Create app.py

app.py

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+import streamlit as st
+from ai4bharat.transliteration import XlitEngine
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+from IndicTransTokenizer import IndicProcessor
+
+# Initialize the transliteration engine and model
+e = XlitEngine(["gu", 'en'], beam_width=10, src_script_type="en")
+model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/indictrans2-indic-en-1B", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("ai4bharat/indictrans2-indic-en-1B", trust_remote_code=True)
+ip = IndicProcessor(inference=True)
+
+def english_to_gujarati(text):
+    return e.translit_sentence(text)['gu']
+
+def translate_question(english_question):
+    gujarati_question = english_to_gujarati(english_question)
+    lst = [gujarati_question]
+    
+    batch = ip.preprocess_batch(lst, src_lang="guj_Gujr", tgt_lang="eng_Latn")
+    batch = tokenizer(batch, padding="longest", truncation=True, max_length=256, return_tensors="pt")
+
+    with torch.inference_mode():
+        outputs = model.generate(**batch, num_beams=5, num_return_sequences=1, max_length=256)
+
+    with tokenizer.as_target_tokenizer():
+        outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+
+    outputs = ip.postprocess_batch(outputs, lang="eng_Latn")
+    return outputs
+
+# Streamlit UI
+st.title("English to Gujarati Translation")
+st.write("Enter your question in English:")
+
+english_question = st.text_input("Question:")
+
+if st.button("Translate"):
+    if english_question:
+        translated = translate_question(english_question)
+        st.write("Transliterated and Translated question:", translated)
+    else:
+        st.write("Please enter a question.")
+