output codes have been updated.

app.py

@@ -22,7 +22,7 @@ def translate_to_english(text, lang_model_id, base_lang):
     else:
         base_lang = lang_ids[base_lang]
         new_text = translate(lang_model_id, text, base_lang, "en")
-        return new_text
+        return new_text[0]
 
 def biogpt(
     prompt: str,
@@ -33,41 +33,47 @@ def biogpt(
     lang_model_id: str
 ):
     
-    en_prompt = translate_to_english(prompt, lang_model_id, base_lang)[0]
+    en_prompt = translate_to_english(prompt, lang_model_id, base_lang)
     model = BioGptForCausalLM.from_pretrained(biogpt_model_id)
     tokenizer = BioGptTokenizer.from_pretrained(biogpt_model_id)
     generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
     set_seed(42)
     output = generator(en_prompt, max_length=max_length, num_return_sequences=num_return_sequences, do_sample=True)
-    output_dict = {
-        "1": output[0]['generated_text'],
-        "2": output[1]['generated_text'],
-        "3": output[2]['generated_text'],
-        "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
+    output_dict = {}
+    for i in range(num_return_sequences):
+        output_dict[str(i+1)] = output[i]['generated_text']
+    
+    output_text = ""
+    for i in range(num_return_sequences):
+        output_text += f'{output_dict[str(i+1)]}\n\n'
+
+    if base_lang == "English":
+        base_lang_output = output_text
+        
+    base_lang_output = translate(lang_model_id, output_text, "en", lang_ids[base_lang])[0]
+    
+    return en_prompt, output_text, base_lang_output
 
 
 inputs = [
-    gr.inputs.Textbox(label="Prompt", lines=5, default="COVID-19 is"),
-    gr.Dropdown(biogpt_model_list, value="microsoft/biogpt", label="Model ID"),
-    gr.inputs.Slider(5, 100, 25, default=25, label="Max Length"),
-    gr.inputs.Slider(1, 10, 5, default=5, label="Num Return Sequences"),
+    gr.Textbox(lines=5, value="COVID-19 is", label="Prompt"),
+    gr.Dropdown(biogpt_model_list, value="microsoft/biogpt", label="BioGPT Model ID"),
+    gr.Slider(minumum=1, maximum=100, value=25, step=1, label="Max Length"),
+    gr.Slider(minumum=1, maximum=10, value=2, step=1, label="Number of Outputs"),
     gr.Dropdown(lang_list, value="English", label="Base Language"),
-    gr.Dropdown(lang_model_list, value="facebook/m2m100_418M", label="Model ID"),
+    gr.Dropdown(lang_model_list, value="facebook/m2m100_418M", label="Language Model ID")
     ]
 
 outputs = [
     gr.outputs.Textbox(label="Prompt"),
-    gr.outputs.Textbox(label="Output")
+    gr.outputs.Textbox(label="Output"),
+    gr.outputs.Textbox(label="Translated Output")
 ]
 
 examples = [
-    ["COVID-19 is", "microsoft/biogpt", 25, 5, "English", "facebook/m2m100_418M"],
-    ["Kanser", "microsoft/biogpt", 25, 5, "Turkish", "facebook/m2m100_1.2B"]
+    ["COVID-19 is", "microsoft/biogpt", 25, 2, "English", "facebook/m2m100_418M"],
+    ["Kanser", "microsoft/biogpt", 25, 2, "Turkish", "facebook/m2m100_1.2B"]
 ]
 
 title = " BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining"