app.py

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 **Tencent's Hunyuan-1.8B** to generate creative outlines formatted in Markdown.

**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 a smaller, CPU-friendly model.
# 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).
    # This makes the authentication more robust, overriding any bad default credentials.
    hf_token = os.environ.get("HF_TOKEN")

    # Add a check to see if the token was loaded correctly.
    if hf_token:
        print("✅ HF_TOKEN secret found.")
    else:
        print("⚠️ HF_TOKEN secret not found. Please ensure it is set in your Hugging Face Space settings.")
        # Raise an error to stop the app from proceeding without a token.
        raise ValueError("Hugging Face token not found. Please set the HF_TOKEN secret.")

    # Using a smaller model from the user's list.
    # Passing the token explicitly to ensure correct authentication.
    generator = pipeline(
        "text-generation",
        model="tencent/Hunyuan-1.8B-Instruct",
        torch_dtype=torch.bfloat16, # Use bfloat16 for better performance if available
        device_map="auto", # Will use GPU if available, otherwise CPU
        token=hf_token
    )
    print("✅ Tencent/Hunyuan-1.8B-Instruct 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

    # This prompt format is specific to the Hunyuan model.
    system_instruction = "You are an expert storyteller. Your task is to take a user's prompt and write a short story as a Markdown outline. The story must have a dramatic arc and be the length of a song. Use emojis to highlight the story sections."
    
    story_prompt = f"<|im_start|>system\n{system_instruction}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"

    # Parameters for the pipeline to generate 10 diverse results.
    params = {
        "max_new_tokens": 250,
        "num_return_sequences": 10,
        "do_sample": True,
        "temperature": 0.8,
        "top_p": 0.95,
        "pad_token_id": generator.tokenizer.eos_token_id # Suppress warning
    }
    
    # Generate 10 different story variations
    outputs = generator(story_prompt, **params)

    # Extract the generated text and clean it up.
    stories = []
    for out in outputs:
        # Remove the prompt part from the full generated text
        full_text = out['generated_text']
        assistant_response = full_text.split("<|im_start|>assistant\n")[-1]
        stories.append(assistant_response)

    # 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()