Create app.py

app.py

@@ -0,0 +1,42 @@
+import streamlit as st
+from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+# Load the models and tokenizers
+model_translation = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-one-to-many-mmt")
+model_masked_lm = AutoModelForMaskedLM.from_pretrained("alabnii/jmedroberta-base-sentencepiece")
+model_translation.eval()
+
+tokenizer_translation = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-one-to-many-mmt", src_lang="en_XX")
+tokenizer_masked_lm = AutoTokenizer.from_pretrained("alabnii/jmedroberta-base-sentencepiece")
+
+text = st.text_area('Enter the text:')
+
+if text:
+    model_inputs = tokenizer_translation(text, return_tensors="pt")
+    lg = st.text_input("Select Language: 1.Hindi, 2.Telugu, 3.Gujarati, 4.Bengali")
+    if lg==1:
+        generated_tokens = model_translation.generate(
+        **model_inputs,
+        forced_bos_token_id=tokenizer_translation.lang_code_to_id["hi_IN"]
+        )
+    else if lg==2:
+        generated_tokens = model_translation.generate(
+        **model_inputs,
+        forced_bos_token_id=tokenizer_translation.lang_code_to_id["te_IN"]
+        )
+    else if lg==3:
+        generated_tokens = model_translation.generate(
+        **model_inputs,
+        forced_bos_token_id=tokenizer_translation.lang_code_to_id["gu_IN"]
+        )
+    else if lg==4:
+        generated_tokens = model_translation.generate(
+        **model_inputs,
+        forced_bos_token_id=tokenizer_translation.lang_code_to_id["bn_IN"]
+        )
+    else:
+        return 0
+        
+    translation = tokenizer_translation.batch_decode(generated_tokens, skip_special_tokens=True)
+    st.json(translation)