Create app.py

app.py

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+import torch
+import transformers
+import gradio as gr
+
+from transformers import AutoModelForCausalLM, GPT2Tokenizer
+
+model_name = "DarwinAnim8or/GPT-NoSleep-v2"  
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Handle padding
+if tokenizer.pad_token_id is None:
+    tokenizer.pad_token_id = tokenizer.eos_token_id
+
+def generate_story(prompt, max_length=200):
+    """Generates a story continuation from a given prompt."""
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+
+    # Set generation parameters (adjust for creativity)
+    output = model.generate(
+        input_ids,
+        max_length=max_length,
+        num_return_sequences=1,  # Generate a single story
+        no_repeat_ngram_size=2,  # Prevent repetitive phrases
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        temperature=0.8,      # Control randomness (higher = more creative)
+    )
+
+    # Decode the generated story
+    story = tokenizer.decode(output[0], skip_special_tokens=True)
+    return story
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("## 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")
+    generate_button = gr.Button("Generate Story")
+
+    # Event handling
+    generate_button.click(
+        fn=generate_story, 
+        inputs=[prompt_input, max_length_slider], 
+        outputs=story_output
+    )
+
+# Launch the demo (customize the sharing options if desired)
+demo.launch()