Update app.py

app.py

@@ -79,6 +79,8 @@ def generate_random_name(interpreter, vocab_size, sp, max_length=10, temperature
 
     decoded_name = decoded_name.replace("▁", " ")
     decoded_name = decoded_name.replace("</s>", "")
+    decoded_name = decoded_name.replace("<unk>", "")
+    decoded_name = decoded_name.replace("<s>", "")
     generated_name = decoded_name.rsplit(' ', 1)[0]
     generated_name = generated_name[0].upper() + generated_name[1:]
 
@@ -136,7 +138,7 @@ def generateNames(type, amount, max_length=30, temperature=0.5, seed_text=""):
 
 demo = gr.Interface(
     fn=generateNames,
-    inputs=[gr.Radio(choices=["Terraria", "Skyrim"], label="Choose a model for your request"), gr.Slider(1,25, step=1, label='Amount of Names', info='How many names to generate, must be greater than 0'), gr.Slider(10, 60, value=30, step=1, label='Max Length', info='Max length of the generated word'), gr.Slider(0.1, 1, value=0.5, label='Temperature', info='Controls randomness of generation, higher values = more creative, lower values = more probalistic'), gr.Textbox('', label='Seed text (optional)', info='The starting text to begin with', max_lines=1, )],
+    inputs=[gr.Radio(choices=["Terraria", "Skyrim"], label="Choose a model for your request", value="Terraria"), gr.Slider(1,25, step=1, label='Amount of Names', info='How many names to generate, must be greater than 0'), gr.Slider(10, 60, value=30, step=1, label='Max Length', info='Max length of the generated word'), gr.Slider(0.1, 1, value=0.5, label='Temperature', info='Controls randomness of generation, higher values = more creative, lower values = more probalistic'), gr.Textbox('', label='Seed text (optional)', info='The starting text to begin with', max_lines=1, )],
     outputs=[gr.Dataframe(row_count = (2, "dynamic"), col_count=(1, "fixed"), label="Generated Names", headers=["Names"])],
     title='Dungen - Name Generator',
     description='A fun game-inspired name generator. For an example of how to create, and train your model, similar to this one, head over to: https://github.com/infinitode/open-arc/tree/main/project-5-twng/. There you will find our base model, the dataset we used, and implementation code in the form of a Jupyter Notebook (exported from Kaggle).'