Update app.py

app.py

@@ -29,18 +29,6 @@ languages_list = [("Gujarati", "gu_IN"), ('Hindi',"hi_IN") , ("Bengali","bn_IN")
 
 
 
-def english_to_indian(sentence):
-    translation_tokenizer.src_lang = "en_xx"
-    encoded_hi = translation_tokenizer(sentence, return_tensors="pt")
-    generated_tokens = translation_model.generate(**encoded_hi, forced_bos_token_id=translation_tokenizer.lang_code_to_id["hi_IN"] )
-    return (translation_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True))
-
-
-def indian_to_english(sentence):
-    translation_tokenizer.src_lang = "hi_IN"
-    encoded_hi = translation_tokenizer(sentence, return_tensors="pt")
-    generated_tokens = translation_model.generate(**encoded_hi, forced_bos_token_id=translation_tokenizer.lang_code_to_id["en_XX"] )
-    return (translation_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True))
 
 def intitalize_lang(language):
     # translation_tokenizer.src_lang = "en_xx"
@@ -194,7 +182,7 @@ def conversation(qa_chain, message, history):
     # Generate response using QA chain
     response = qa_chain({"question": message, "chat_history": formatted_chat_history})
     response_answer = response["answer"]
-    print(english_to_hindi(response["answer"][:100]))
+    print(english_to_indian(response["answer"][:100]))
     if response_answer.find("Helpful Answer:") != -1:
         response_answer = response_answer.split("Helpful Answer:")[-1]
     response_sources = response["source_documents"]
@@ -224,6 +212,20 @@ def upload_file(file_obj):
     return list_file_path
 
 
+def english_to_indian(sentence):
+    translation_tokenizer.src_lang = "en_xx"
+    encoded_hi = translation_tokenizer(sentence, return_tensors="pt")
+    generated_tokens = translation_model.generate(**encoded_hi, forced_bos_token_id=translation_tokenizer.lang_code_to_id[lang_btn] )
+    return (translation_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True))
+
+
+def indian_to_english(sentence):
+    translation_tokenizer.src_lang = lang_btn
+    encoded_hi = translation_tokenizer(sentence, return_tensors="pt")
+    generated_tokens = translation_model.generate(**encoded_hi, forced_bos_token_id=translation_tokenizer.lang_code_to_id["en_XX"] )
+    return (translation_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True))
+
+
 def demo():
     with gr.Blocks(theme="base") as demo:
         vector_db = gr.State()
@@ -276,7 +278,7 @@ def demo():
         with gr.Row():
             lang_btn = gr.Dropdown(languages_list, label="Languages", value = languages_list[1],
                                 type="value", info="Choose your language",interactive = True).select(intitalize_lang)
-        #lang_btn.select(intitalize_lang, inputs)
+
         
         chatbot = gr.Chatbot(height=300)