Create app.py

app.py

@@ -0,0 +1,52 @@
+import streamlit as st
+from fairseq.models.transformer import TransformerModel
+import time
+
+@st.cache_resource
+def load_model():
+    # Load English-to-Indic model
+    en2indic = TransformerModel.from_pretrained(
+        'ai4bharat/indictrans2-en-indic-1B',
+        checkpoint_file='model.pt',
+        bpe='sentencepiece',
+        sentencepiece_model='ai4bharat/indictrans2-en-indic-1B/en-indic.model',
+        device='cpu'
+    )
+    
+    # Load Indic-to-English model
+    indic2en = TransformerModel.from_pretrained(
+        'ai4bharat/indictrans2-indic-en-1B',
+        checkpoint_file='model.pt',
+        bpe='sentencepiece',
+        sentencepiece_model='ai4bharat/indictrans2-indic-en-1B/indic-en.model',
+        device='cpu'
+    )
+    
+    return en2indic, indic2en
+
+en2indic, indic2en = load_model()
+
+st.title("IndicTrans2 Translator")
+src_lang = st.selectbox("Source Language", ["English", "Hindi", "Tamil", "Telugu", "Malayalam"])
+tgt_lang = st.selectbox("Target Language", ["English", "Hindi", "Tamil", "Telugu", "Malayalam"])
+input_text = st.text_area("Enter text to translate")
+
+if st.button("Translate"):
+    start_time = time.time()
+    
+    # Language code mapping
+    lang_codes = {
+        "English": "eng_Latn",
+        "Hindi": "hin_Deva",
+        "Tamil": "tam_Taml",
+        "Telugu": "tel_Telu",
+        "Malayalam": "mal_Mlym"
+    }
+    
+    if src_lang == "English":
+        translation = en2indic.translate(input_text, lang_pair=(lang_codes[src_lang], lang_codes[tgt_lang]))
+    else:
+        translation = indic2en.translate(input_text, lang_pair=(lang_codes[src_lang], lang_codes[tgt_lang]))
+    
+    st.text_area("Translation", translation[0], height=200)
+    st.write(f"Translation time: {time.time() - start_time:.2f} seconds")