Update app.py

app.py

@@ -29,24 +29,24 @@ def generate_text(input_text, max_length=16, num_beams=5, do_sample=False, no_re
     generated_text = tokenizer.decode(output[0])
     return generated_text
 
-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.
-    """
-    # Encode the input text and move it to the appropriate device
-    input_ids = tokenizer(input_text, return_tensors='pt')['input_ids']
-    # Generate text using nucleus sampling
-    output = model.generate(input_ids, max_length=max_length, do_sample=do_sample, top_p=top_p)
-    # Decode the generated output
-    generated_text = tokenizer.decode(output[0])
-    return generated_text
+# 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.
+#     """
+#     # Encode the input text and move it to the appropriate device
+#     input_ids = tokenizer(input_text, return_tensors='pt')['input_ids']
+#     # Generate text using nucleus sampling
+#     output = model.generate(input_ids, max_length=max_length, do_sample=do_sample, top_p=top_p)
+#     # Decode the generated output
+#     generated_text = tokenizer.decode(output[0])
+#     return generated_text
 
 # Create Gradio input interface
 input_text_interface = gr.Textbox(lines=5, label="Input Text", placeholder="Enter text for generation...")
@@ -61,14 +61,14 @@ interface1 = gr.Interface(generate_text, input_text_interface, output_text_inter
                           allow_flagging="never")
 
 # Create Gradio output interface for text generation with nucleus sampling
-output_text_interface2 = gr.Textbox(label="Generated Text (Nucleus Sampling)", placeholder="Generated text will appear here...")
+# output_text_interface2 = gr.Textbox(label="Generated Text (Nucleus Sampling)", placeholder="Generated text will appear here...")
 
-# 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")
+# # 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")
 
 # Launch both interfaces
 interface1.launch(share=True)
-interface2.launch(share=True)
+# interface2.launch(share=True)