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import gradio as gr
import torch
from transformers import pipeline
import os
# --- App Configuration ---
TITLE = "✍️ AI Story Outliner"
DESCRIPTION = """
Enter a prompt and get 10 unique story outlines from a CPU-friendly AI model.
The app uses **Microsoft's Phi-2**, a powerful small language model, to generate creative outlines.
**How it works:**
1. Enter your story idea.
2. The AI will generate 10 different story outlines.
3. Each outline has a dramatic beginning and is concise, like a song.
"""
# --- Example Prompts for Storytelling ---
examples = [
["The old lighthouse keeper stared into the storm. He'd seen many tempests, but this one was different. This one had eyes..."],
["In a city powered by dreams, a young inventor creates a machine that can record them. His first recording reveals a nightmare that doesn't belong to him."],
["The knight adjusted his helmet, the dragon's roar echoing in the valley. He was ready for the fight, but for what the dragon said when it finally spoke."],
["She found the old leather-bound journal in her grandfather's attic. The first entry read: 'To relieve stress, I walk in the woods. But today, the woods walked with me.'"],
["The meditation app promised to help her 'delete unhelpful thoughts.' She tapped the button, and to her horror, the memory of her own name began to fade..."]
]
# --- Model Initialization ---
# This section loads the Phi-2 model, which requires authentication.
# It will automatically use the HF_TOKEN secret when deployed on Hugging Face Spaces.
generator = None
model_error = None
try:
print("Initializing model... This may take a moment.")
# Explicitly load the token from environment variables (for HF Spaces secrets).
hf_token = os.environ.get("HF_TOKEN")
if hf_token:
print("✅ HF_TOKEN secret found.")
else:
# If no token is found, raise an error to prevent the app from crashing later.
raise ValueError("Hugging Face token not found. Please set the HF_TOKEN secret in your Space settings.")
# Using 'microsoft/phi-2'. This model requires authentication and trust_remote_code=True.
generator = pipeline(
"text-generation",
model="microsoft/phi-2",
token=hf_token,
torch_dtype=torch.bfloat16, # More performant data type
device_map="auto", # Will use GPU if available, otherwise CPU
trust_remote_code=True # Required for Phi-2 model
)
print("✅ microsoft/phi-2 model loaded successfully!")
except Exception as e:
model_error = e
print(f"--- 🚨 Error loading model ---")
print(f"Error: {model_error}")
# --- App Logic ---
def generate_stories(prompt: str) -> list[str]:
"""
Generates 10 story outlines from the loaded model based on the user's prompt.
"""
# If the model failed to load, display the error in all output boxes.
if model_error:
error_message = f"**Model failed to load.**\n\nPlease check the console logs for details.\n\n**Error:**\n`{str(model_error)}`"
return [error_message] * 10
if not prompt:
# Return a list of 10 empty strings to clear the outputs
return [""] * 10
# A clear, instructive prompt format that works well with models like Phi-2.
story_prompt = f"""Instruct: Create a short story outline based on this idea: "{prompt}"
The outline should have three parts: a dramatic hook, a concise ballad, and a satisfying finale. Use emojis.
Output:
### 🎬 The Hook
"""
# Parameters for the pipeline to generate 10 diverse results.
params = {
"max_new_tokens": 250,
"num_return_sequences": 10,
"do_sample": True,
"temperature": 0.9,
"top_k": 50,
"top_p": 0.95,
"pad_token_id": generator.tokenizer.eos_token_id
}
# Generate 10 different story variations
outputs = generator(story_prompt, **params)
# Extract the generated text.
stories = []
for out in outputs:
# The model will generate the prompt plus the continuation. We extract just the new part.
full_text = out['generated_text']
# Add back the part of the prompt we want to see in the output
story_start = "### 🎬 The Hook\n"
generated_part = full_text.split(story_start)[-1]
stories.append(story_start + generated_part)
# Ensure we return exactly 10 stories, padding if necessary.
while len(stories) < 10:
stories.append("Failed to generate a story for this slot.")
return stories
# --- Gradio Interface ---
with gr.Blocks(theme=gr.themes.Soft(), css=".gradio-container {max-width: 95% !important;}") as demo:
gr.Markdown(f"<h1 style='text-align: center;'>{TITLE}</h1>")
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Column(scale=1):
input_area = gr.TextArea(
lines=5,
label="Your Story Prompt �",
placeholder="e.g., 'The last dragon on Earth lived not in a cave, but in a library...'"
)
generate_button = gr.Button("Generate 10 Outlines ✨", variant="primary")
gr.Markdown("---")
gr.Markdown("## 📖 Your 10 Story Outlines")
# Create 10 markdown components to display the stories in two columns
story_outputs = []
with gr.Row():
with gr.Column():
for i in range(5):
md = gr.Markdown(label=f"Story Outline {i + 1}")
story_outputs.append(md)
with gr.Column():
for i in range(5, 10):
md = gr.Markdown(label=f"Story Outline {i + 1}")
story_outputs.append(md)
gr.Examples(
examples=examples,
inputs=input_area,
label="Example Story Starters (Click to use)"
)
generate_button.click(
fn=generate_stories,
inputs=input_area,
outputs=story_outputs,
api_name="generate"
)
if __name__ == "__main__":
demo.launch() |