Update app.py

app.py

@@ -1,4 +1,5 @@
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import MarianMTModel, MarianTokenizer
 from elevenlabs import set_api_key
 from elevenlabs import clone, generate, play
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
@@ -11,18 +12,20 @@ import streamlit as st
 
 
 #language translation function 
-def translate(text): 
-    tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M",use_auth_token=True)
-    tokenizer = AutoTokenizer.from_pretrained(".",use_auth_token=True)
-    model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M",use_auth_token=True)
-    model = AutoModelForSeq2SeqLM.from_pretrained(".",use_auth_token=True)
-    model.config.to_json()
-    inputs = tokenizer(text, return_tensors="pt")
-    translated_tokens = model.generate(**inputs, 
-                                           forced_bos_token_id=tokenizer.lang_code_to_id["hin_Deva"], 
-                                           max_length=100)
-    result = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
-    return result 
+def translate_english_to_hindi(input_text):
+    # Load the pre-trained English to Hindi translation model and tokenizer
+    model_name = "Helsinki-NLP/opus-mt-en-hi"
+    tokenizer = MarianTokenizer.from_pretrained(model_name)
+    model = MarianMTModel.from_pretrained(model_name)
+
+    # Tokenize the input text and generate translation
+    inputs = tokenizer(input_text, return_tensors="pt", padding=True)
+    translated_ids = model.generate(inputs.input_ids)
+
+    # Decode the translated output
+    translated_text = tokenizer.decode(translated_ids[0], skip_special_tokens=True)
+
+    return translated_text
 
 
 def save_uploaded_file_as_mp3(uploaded_file, output_file_path):