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 powerful AI model.
The app uses **Mistral-7B-v0.1**, a popular and capable open-source 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 Mistral-7B 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 'mistralai/Mistral-7B-v0.1'. This model is gated and requires a token.
    generator = pipeline(
        "text-generation",
        model="mistralai/Mistral-7B-v0.1",
        token=hf_token,
        torch_dtype=torch.bfloat16, # More performant data type
        device_map="auto" # Will use GPU if available, otherwise CPU
    )
    print("✅ mistralai/Mistral-7B-v0.1 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.
    """
    print("--- Button clicked. Attempting to generate stories... ---")
    
    # If the model failed to load during startup, display that error.
    if model_error:
        error_message = f"**Model failed to load during startup.**\n\nPlease check the console logs for details.\n\n**Error:**\n`{str(model_error)}`"
        print(f"Returning startup error: {error_message}")
        return [error_message] * 10
        
    if not prompt:
        # Return a list of 10 empty strings to clear the outputs
        return [""] * 10

    try:
        # This prompt format is optimized for Mistral instruct models.
        story_prompt = f"""[INST] Create a short story outline based on this idea: "{prompt}"
The outline must have three parts: a dramatic hook, a concise ballad, and a satisfying finale. Use emojis for each section header. [/INST]
### 🎬 The Hook
"""

        # Parameters for the pipeline to generate 10 diverse results.
        params = {
            "max_new_tokens": 250,
            "num_return_sequences": 10,
            "do_sample": True,
            "temperature": 0.8,
            "top_p": 0.95,
            "pad_token_id": generator.tokenizer.eos_token_id
        }
        
        print("Generating text with the model...")
        # Generate 10 different story variations
        outputs = generator(story_prompt, **params)
        print("✅ Text generation complete.")

        # 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"
            # Split by the instruction closing tag to get only the model's response
            generated_part = full_text.split("[/INST]")[-1].strip()
            stories.append(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

    except Exception as e:
        # Catch any errors that happen DURING generation and display them in the UI.
        print(f"--- 🚨 Error during story generation ---")
        print(f"Error: {e}")
        runtime_error_message = f"**An error occurred during story generation.**\n\nPlease check the console logs for details.\n\n**Error:**\n`{str(e)}`"
        return [runtime_error_message] * 10


# --- 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()