Update app.py

app.py

@@ -2,9 +2,6 @@ import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import gradio as gr
 
-# Check if GPU is available, otherwise use CPU
-# device = "cuda" if torch.cuda.is_available() else "cpu"
-
 # Load pre-trained GPT-2 model and tokenizer
 model_name = "gpt2-large"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -13,14 +10,12 @@ 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.
     """
@@ -36,13 +31,11 @@ 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.
     """
@@ -54,17 +47,25 @@ def generate_text_with_nucleus_search(input_text, max_length=16, do_sample=True,
     generated_text = tokenizer.decode(output[0])
     return generated_text
 
-# Create Gradio interfaces
+# Create Gradio input interface
 input_text_interface = gr.Textbox(lines=5, label="Input Text", placeholder="Enter text for generation...")
 
-output_text_interface = gr.Textbox(label="Generated Text", placeholder="Generated text will appear here...")
+# Create Gradio output interfaces
+output_text_interface1 = gr.Textbox(label="Generated Text (Regular)", placeholder="Generated text will appear here...")
+output_text_interface2 = gr.Textbox(label="Generated Text (Nucleus Sampling)", placeholder="Generated text will appear here...")
+
+# Interface for regular text generation
+interface1 = gr.Interface(generate_text, input_text_interface, output_text_interface1, 
+                          title="Text Generation with GPT-2",
+                          description="Generate text using the GPT-2 model with regular generation method.",
+                          allow_flagging="never")
 
-gr.Interface(generate_text, input_text_interface, output_text_interface, 
-             title="Text Generation with GPT-2",
-             description="Generate text using the GPT-2 model.",
-             allow_flagging="never").launch(share=True)
+# Interface for text generation with nucleus sampling
+interface2 = gr.Interface(generate_text_with_nucleus_search, input_text_interface, output_text_interface2, 
+                          title="Text Generation with Nucleus Sampling",
+                          description="Generate text using nucleus sampling with the GPT-2 model.",
+                          allow_flagging="never")
 
-gr.Interface(generate_text_with_nucleus_search, input_text_interface, output_text_interface, 
-             title="Text Generation with Nucleus Sampling",
-             description="Generate text using nucleus sampling with the GPT-2 model.",
-             allow_flagging="never").launch(share=True)
+# Launch both interfaces
+interface1.launch(share=True)
+interface2.launch(share=True)