Update app.py

app.py

@@ -5,15 +5,20 @@ import pickle
 import json
 import keras
 from huggingface_hub import hf_hub_download
+from transformers import pipeline
+
+
+model_name = "Helsinki-NLP/opus-mt-en-hi"
+
+tokenizer_base_nmt = MarianMTModel.from_pretrained(model_name)
+model_base_nmt = AutoTokenizer.from_pretrained(model_name)
+
 
 
 # Define the model repository and tokenizer checkpoint
 model_checkpoint = "himanishprak23/neural_machine_translation"
 tokenizer_checkpoint = "Helsinki-NLP/opus-mt-en-hi"
 
-tokenizer_base_nmt = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
-model_base_nmt = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-hi")
-
 # Load the tokenizer from Helsinki-NLP and model from Hugging Face repository
 tokenizer_nmt = AutoTokenizer.from_pretrained(tokenizer_checkpoint)
 model_nmt = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
@@ -33,9 +38,9 @@ max_len_eng = 20
 max_len_hin = 22
 
 def translate_text_base_nmt(input_text):
-    tokenized_input = tokenizer_base_nmt(input_text, return_tensors='tf', max_length=128, truncation=True)
-    generated_tokens = model_base_nmt.generate(**tokenized_input, max_length=128)
-    predicted_text = tokenizer_nmt.decode(generated_tokens[0], skip_special_tokens=True)
+    batch = tokenizer_base_nmt([input_text], return_tensors="pt")
+    generated_ids = model_base_nmt.generate(**batch)
+    predicted_text = tokenizer_base_nmt.batch_decode(generated_ids, skip_special_tokens=True)[0]
     return predicted_text
 
 def translate_text_nmt(input_text):