Update app.py

app.py

@@ -2,7 +2,6 @@ from transformers import pipeline, set_seed
 from transformers import BioGptTokenizer, BioGptForCausalLM
 from multilingual_translation import translate
 from utils import lang_ids
-
 import gradio as gr
 
 model_list = [
@@ -19,18 +18,16 @@ def translate_to_english(text, base_lang):
         base_lang = lang_ids[base_lang]
         new_text = translate("facebook/m2m100_418M", text, base_lang, "en")
         return new_text
-    
 
 def biogpt(
     prompt: str,
     model_id: str,
     max_length: int = 25,
     num_return_sequences: int = 5,
-    base_lang: str = "en"
+    base_lang: str = "English"
 ):
     
-    en_prompt = translate_to_english(prompt, base_lang)
-    
+    en_prompt = translate_to_english(prompt, base_lang)[0]
     model = BioGptForCausalLM.from_pretrained(model_id)
     tokenizer = BioGptTokenizer.from_pretrained(model_id)
     generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
@@ -43,6 +40,7 @@ def biogpt(
         "4": output[3]['generated_text'],
         "5": output[4]['generated_text']
     }
+    
     output_text = f'{output_dict["1"]}\n\n{output_dict["2"]}\n\n{output_dict["3"]}\n\n{output_dict["4"]}\n\n{output_dict["5"]}'
     return en_prompt, output_text
 
@@ -64,7 +62,6 @@ examples = [
     ["COVID-19 is", "microsoft/biogpt", 25, 5, "English"],
     ["Kanser", "microsoft/biogpt", 25, 5, "Turkish"]
 ]
-
 title = " BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining"
 demo_app = gr.Interface(
     biogpt,