Update app.py

app.py

@@ -14,14 +14,12 @@ model = AutoModelForCausalLM.from_pretrained(model_name)
 def generate_text(input_text, max_length=16, num_beams=5, do_sample=False, no_repeat_ngram_size=2):
     """
     Generate text based on the given input text.
-
     Parameters:
     - input_text (str): The input text to start generation from.
     - max_length (int): Maximum length of the generated text.
     - num_beams (int): Number of beams for beam search.
     - do_sample (bool): Whether to use sampling or not.
     - no_repeat_ngram_size (int): Size of the n-gram to avoid repetition.
-
     Returns:
     - generated_text (str): The generated text.
     """
@@ -38,13 +36,11 @@ def generate_text(input_text, max_length=16, num_beams=5, do_sample=False, no_re
 def generate_text_with_nucleus_search(input_text, max_length=16, do_sample=True, top_p=0.9):
     """
     Generate text with nucleus sampling based on the given input text.
-
     Parameters:
     - input_text (str): The input text to start generation from.
     - max_length (int): Maximum length of the generated text.
     - do_sample (bool): Whether to use sampling or not.
     - top_p (float): Nucleus sampling parameter.
-
     Returns:
     - generated_text (str): The generated text.
     """
@@ -60,11 +56,19 @@ def generate_text_with_nucleus_search(input_text, max_length=16, do_sample=True,
 # Create Gradio interface
 input_text = gr.Textbox(lines=10, label="Input Text", placeholder="Enter text for text generation...")
 output_text1 = gr.Textbox(label="Generated Text")
-output_text2 = gr.Textbox(label="generate_text_with_nucleus_search")
+output_text2 = gr.Textbox(label="Generated Text with Nucleus Search")
 
+# Set examples to None or empty list if not available
+examples = [
+    ["I am happy."],
+    ["This is a good day."],
+    ["It is raining outside."],
+    None  # Example for output_text2
+]
 
-gr.Interface(generate_text, input_text, output_text1,output_text2,
+gr.Interface(generate_text, input_text, output_text1, output_text2,
              title="Text Generation with GPT-2",
              description="Generate text using the GPT-2 model.",
              theme="default",  # Change theme to default
-             allow_flagging="never").launch(share=True)
+             allow_flagging="never",
+             examples=examples).launch(share=True)