Update app.py

app.py

@@ -13,7 +13,7 @@ model.eval()
 model.to('cpu')
 
 # Define the function that generates text from a prompt
-def generate_text(prompt, temperature, top_p):
+def generate_text(prompt, temperature):
 
     print(prompt)
     
@@ -29,13 +29,6 @@ def generate_text(prompt, temperature, top_p):
         with torch.no_grad():
             outputs = model(input_tokens)
             predictions = outputs.logits[:, -1, :] / temperature
-            sorted_logits, sorted_indices = torch.sort(predictions, descending=True)
-            cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
-            sorted_indices_to_remove = cumulative_probs > top_p
-            sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
-            sorted_indices_to_remove[..., 0] = 0
-            indices_to_remove = sorted_indices[sorted_indices_to_remove]
-            predictions[:, indices_to_remove] = -float('Inf')
             next_token = torch.multinomial(torch.softmax(predictions, dim=-1), 1)
         
         input_tokens = torch.cat((input_tokens, next_token), dim=1)
@@ -46,13 +39,12 @@ def generate_text(prompt, temperature, top_p):
             break
         yield generated_text[prompt_length:]  # Yield the generated text excluding the initial prompt plus "EOS"
 
-# Create a Gradio interface with a text input, sliders for temperature and top_p, and a text output
+# Create a Gradio interface with a text input and a slider for temperature
 interface = gr.Interface(
     fn=generate_text,
     inputs=[
         gr.Textbox(lines=2, placeholder="Enter your prompt here..."),
         gr.Slider(minimum=0.1, maximum=1.0, value=0.1, label="Temperature"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=1.0, label="Top_P"),
     ],
     outputs=gr.Textbox(),
     live=False,