Update app.py

app.py

@@ -12,7 +12,7 @@ model = AutoModelForCausalLM.from_pretrained(model_name)
 if tokenizer.pad_token_id is None:
     tokenizer.pad_token_id = tokenizer.eos_token_id
 
-def generate_story(prompt, max_length=200):
+def generate_story(prompt, max_length=200, temp=0.3):
     """Generates a story continuation from a given prompt."""
     input_ids = tokenizer.encode(prompt, return_tensors="pt")
 
@@ -25,7 +25,7 @@ def generate_story(prompt, max_length=200):
         do_sample=True,
         top_k=50,
         top_p=0.95,
-        temperature=0.8,      # Control randomness (higher = more creative)
+        temperature=temp,      # Control randomness (higher = more creative)
     )
 
     # Decode the generated story
@@ -34,16 +34,17 @@ def generate_story(prompt, max_length=200):
 
 # Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("## Storyteller: Generate a story from a prompt!")
+    gr.Markdown("## 'NoSleep' Storyteller: Generate a story from a prompt!")
     prompt_input = gr.Textbox(label="Enter your story prompt:")
     story_output = gr.Textbox(label="Generated story:")
     max_length_slider = gr.Slider(minimum=50, maximum=500, value=200, step=10, label="Max Story Length")
+    temp_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.3, step=0.1, label="Temperature (randomness)")
     generate_button = gr.Button("Generate Story")
 
     # Event handling
     generate_button.click(
         fn=generate_story, 
-        inputs=[prompt_input, max_length_slider], 
+        inputs=[prompt_input, max_length_slider, temp_slider], 
         outputs=story_output
     )