Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+def translate_text(article, source_language, target_language):
+    # Importing the pre-trained model
+    tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
+    model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
+
+    # Mapping language to language code
+    lang_code = {
+        "hindi": "hin_Deva",
+        "telugu": "tel_Telu",
+        "english":"eng_Latn"
+        # Add more language codes as needed
+    }
+
+    # Appending source language to the input text
+    article_with_lang = f"{article} [lang:{lang_code[source_language]}]"
+
+    # Translating the sentence
+    inputs = tokenizer(article_with_lang, return_tensors="pt")
+    translated_tokens = model.generate(
+        **inputs, forced_bos_token_id=tokenizer.lang_code_to_id[lang_code[target_language]], max_length=30
+    )
+
+    # Decoding and returning the translated text
+    return tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
+
+# Streamlit UI
+st.title("Language Translator")
+
+# Input section
+st.sidebar.header("Input Options")
+input_text = st.text_area("Enter Text to Translate:")
+source_language = st.sidebar.selectbox("Select Source Language:", ["english", "hindi", "telugu"])
+
+# Output section
+st.sidebar.header("Output Options")
+target_language = st.sidebar.selectbox("Select Target Language:", ["english","hindi", "telugu"])
+
+# Translate button
+if st.button("Translate"):
+    if input_text:
+        translated_text = translate_text(input_text, source_language, target_language)
+        st.success(f"Translated ({target_language}): {translated_text}")
+    else:
+        st.warning("Please enter text to translate.")