Update app.py

app.py

@@ -21,16 +21,6 @@ if audio_bytes:
     out_S=pipe(out_s)
     st.text_area(label="Output sentence:", value=out_S)
     
-
-pipe_p=pipeline(model="ramsrigouthamg/t5_sentence_paraphraser")
-st.title("Paraphraser")
-import sentencepiece as spm
-sp=spm.SentencePieceProcessor(model_file="D:\llm\t5_sentence_paraphraser\spiece.model")
-text_p=st.text_area('Input sentence:')
-if text_p:
-    out_p=pipe_p(text_p)
-    st.text_area(label="Output sentence:", value=out_p)
-
 from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
 st.title("Language Translator")
 model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
@@ -42,4 +32,14 @@ generated_tokens = model.generate(
     **encoded_en,
     forced_bos_token_id=tokenizer.lang_code_to_id["hi_IN"]
 )
-tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+out_t=tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+st.text_area(label="Output sentence:", value=out_t)
+
+pipe_p=pipeline(model="ramsrigouthamg/t5_sentence_paraphraser")
+st.title("Paraphraser")
+import sentencepiece as spm
+sp=spm.SentencePieceProcessor(model_file="D:\llm\t5_sentence_paraphraser\spiece.model")
+text_p=st.text_area('Input sentence:')
+if text_p:
+    out_p=pipe_p(text_p)
+    st.text_area(label="Output sentence:", value=out_p)