang_code, "en_XX")  # Default to English if unsupported
+    inputs = tokenizer(
+        f"<{mbart_lang_code}>{text}", 
+        return_tensors="pt", 
+        max_length=1024, 
+        truncation=True
+    )
+    summary_ids = summarizer.model.generate(
+        inputs["input_ids"], 
+        max_length=100, 
+        min_length=30, 
+        length_penalty=2.0, 
+        num_beams=4
+    )
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+
+def translate_to_english(text, lang_code):
+    mbart_lang_code = LANGUAGE_CODES.get(lang_code, "en_XX")  # Default to English if unsupported
+    inputs = tokenizer(
+        f"<{mbart_lang_code}>{text}", 
+        return_tensors="pt", 
+        max_length=1024, 
+        truncation=True
+    )
+    translated_ids = translator.model.generate(
+        inputs["input_ids"], 
+        max_length=100, 
+        length_penalty=2.0, 
+        num_beams=4
+    )
+    translated_text = tokenizer.decode(translated_ids[0], skip_special_tokens=True)
+    return translated_text
+
+
+st.title("Multilingual Summarization and Translation App")
+st.markdown("""This app detects the language of the input text, summarizes it in the same language, and translates it into English.""")
+
+
+user_input = st.text_area("Enter text in any language:", "")
+
+if st.button("Process Text"):
+    if user_input.strip():
+        
+        lang_code = detect_language(user_input)
+        st.write(f"**Detected Language Code:** {lang_code}")
+        
+        if lang_code not in LANGUAGE_CODES:
+            st.warning(f"The detected language ({lang_code}) is not supported by the model.")
+        else:
+            try:
+               
+                summary = summarize_text(user_input, lang_code)
+                st.write(f"### Summarized Text ({lang_code}):")
+                st.write(summary)
+
+                
+                translation = translate_to_english(summary, lang_code)
+                st.write("### Translated Text (English):")
+                st.write(translation)
+
+            except Exception as e:
+                st.error(f"An error occurred during processing: {e}")
+    else:
+        st.warning("Please enter some text to process.")